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REVIEW 2 major objections 4 minor 126 references

Point of Interest Recommendation: Pitfalls and Viable Solutions

T0 review · 2 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper argues that point-of-interest recommendation research has failed its stakeholders because of 20 recurring pitfalls in datasets, algorithms, and evaluation, and that a six-part agenda—multistakeholder design, context awareness…

desk verdict A genuinely useful, well-organized reflection paper that will serve as a practical checklist for POI researchers, but the central claim that 'the majority' of solutions have failed is an opinion, not a measured finding. read the letter →

arxiv 2507.13725 v1 pith:A3RKLNQK submitted 2025-07-18 cs.IR cs.AI

Alejandro Bellogín , Linus W. Dietz , Francesco Ricci , Pablo Sánchez This is my paper
classification cs.IRcs.AI
keywords point-of-interestrecommendationtouristrecommendersystemsevaluationpitfallsmultistakeholderlocation-basedsocialnetworkscontext-awarereproducibilitytourismdata
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper is a critical review of point-of-interest (POI) recommendation in tourism. It argues that after years of research, most proposed systems still fail the people and organizations they are meant to serve, because three parts of the research pipeline—datasets, algorithms, and evaluation—carry 20 systematic pitfalls. These include outdated and biased location-based social network data, accuracy-obsessed models that ignore context and other stakeholders, and offline evaluations that measure the wrong things. The authors' positive claim is that a structured agenda built around six directions—multistakeholder design, context-awareness, data collection, trustworthiness, novel interactions, and real-world evaluation—can turn the field toward systems that work outside the laboratory. A sympathetic reader should care because tourism recommendation is high-stakes: users spend time, money, and effort on the suggestions, and destinations face overcrowding and sustainability problems that current systems can worsen.

What carries the argument

The load-bearing object is the catalogue of 20 pitfalls, organized along three dimensions (datasets, algorithms, evaluation) and mapped to six solutions in a pitfall–solution table, with primary and secondary matches. The catalogue does the work of structuring the diagnosis, and the table does the work of showing that the solutions are neither arbitrary nor independent: for example, real-world evaluation is the primary remedy for six pitfalls and a secondary remedy for seven, while context-awareness is the only solution that does not touch any evaluation pitfall. The multistakeholder framing is the conceptual motor: it defines POI recommendation as a balancing act among tourists, destination managers, local businesses, and communities, which makes accuracy metrics an obviously insufficient target and turns the six solutions into a coherent agenda.

What would settle it

A systematic coding of the POI recommendation literature published since 2011, using a transparent protocol on the same 310 papers the authors rely on, would settle the prevalence claim: if a majority of those papers already avoid most of the 20 pitfalls—for example, by using post-2020 data, user studies, or multistakeholder metrics—the paper's central diagnosis would be false.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is a diagnosis and a route forward: POI recommender research has largely optimized accuracy on LBSN check-in histories, and in doing so has built systems that are outdated, biased, sparsely populated, unmindful of tourists' real contexts and group behavior, ignorant of the interests of destination managers and local communities, and impossible to compare or reproduce. The authors identify 20 pitfalls across the data, algorithmic, and evaluation dimensions and claim these are the most prevalent and critical blockers of real-world applicability. They then claim that a coordinated research agenda, with real-world evaluation as the keystone, is a viable solution: multistakeholder design addresses whose goals count, context-awareness handles in-trip re-planning, data collection replaces stale biased logs, trustworthiness designs make recommendations credible, novel interactions integrate trip components, and real-world evaluation tests systems in living labs and calibrated simulations. The conclusion they draw is that the majority of proposed solutions have failed to address the important needs and demands of the involved stakeholders.

Load-bearing premise

The claim that these 20 pitfalls are the most prevalent and critical rests on the authors' narrative reading of the literature, not on a systematic, transparent selection methodology; if that selection is skewed, the research agenda could be aimed at the wrong problems.

Editorial extensions

If this is right

  • If the diagnosis is correct, accuracy-focused offline benchmarks on Foursquare and Gowalla cannot serve as evidence that a POI recommender will work in practice; they need to be supplemented or replaced by newer, complete, stakeholder-aware data.
  • Algorithms that optimize only precision and recall will keep producing popular-biased, context-blind suggestions, so future models must adopt multi-objective targets that include diversity, fairness, and stakeholder interests.
  • Real-world evaluation—through living labs and calibrated simulations—becomes the primary filter for selecting candidate systems, with offline experiments used mainly to shortlist them.
  • The pitfall-solution mapping implies that no single fix is enough: data collection reforms change what algorithms can learn, and trustworthiness constraints shape novel interactions, so the agenda must be pursued as a coordinated program.
  • Reproducibility will require changes in publication norms—sharing code, data splits, and evaluation protocols—alongside new methods, since the field's own survey found that only 13 of 310 proposals published their code.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A likely consequence the authors do not spell out is that the next shared POI benchmark should incorporate post-COVID mobility data and stakeholder outcomes such as crowding reduction and small-business visibility, not just ranking accuracy.
  • The pitfall–solution mapping suggests a testable extension: because context-awareness is the only solution that does not address any evaluation pitfall, context-rich models tested with traditional offline protocols may show limited gains unless the evaluation is reformed at the same time.
  • Regulatory pressure—GDPR's consent requirements and the Digital Services Act's transparency duties—may push the data-collection and trustworthiness agenda faster than internal research incentives alone, making the proposed directions increasingly hard to ignore.
  • A concrete way to test the central claim would be a living-lab comparison of a multistakeholder, context-aware, explainable recommender against an accuracy-optimized baseline, measuring user satisfaction, crowding, and coverage of lesser-known venues; the authors' diagnosis predicts the former wins on those metrics.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. This reflection paper argues that POI recommendation research, despite its volume, remains poorly suited for real-world deployment. The authors identify 20 pitfalls across three dimensions—datasets (Pitfalls 1–10), algorithms (Pitfalls 11–15), and evaluation (Pitfalls 16–20)—and propose a six-part research agenda: multistakeholder design, context-awareness, data collection, trustworthiness design, novel interactions, and real-world evaluations. The paper reviews the state of the art in POI data (LBSNs, Flickr, mobility traces, user studies, synthetic data), algorithmic approaches (classical, neural, LLM-based, reinforcement learning, operations research), and evaluation practices. It closes with Table 1 mapping pitfalls to primary and secondary solutions, and a conclusion asserting that the majority of proposed POI solutions have failed to address stakeholder needs.

Significance. If treated as a position statement, the paper is useful and timely. It consolidates scattered concerns about LBSN data biases, MNAR issues, popularity bias, reproducibility, multistakeholder trade-offs, and the limitations of offline evaluation into a single structured catalog, and it translates these into a concrete research agenda with named solution directions. The paper also contains at least one concrete quantitative anchor: the 13-out-of-310 code-availability figure for POI recommendation papers, taken from [95]. Its main value is as a community reference for what has gone wrong and what could be done next, rather than as a new technical result. However, because the central claim is a quantitative generalization about the field, the evidentiary basis for that generalization must be examined carefully; this is where the manuscript currently needs strengthening.

major comments (2)
  1. [8 (Lessons Learned and Conclusions)] The concluding claim that “the majority of the proposed solutions have failed to address the important needs and demands of the involved stakeholders” is not supported by the evidence presented in the paper. The 20 pitfalls are introduced in Section 1 as selected “based on our review of the literature and discussions,” not from a systematic survey. Table 1 encodes the authors’ qualitative primary/secondary judgments, and the only concrete prevalence statistic in the paper (Pitfall 20: 13 of 310 proposals released code, citing [95]) concerns reproducibility rather than stakeholder need satisfaction. The text therefore demonstrates that many pitfalls are common, but it does not establish that a majority of proposed solutions fail. Either soften the quantifier (e.g., “many” or “a substantial share”) or provide a systematic evidence base, such as a coded sample of recent publications indicating the frequency of each pitfall.
  2. [7 and Table 1] The research agenda’s plausibility rests in part on Table 1’s assignment of each pitfall to primary and secondary solutions, but no criteria for these assignments are given. For example, Pitfall 3 (Incomplete Data) is assigned Data-Collection as primary and Context-Awareness as secondary, while one could reasonably argue that richer evaluation protocols or data augmentation are equally primary. Section 8’s statement that Real-World Evaluations “emerges as the most important area of improvement” is a counting result over these subjective assignments. The authors should either state explicit coding rules and ideally report inter-coder agreement, or explicitly present Table 1 as an informed opinion rather than a derived, reproducible result.
minor comments (4)
  1. [5, Pitfall 11] The first sentence contains a typo: “he incompleteness of LBSN datasets” should read “The incompleteness of LBSN datasets.”
  2. [4.1, Pitfall 1] The phrase “undermining the system’s real-world applicability of the system” is redundant; consider “undermining the system’s real-world applicability.”
  3. [Table 1] The column heading “Real-World Evalua- tions” is hyphenated across a line break; use “Real-World Evaluations” consistently.
  4. [1 and 8] The paper carefully disclaims that the pitfall list is not comprehensive, which is good; the same caution should be applied to the framing that the six solutions are “viable” or sufficient, since the manuscript does not demonstrate that the proposed agenda, if followed, would resolve the identified pitfalls.

Circularity Check

0 steps flagged · score 1.0 of 10

No material circularity: the paper is a narrative reflection whose self-citations serve as ordinary literature support, not as a load-bearing derivation.

full rationale

This is a reflection/position paper, not a derivation or fitting pipeline, so the standard circularity checks (prediction equal to fitted input, uniqueness theorem imported from the same authors, ansatz smuggled in via citation) do not apply. The 20 pitfalls are explicitly presented as a narrative selection: 'based on our review of the literature and discussions' (Section 1), and the six-solution agenda is a forward-looking synthesis 'starting from the identified issues' (Section 7), not a mathematical consequence of those pitfalls. Self-citations are frequent, e.g., [95] for the 13-of-310 code-release statistic, [94] for localized user behavior, and [97] for traveler/local segmentation, but they are used as ordinary literature support with checkable empirical content; they do not restate the paper's central claim by construction. The conclusion that 'the majority of the proposed solutions have failed' is under-supported and may be an over-generalization, but that is an evidentiary/validity weakness, not circularity. Table 1's primary/secondary mapping is subjective, and Section 8's ranking of Solution 6 depends on that mapping, but this is an explicit author judgment rather than a hidden redefinition. No step in the text reduces an output to an input by definition.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper contains no formal derivation, so there are no fitted parameters or invented entities. Its conclusions rest on domain assumptions about the representativeness of the literature and the goals that recommender systems should optimize.

assumptions (4)
  • domain assumption The 20 listed pitfalls are the most prevalent and critical in current POI recommendation research.
    Stated in Section 1 as 'based on our review of the literature and discussions on the state of the art'; no systematic methodology is provided, so this is an assumption.
  • domain assumption The academic literature and LBSN datasets are representative of real-world POI recommender failures and user behavior.
    The paper uses published papers and their datasets as evidence of systemic issues (Sections 3 and 4), assuming that publication and dataset availability reflect practice.
  • domain assumption Multistakeholder, sustainability, and trustworthiness are the correct normative objectives for POI recommender systems.
    The research agenda (Section 7) prescribes these goals as required improvements; this is a value judgment rather than an empirical finding.
  • domain assumption The authors' prior works cited as evidence are correctly interpreted and their statistics are reliable.
    For example, the 13/310 code-availability statistic in Pitfall 20 is taken from the authors' own survey [95] without independent verification.

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Cite this review

Pith. "Pith review of Point of Interest Recommendation: Pitfalls and Viable Solutions." pith.science (2026). https://pith.science/paper/A3RKLNQK

@misc{pith2026250713725,
  author       = {Pith},
  title        = {Pith review of: Point of Interest Recommendation: Pitfalls and Viable Solutions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/A3RKLNQK}},
  note         = {Machine review of arXiv:2507.13725}
}
read the original abstract

Point of interest (POI) recommendation can play a pivotal role in enriching tourists' experiences by suggesting context-dependent and preference-matching locations and activities, such as restaurants, landmarks, itineraries, and cultural attractions. Unlike some more common recommendation domains (e.g., music and video), POI recommendation is inherently high-stakes: users invest significant time, money, and effort to search, choose, and consume these suggested POIs. Despite the numerous research works in the area, several fundamental issues remain unresolved, hindering the real-world applicability of the proposed approaches. In this paper, we discuss the current status of the POI recommendation problem and the main challenges we have identified. The first contribution of this paper is a critical assessment of the current state of POI recommendation research and the identification of key shortcomings across three main dimensions: datasets, algorithms, and evaluation methodologies. We highlight persistent issues such as the lack of standardized benchmark datasets, flawed assumptions in the problem definition and model design, and inadequate treatment of biases in the user behavior and system performance. The second contribution is a structured research agenda that, starting from the identified issues, introduces important directions for future work related to multistakeholder design, context awareness, data collection, trustworthiness, novel interactions, and real-world evaluation.

Figures

Figures reproduced from arXiv: 2507.13725 by the authors.

Figure 1
Figure 1. Relationships between various entities concerning regional sustainability, tourism, and the environment, based on [ [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

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Works this paper leans on

126 extracted references · 58 canonical work pages

  1. [95]

    Pablo Sánchez and Alejandro Bellogín. 2022. Point-of-Interest Recommender Systems Based on Location-Based Social Networks: A Survey from an Experimental Perspective. Comput. Surveys 54, 11s (2022), 223:1–223:37. doi:10.1145/3510409

    doi:10.1145/3510409 2022

  2. [1]

    Himan Abdollahpouri and Robin Burke. 2022. Multistakeholder Recommender Systems. In Recommender Systems Handbook , Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.). Springer, 647–677. doi:10.1007/978-1-0716-2197-4_17

    work page doi:10.1007/978-1-0716-2197-4_17 2022

  3. [2]

    Gediminas Adomavicius, Konstantin Bauman, Alexander Tuzhilin, and Moshe Unger. 2022. Context-Aware Recommender Systems: From Foundations to Recent Developments. In Recommender Systems Handbook , Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.). Springer, 211–250. doi:10.1007/978-1-0716-2197-4_6

    work page doi:10.1007/978-1-0716-2197-4_6 2022

  4. [3]

    Taras Agryzkov, Pablo Martí, Leandro Tortosa, and José-Francisco Vicent. 2017. Measuring urban activities using Foursquare data and network analysis: a case study of Murcia (Spain). International Journal of Geographical Information Science 31, 1 (May 2017), 100–121. doi:10.1080/13658816. 2016.1188931

    arXiv 2017

  5. [4]

    Alrayes, Alia I

    Fatma S. Alrayes, Alia I. Abdelmoty, Waleed El-Geresy, and George Theodorakopoulos. 2020. Modelling perceived risks to personal privacy from location disclosure on online social networks. International Journal of Geographical Information Science 34, 1 (2020), 150–176. doi:10.1080/13658816. 2019.1654109

    arXiv 2020

  6. [5]

    Saraiva, João Carneiro, Paulo Novais, and Goreti Marreiros

    Patrícia Alves, Helena Martins, Pedro M. Saraiva, João Carneiro, Paulo Novais, and Goreti Marreiros. 2023. Group recommender systems for tourism: how does personality predict preferences for attractions, travel motivations, preferences and concerns? User Modeling and User-Adapted Interaction 33, 5 (2023), 1141–1210. doi:10.1007/S11257-023-09361-2

    work page doi:10.1007/s11257-023-09361-2 2023

  7. [6]

    Victor Anthony Arrascue Ayala, Kemal Cagin Gülsen, Anas Alzogbi, Michael Färber, Marco Muñiz, and Georg Lausen. 2017. A Delay-Robust Touristic Plan Recommendation Using Real-World Public Transportation Information. In 2nd RecSys Workshop on Recommenders in Tourism (CEUR Workshop Proceedings, Vol. 1906). CEUR-WS.org, 9–17

    2017

  8. [7]

    Gokulakrishnan Balakrishnan and Wolfgang Wörndl. 2021. Multistakeholder Recommender Systems in Tourism. In RecSys Workshop on Recom- menders in Tourism (CEUR Workshop Proceedings, Vol. 2974) . CEUR-WS.org, 39–53

    2021

Show all 126 references
  1. [8]

    Pieter Ballon and Dimitri Schuurman. 2015. Living labs: concepts, tools and cases. Info 17, 4 (2015). doi:10.2139/ssrn.2642754

    2015 doi

  2. [9]

    Linas Baltrunas, Bernd Ludwig, Stefan Peer, and Francesco Ricci. 2012. Context relevance assessment and exploitation in mobile recommender systems. Personal and Ubiquitous Computing 16, 5 (2012), 507–526. doi:10.1007/S00779-011-0417-X

    2012 doi

  3. [10]

    Ashmi Banerjee, Paromita Banik, and Wolfgang Wörndl. 2024. A review on individual and multistakeholder fairness in tourism recommender systems. Frontiers Big Data 6 (May 2024). doi:10.3389/fdata.2023.1168692

    2024

  4. [11]

    Patro, Linus W

    Ashmi Banerjee, Gourab K. Patro, Linus W. Dietz, and Abhijnan Chakraborty. 2020. Analyzing ‘Near Me’ Services: Potential for Exposure Bias in Location-based Retrieval. In International Workshop on Fair and Interpretable Learning Algorithms (FILA’20) . IEEE. doi:10.1109/bigdata...

    2020

  5. [12]

    Ashmi Banerjee, Adithi Satish, Fitri Nur Aisyah, Wolfgang Wörndl, and Yashar Deldjoo. 2025. SynthTRIPs: A Knowledge-Grounded Framework for Benchmark Query Generation for Personalized Tourism Recommenders. arXiv preprint arXiv:2504.09277 (2025). doi:10.48550/arXiv.2504.09277 In...

    2025 doi arXiv

  6. [14]

    Jie Bao, Yu Zheng, David Wilkie, and Mohamed Mokbel. 2015. Recommendations in location-based social networks: a survey. GeoInformatica 19, 3 (Feb. 2015), 525–565. doi:10.1007/s10707-014-0220-8

    2015 doi

  7. [15]

    Kassegn Berhanu and Sahil Raj. 2020. The trustworthiness of travel and tourism information sources of social media: perspectives of international tourists visiting Ethiopia. Heliyon 6, 3 (2020), e03439. doi:10.1016/j.heliyon.2020.e03439

    2020 doi

  8. [16]

    Samantha Boom, Jelmer Weijschede, Frans Melissen, Ko Koens, and Igor Mayer and. 2021. Identifying stakeholder perspectives and worldviews on sustainable urban tourism development using a Q-sort methodology. Current Issues in Tourism 24, 4 (2021), 520–535. doi:10.1080/13683500....

    2021

  9. [17]

    Matthias Braunhofer, Mehdi Elahi, Francesco Ricci, and Thomas Schievenin. 2014. Context-Aware Points of Interest Suggestion with Dynamic Weather Data Management. In Information and Communication Technologies in Tourism 2014 (2013). Springer, 87–100. doi:10.1007/978-3-319- 03973-2_7

    2013 doi

  10. [18]

    Matthias Braunhofer and Francesco Ricci. 2016. Contextual Information Elicitation in Travel Recommender Systems. In Information and Communication Technologies in Tourism 2016 (2016). Springer, 579–592. doi:10.1007/978-3-319-28231-2_42

    2016 doi

  11. [19]

    Igo Ramalho Brilhante, José Antônio Fernandes de Macêdo, Franco Maria Nardini, Raffaele Perego, and Chiara Renso. 2013. Where shall we go today?: planning touristic tours with tripbuilder. In 22nd ACM International Conference on Information & Knowledge Management (CIKM’13) . A...

    2013

  12. [20]

    Burke and Maryam Ramezani

    Robin D. Burke and Maryam Ramezani. 2011. Recommender Systems Handbook. Springer, Boston, MA, USA, Chapter Matching Recommendation Technologies and Domains, 367–386. doi:10.1007/978-0-387-85820-3_11

    2011 doi

  13. [21]

    Mark Anthony Camilleri and Ciro Troise. 2023. Chatbot recommender systems in tourism: A systematic review and a benefit-cost analysis. In 8th International Conference on Machine Learning Technologies . ACM, New York, NY, USA, 151–156. doi:10.1145/3589883.3589906

    2023

  14. [22]

    Rocío Cañamares and Pablo Castells. 2018. Should I Follow the Crowd?: A Probabilistic Analysis of the Effectiveness of Popularity in Recommender Systems. In 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 415–424. doi:10.1145/32...

    2018

  15. [23]

    Dawei Chen, Cheng Soon Ong, and Lexing Xie. 2016. Learning Points and Routes to Recommend Trajectories. In 25th ACM International on Conference on Information and Knowledge Management (CIKM’16) . ACM, New York, NY, USA, 2227–2232. doi:10.1145/2983323.2983672

    2016

  16. [24]

    Lei Chen, Guixiang Zhu, Weichao Liang, and Youquan Wang. 2023. Multi-objective reinforcement learning approach for trip recommendation. Expert Systems with Applications 226 (2023), 120145. doi:10.1016/J.ESWA.2023.120145

    2023

  17. [25]

    Maurizio Ferrari Dacrema, Simone Boglio, Paolo Cremonesi, and Dietmar Jannach. 2021. A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research. ACM Transactions on Information Systems 39, 2 (April 2021), 1–49. doi:10.1145/3434185

    2021 doi

  18. [26]

    Amra Delic, Hanif Emamgholizadeh, Thuy Ngoc Nguyen, and Francesco Ricci. 2024. CHARM: a Group Decision Making Support Chatbot. In 29th International Conference on Intelligent User Interfaces . ACM, 7–10. doi:10.1145/3640544.3645220

    2024

  19. [27]

    Amra Delic, Hanif Emamgholizadeh, Francesco Ricci, and Judith Masthoff. 2024. Supporting Group Decision-Making: Insights from a Focus Group Study. In 32nd ACM Conference on User Modeling, Adaptation and Personalization . ACM, 301–306. doi:10.1145/3627043.3659538

    2024

  20. [28]

    Amra Delic, Julia Neidhardt, Thuy Ngoc Nguyen, and Francesco Ricci. 2018. An observational user study for group recommender systems in the tourism domain. Information Technology & Tourism 19, 1-4 (2018), 87–116. doi:10.1007/S40558-018-0106-Y

    2018 doi

  21. [29]

    Dietz, Sanja Scepanovic, Ke Zhou, and Daniele Quercia

    Linus W. Dietz, Sanja Scepanovic, Ke Zhou, and Daniele Quercia. 2024. Exploratory Analysis of Recommending Urban Parks for Health-Promoting Activities. In 18th ACM Conference on Recommender Systems . ACM, 1131–1135. doi:10.1145/3640457.3691712

    2024

  22. [30]

    Dietz, Avradip Sen, Rinita Roy, and Wolfgang Wörndl

    Linus W. Dietz, Avradip Sen, Rinita Roy, and Wolfgang Wörndl. 2020. Mining Trips from Location-based Social Networks for Clustering Travelers and Destinations. Information Technology & Tourism 22, 1 (March 2020), 131–166. doi:10.1007/s40558-020-00170-6

    2020 doi

  23. [31]

    Dietz, Mete Sertkan, Saadi Myftija, Sameera Thimbiri Palage, Julia Neidhardt, and Wolfgang Wörndl

    Linus W. Dietz, Mete Sertkan, Saadi Myftija, Sameera Thimbiri Palage, Julia Neidhardt, and Wolfgang Wörndl. 2022. A Comparative Study of Data-driven Models for Travel Destination Characterization. Frontiers in Big Data 5 (April 2022). doi:10.3389/fdata.2022.829939

    2022

  24. [32]

    Eric A. Drott. 2018. Music as a Technology of Surveillance. Journal of the Society for American Music 12, 3 (July 2018), 233–267. doi:10.1017/ s1752196318000196

    2018

  25. [33]

    Hanif Emamgholizadeh, Amra Delic, and Francesco Ricci. 2024. Predicting Group Choices from Group Profiles. ACM Transactions on Interactive Intelligent Systems 14, 1 (2024), 7:1–7:27. doi:10.1145/3639710

    2024 doi

  26. [34]

    European Parliament and Council of the European Union. 2016. Regulation (EU) 2016/679 of the European Parliament and of the Council . OJ L 119, 4.5.2016, p. 1–88. https://data.europa.eu/eli/reg/2016/679/oj

    2016

  27. [35]

    Cipolla Ficarra

    Francisco V. Cipolla Ficarra. 2010. Human-Computer Interaction, Tourism and Cultural Heritage. In 1st International Workshop Human-Computer Interaction, Tourism and Cultural Heritage (Lecture Notes in Computer Science, Vol. 6529) . Springer, 39–50. doi:10.1007/978-3-642-18348-5_5

    2010 doi

  28. [36]

    Pantziou, and Nikolaos Vathis

    Damianos Gavalas, Charalampos Konstantopoulos, Konstantinos Mastakas, Grammati E. Pantziou, and Nikolaos Vathis. 2015. Heuristics for the time dependent team orienteering problem: Application to tourist route planning. Computers & Operations Research and their Application to P...

    2015 doi

  29. [37]

    Juergen Gnoth. 1997. Tourism motivation and expectation formation. Annals of Tourism research 24, 2 (1997), 283–304. doi:10.1016/s0160- 7383(97)80002-3 20 Bellogín, Dietz, Ricci, & Sánchez

    1997 doi

  30. [38]

    Dmitri Goldenberg and Javier Albert. 2022. Personalizing Benefits Allocation Without Spending Money: Utilizing Uplift Modeling in a Budget Constrained Setup. In 16th ACM Conference on Recommender Systems . ACM, New York, NY, USA, 464–465. doi:10.1145/3523227.3547381

    2022

  31. [39]

    Dmitri Goldenberg, Sarai Mizrachi, Adam Horowitz, Ioannis Kangas, Or Levkovich, Alessandro Mozzato, Maud Schwoerer, Michele Ferretti, Panagiotis Korvesis, and Lucas Bernardi. 2021. I Know What You Did Next Summer: Challenges in Travel Destinations Recommendation. InRecSys Work...

    2021

  32. [40]

    Ulrike Gretzel. 2011. Intelligent systems in tourism: A social science perspective. Annals of tourism research 38, 3 (2011), 757–779. doi:10.1016/j. annals.2011.04.014

    2011 doi

  33. [41]

    Ibrahim Al Hazwani, Tiantian Luo, Oana Inel, Francesco Ricci, Mennatallah El-Assady, and Jürgen Bernard. 2024. ScrollyPOI: A Narrative-Driven Interactive Recommender System for Points-of-Interest Exploration and Explainability. In 32nd ACM Conference on User Modeling, Adaptati...

    2024

  34. [42]

    Hernández, Andrei P

    Juan M. Hernández, Andrei P. Kirilenko, and Svetlana Stepchenkova. 2018. Network approach to tourist segmentation via user generated content. Annals of Tourism Research 73 (2018), 35–47. doi:10.1016/j.annals.2018.09.002

    2018 doi

  35. [43]

    Dietz, and Wolfgang Wörndl

    Daniel Herzog, Linus W. Dietz, and Wolfgang Wörndl. 2019. Tourist Trip Recommendations – Foundations, State of the Art and Challenges. In Personalized Human-Computer Interaction, Miriam Augstein, Eelco Herder, and Wolfgang Wörndl (Eds.). de Gruyter Oldenbourg, Berlin, Germany,...

    2019 doi

  36. [45]

    Matthew Hutson. 2018. Artificial intelligence faces reproducibility crisis. Science 359, 6377 (2018), 725–726. doi:10.1126/science.359.6377.725

    2018 doi

  37. [46]

    Willemsen, and Alexander Felfernig

    Anthony Jameson, Martijn C. Willemsen, and Alexander Felfernig. 2022. Individual and Group Decision Making and Recommender Systems. In Recommender Systems Handbook, Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.). Springer, 789–832. doi:10.1007/978-1-0716-2197-4_21

    2022 doi

  38. [47]

    Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, and Li Chen. 2022. A Survey on Conversational Recommender Systems. Comput. Surveys 54, 5 (2022), 105:1–105:36. doi:10.1145/3453154

    2022 doi

  39. [48]

    Dietmar Jannach and Markus Zanker. 2020. Interactive and Context-Aware Systems in Tourism. In Handbook of e-Tourism, Zheng Xiang, Matthias Fuchs, Ulrike Gretzel, and Wolfram Höpken (Eds.). Springer, Cham, 1–22. doi:10.1007/978-3-030-05324-6_125-1

    2020 doi

  40. [49]

    Priit Järv. 2017. Extracting Human Mobility Data from Geo-tagged Photos. In 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility . ACM, 4:1–4:7. doi:10.1145/3152341.3152346

    2017

  41. [50]

    Yitong Ji, Aixin Sun, Jie Zhang, and Chenliang Li. 2023. A Critical Study on Data Leakage in Recommender System Offline Evaluation. ACM Transactions on Information Systems 41, 3 (2023), 75:1–75:27. doi:10.1145/3569930

    2023 doi

  42. [51]

    Esma Karahodža, Amra Delić, and Francesco Ricci. 2025. Conceptual Framework for Group Dynamics Modeling from Group Chat Interactions. In 33rd ACM Conference on User Modeling, Adaptation and Personalization . ACM, New York City, NY, USA, 5. doi:10.1145/3708319.3733682

    2025

  43. [52]

    Ibrahim Khalil and Mst

    Md. Ibrahim Khalil and Mst. Khadijatul Kobra. 2021. Monitoring overtourism: Destination Management Systems as a way forward. Bangladesh Journal of Public Administration 29, 2 (2021), 79–87. doi:10.36609/bjpa.v29i2.211

    2021 doi

  44. [53]

    Minkyoung Kim, Lexing Xie, and Peter Christen. 2021. Event Diffusion Patterns in Social Media. International AAAI Conference on Web and Social Media 6, 1 (Aug. 2021), 178–185. doi:10.1609/icwsm.v6i1.14248

    2021 doi

  45. [54]

    Burguillo

    Fátima Leal, Benedita Malheiro, and Juan C. Burguillo. 2018. Context-aware tourism technologies. The Knowledge Engineering Review 33 (2018). doi:10.1017/s0269888918000152

    2018 doi

  46. [55]

    Burguillo

    Fátima Leal, Bruno Veloso, Benedita Malheiro, and Juan C. Burguillo. 2025. Towards adaptive and transparent tourism recommendations: A survey. Expert Systems: The Journal of Knowledge Engineering 42, 1 (2025). doi:10.1111/EXSY.13400

    2025 doi

  47. [56]

    Forina Lestari, Melasutra Md Dali, and Norbani Che-Ha. 2022. The Importance of A Multistakeholder Perspective in Mapping Stakeholders’ Roles Toward City Branding Implementation. Policy & Governance Review 6, 3 (2022), 264–281. doi:10.30589/pgr.v6i3.601

    2022 doi

  48. [57]

    Changheng Li, Yongjing Hao, Pengpeng Zhao, Fuzhen Zhuang, Yanchi Liu, and Victor S. Sheng. 2021. Tell Me Where to Go Next: Improving POI Recommendation via Conversation. In 26th International Conference on Database Systems for Advanced Applications (Lecture Notes in Computer S...

    2021 doi

  49. [58]

    Peibo Li, Maarten de Rijke, Hao Xue, Shuang Ao, Yang Song, and Flora D. Salim. 2024. Large Language Models for Next Point-of-Interest Recommendation. In 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’24) . ACM, 1463–1472. do...

    2024

  50. [59]

    Kwan Hui Lim, Jeffrey Chan, Christopher Leckie, and Shanika Karunasekera. 2016. Towards Next Generation Touring: Personalized Group Tours. In 26th International Conference on Automated Planning and Scheduling , Vol. 26. AAAI, 412–420. doi:10.1609/icaps.v26i1.13775

    2016 doi

  51. [60]

    Kwan Hui Lim, Jeffrey Chan, Christopher Leckie, and Shanika Karunasekera. 2018. Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency.Knowledge and Information Systems 54, 2 (2018), 375–406. doi:10.1007/S101...

    2018 doi

  52. [61]

    Sebastian Linxen, Christian Sturm, Florian Brühlmann, Vincent Cassau, Klaus Opwis, and Katharina Reinecke. 2021. How WEIRD is CHI?. In 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21) . ACM, New York, NY, USA, Article 143, 14 pages. doi:10.1145/3411764.3445488

    2021

  53. [62]

    Xin Liu, Yongjian Yang, Yuanbo Xu, Funing Yang, Qiuyang Huang, and Hong Wang. 2022. Real-time POI recommendation via modeling long- and short-term user preferences. Neurocomputing 467 (2022), 454–464. doi:10.1016/J.NEUCOM.2021.09.056

    2022 doi

  54. [63]

    Rui Neves Madeira, Samuel Robalo, and André Cordeiro. 2024. SHIFT towards Sustainable Tourism: Designing a Ubiquitous Platform for Co- Creating Experiences with Tourists and Residents. In International Conference on Mobile and Ubiquitous Multimedia . ACM, New York, NY, USA, Po...

    2024

  55. [64]

    Manqing Mao, Paishun Ting, Yijian Xiang, Mingyang Xu, Julia Chen, and Jianzhe Lin. 2024. Multi-User Chat Assistant (MUCA): a Framework Using LLMs to Facilitate Group Conversations. arXiv (2024). doi:10.48550/arXiv.2401.04883

    2024 doi arXiv

  56. [65]

    David Massimo and Francesco Ricci. 2019. Users’ Evaluation of Next-POI Recommendations. In RecSys Recommenders in Tourism Workshop (CEUR Workshop Proceedings, Vol. 2435). CEUR-WS.org, 1–8

    2019

  57. [66]

    David Massimo and Francesco Ricci. 2021. Popularity, novelty and relevance in point of interest recommendation: an experimental analysis. Information Technology & Tourism 23, 4 (2021), 473–508. doi:10.1007/S40558-021-00214-5

    2021 doi

  58. [67]

    David Massimo and Francesco Ricci. 2023. Combining Reinforcement Learning and Spatial Proximity Exploration for New User and New POI Recommendations. In 31st ACM Conference on User Modeling, Adaptation and Personalization . ACM, 164–174. doi:10.1145/3565472.3592966

    2023

  59. [68]

    Noemi Mauro, Livio Scarpinati, Fabio Ferrero, Angelo Geninatti Cossatin, and Claudio Mattutino. 2024. Point-of-Interest Recommender Systems: Nudging towards Sustainable Tourism. In 32nd ACM Conference on User Modeling, Adaptation and Personalization . ACM, New York, NY, USA, 4...

    2024

  60. [69]

    Zaiqiao Meng, Richard McCreadie, Craig Macdonald, and Iadh Ounis. 2020. Exploring Data Splitting Strategies for the Evaluation of Recommendation Models. In 14th ACM Conference on Recommender Systems . ACM, 681–686. doi:10.1145/3383313.3418479

    2020

  61. [70]

    Pavel Merinov, David Massimo, and Francesco Ricci. 2023. Behaviour-aware Tourist Profiles Data Generation. In 13th Italian Information Retrieval Workshop (CEUR Workshop Proceedings, Vol. 3448). CEUR-WS.org, 3–8

    2023

  62. [72]

    Sara Migliorini, Damiano Carra, and Alberto Belussi. 2021. Distributing Tourists among POIs with an Adaptive Trip Recommendation System. IEEE Transactions on Emerging Topics in Computing 9, 4 (2021), 1765–1779. doi:10.1109/TETC.2019.2920484

    2021

  63. [73]

    Thuy Ngoc Nguyen and Francesco Ricci. 2018. A chat-based group recommender system for tourism. Information Technology & Tourism 18, 1-4 (2018), 5–28. doi:10.1007/S40558-017-0099-Y

    2018 doi

  64. [74]

    Thuy Ngoc Nguyen, Francesco Ricci, Amra Delic, and Derek G. Bridge. 2019. Conflict resolution in group decision making: insights from a simulation study. User Modeling and User-Adapted Interaction 29, 5 (2019), 895–941. doi:10.1007/S11257-019-09240-9

    2019 doi

  65. [75]

    Anastasios Noulas, Salvatore Scellato, Cecilia Mascolo, and Massimiliano Pontil. 2011. An Empirical Study of Geographic User Activity Patterns in Foursquare. In 5th International Conference on Weblogs and Social Media , Vol. 5. AAAI, 570–573. doi:10.1609/icwsm.v5i1.14175

    2011 doi

  66. [76]

    Mariam Orabi, Djedjiga Mouheb, Zaher Al Aghbari, and Ibrahim Kamel. 2020. Detection of bots in social media: a systematic review. Information Processing & Management 57, 4 (July 2020), 102250. doi:10.1016/j.ipm.2020.102250

    2020

  67. [77]

    Keisuke Otaki and Yukino Baba. 2025. Travel itinerary recommendation using interaction-based augmented data. Expert Systems with Applications 269 (2025), 126294. doi:10.1016/J.ESWA.2024.126294

    2025

  68. [78]

    Suayb Talha Ozcelik, Meltem Turhan Yondem, Ines Caetano, Jose Figueiredo, Patricia Alves, Goreti Marreiros, Huseyin Bahtiyar, Eda Yuksel, Fernando Perales, and George Suciu. 2024. Transforming Tourism Experience: AI-Based Smart Travel Platform. In 4th European Symposium on Sof...

    2024

  69. [79]

    Sangwon Park, Yang Yang, and Mingshu Wang. 2019. Travel distance and hotel service satisfaction: An inverted U-shaped relationship.International Journal of Hospitality Management 76 (2019), 261–270. doi:10.1016/j.ijhm.2018.05.015

    2019 doi

  70. [80]

    Isabel Paulino, Sergi Lozano, and Lluís Prats. 2021. Identifying tourism destinations from tourists’ travel patterns. Journal of Destination Marketing & Management 19 (2021), 100508. doi:10.1016/j.jdmm.2020.100508

    2021

  71. [81]

    Pearce and Uk-Il Lee

    Philip L. Pearce and Uk-Il Lee. 2005. Developing the Travel Career Approach to Tourist Motivation. Journal of Travel Research 43, 3 (2005), 226–237. doi:10.1177/0047287504272020

    2005 doi

  72. [82]

    Harald Pechlaner. 2000. Cultural heritage and destination management in the Mediterranean. Thunderbird International Business Review 42, 4 (2000), 409–426. doi:10.1002/1520-6874(200007/08)42:4<409::aid-tie4>3.0.co;2-f

    2000 doi

  73. [83]

    Harald Pechlaner, Elisa Innerhofer, and Greta Erschbamer. 2019. Overtourism: Tourism management and solutions . Routledge, London

    2019

  74. [84]

    Morgan Perry. 2023. Role of Cultural Heritage Preservation in Destination Branding and Tourist Experience Enhancement. Hospitality and Tourism Journal 1, 1 (2023), 36–47

    2023

  75. [85]

    Greta Piliponyte, David Massimo, and Francesco Ricci. 2023. The Impact of Personalised Advertisement Campaigns on Tourist Choices in South Tyrol: A Sustainable Tourism Perspective. In 31st ACM Conference on User Modeling, Adaptation and Personalization . ACM, 100–103. doi:10.1...

    2023

  76. [86]

    Greta Piliponyte, David Massimo, and Francesco Ricci. 2024. Simulation of recommender systems driven tourism promotion campaigns.Information Technology & Tourism 26, 3 (2024), 407–448. doi:10.1007/S40558-024-00283-2

    2024 doi

  77. [87]

    Stanley C. Plog. 1974. Why Destination Areas Rise and Fall in Popularity. Cornell Hotel and Restaurant Administration Quarterly 14, 4 (1974), 55–58. doi:10.1177/001088047401400409

    1974 doi

  78. [88]

    Francesco Ricci and Amra Delić. 2025. Widening the Role of Group Recommender Systems with CAJO. doi:10.48550/arxiv.2504.05934 arXiv:2504.05934

    2025 doi arXiv

  79. [89]

    Francesco Ricci, David Massimo, and Antonella De Angeli. 2021. Challenges for Recommender Systems Evaluation. In 14th Biannual Conference of the Italian SIGCHI Chapter . ACM, 25:1–25:5. doi:10.1145/3464385.3464733 22 Bellogín, Dietz, Ricci, & Sánchez

    2021

  80. [90]

    Rafael Robina-Ramírez, Jesús Torrecilla-Pinero, Ana Leal-Solís, and Juan Antonio Pavón-Pérez. 2024. Tourism as a driver of economic and social development in underdeveloped regions. Regional Science Policy & Practice 16, 1 (2024), 12639. doi:10.1111/rsp3.12639

    2024 doi

  81. [91]

    Luca Rossi, Matthew Williams, Christoph Stich, and Mirco Musolesi. 2015. Privacy and the City: User Identification and Location Semantics in Location-Based Social Networks. In 9th International Conference on Web and Social Media , Vol. 9. AAAI, 387–396. doi:10.1609/icwsm.v9i1.14595

    2015 doi

  82. [92]

    Pablo Sánchez. 2019. Exploiting contextual information for recommender systems oriented to tourism. In 13th ACM Conference on Recommender Systems. ACM, New York, NY, USA, 601–605. doi:10.1145/3298689.3347062

    2019

  83. [93]

    Pablo Sánchez and Alejandro Bellogín. 2020. Applying reranking strategies to route recommendation using sequence-aware evaluation. User Modeling and User-Adapted Interaction 30, 4 (2020), 659–725. doi:10.1007/S11257-020-09258-4

    2020 doi

  84. [94]

    Pablo Sánchez and Alejandro Bellogín. 2021. On the effects of aggregation strategies for different groups of users in venue recommendation. Information Processing and Management 58, 5 (2021), 102609. doi:10.1016/J.IPM.2021.102609

    2021

  85. [96]

    Pablo Sánchez, Alejandro Bellogín, and Ludovico Boratto. 2023. Bias characterization, assessment, and mitigation in location-based recommender systems. Data Mining and Knowledge Discovery 37, 5 (2023), 1885–1929. doi:10.1007/S10618-022-00913-5

    2023 doi

  86. [97]

    Pablo Sánchez and Linus W. Dietz. 2022. Travelers vs. Locals: The Effect of Cluster Analysis in Point-of-Interest Recommendation. In 30th ACM Conference on User Modeling, Adaptation and Personalization . ACM, 132–142. doi:10.1145/3503252.3531320

    2022

  87. [98]

    Clodomir Santana, Federico Botta, Hugo Barbosa, Filippo Privitera, Ronaldo Menezes, and Riccardo Di Clemente. 2023. COVID-19 is linked to changes in the time–space dimension of human mobility. Nature Human Behaviour 7, 10 (July 2023), 1729–1739. doi:10.1038/s41562-023-01660-3

    2023 doi

  88. [99]

    Ali Akbar Septiandri, Marios Constantinides, Mohammad Tahaei, and Daniele Quercia. 2023. WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?. In ACM Conference on Fairness, Accountability, and Transparency (FAccT’23) . ACM, New York, NY, USA, 16...

    2023

  89. [100]

    Yewei Shang, Montserrat Pallares-Barbera, and Francesc Romagosa. 2023. Coordination Mechanism of Stakeholders in Tourism Destinations: A Social Network Analysis Exploration. In New Perspectives and Paradigms in Applied Economics and Business . Springer, Cham, 363–381. doi:10.1...

    2023

  90. [101]

    Egon Smeral. 2019. Overcrowding of tourism destinations: Some suggestions for a solution. In Overtourism. Routledge, 163–173

    2019

  91. [102]

    Xiaoyu Song, Zhizhong Liu, Lingqiang Meng, Dianhui Chu, Jian Yu, and Quan Z. Sheng. 2025. Accurate POI recommendation for random groups with improved graph neural networks and a multi-negotiation model. Scientific Reports 15, 1 (March 2025). doi:10.1038/s41598-025-91805-3

    2025 doi

  92. [103]

    Michaël Soumm, Adrian Popescu, and Bertrand Delezoide. 2023. Vis2Rec: A Large-Scale Visual Dataset for Visit Recommendation. In IEEE/CVF Winter Conference on Applications of Computer Vision . IEEE, 2986–2996. doi:10.1109/WACV56688.2023.00300

    2023

  93. [104]

    Fesenmaier, Cécile Paris, and Craig A

    Steffen Staab, Hannes Werthner, Francesco Ricci, Alexander Zipf, Ulrike Gretzel, Daniel R. Fesenmaier, Cécile Paris, and Craig A. Knoblock. 2002. Intelligent Systems for Tourism. IEEE Intelligent Systems 17, 6 (2002), 53–64. doi:10.1109/MIS.2002.1134362

    2002 arXiv

  94. [105]

    Ugljesa Stankov and Ulrike Gretzel. 2020. Tourism 4.0 technologies and tourist experiences: a human-centered design perspective. Information Technology & Tourism 22, 3 (2020), 477–488. doi:10.1007/S40558-020-00186-Y

    2020 doi

  95. [106]

    Huimin Sun, Jiajie Xu, Kai Zheng, Pengpeng Zhao, Pingfu Chao, and Xiaofang Zhou. 2021. MFNP: A Meta-optimized Model for Few-shot Next POI Recommendation. In 30th International Joint Conference on Artificial ntelligence (IJCAI’21) . International Joint Conferences on Artificial...

    2021 doi

  96. [107]

    Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li

    Bart Thomee, David A. Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li. 2016. YFCC100M: the new data in multimedia research. Commun. ACM 59, 2 (2016), 64–73. doi:10.1145/2812802

    2016 doi

  97. [108]

    Nava Tintarev and Judith Masthoff. 2022. Beyond Explaining Single Item Recommendations. In Recommender Systems Handbook , Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.). Springer, New York, NY, 711–756. doi:10.1007/978-1-0716-2197-4_19

    2022 doi

  98. [109]

    Leonardo Tonetto, Pauline Kister, Nitinder Mohan, and Jörg Ott. 2024. Ethical and Privacy Considerations with Location Based Data Research. doi:10.48550/arxiv.2403.05558 arXiv:2403.05558 [cs.CY]

    2024 doi arXiv

  99. [110]

    Christoph Trattner, Alexander Oberegger, Leandro Balby Marinho, and Denis Parra. 2018. Investigating the Utility of the Weather Context for Point of Interest Recommendations. Information Technology & Tourism 19, 1-4 (2018), 117–150. doi:10.1007/s40558-017-0100-9

    2018 doi

  100. [111]

    Pantziou, and Damianos Gavalas

    Nikolaos Vathis, Charalampos Konstantopoulos, Grammati E. Pantziou, and Damianos Gavalas. 2023. The Vacation Planning Problem: A multi-level clustering-based metaheuristic approach. Computers & Operations Research and their Application to Problems of World Concern 150 (2023), ...

    2023

  101. [112]

    Anna Dalla Vecchia, Sara Migliorini, Elisa Quintarelli, and Alberto Belussi. 2023. A Context-Aware Recommendation System with a Crowding Forecaster. In 31st Symposium of Advanced Database Systems (CEUR Workshop Proceedings, Vol. 3478) . CEUR-WS.org, 632–640

    2023

  102. [113]

    Anna Dalla Vecchia, Sara Migliorini, Elisa Quintarelli, Mauro Gambini, and Alberto Belussi. 2024. Promoting sustainable tourism by recommending sequences of attractions with deep reinforcement learning.Information Technology & Tourism26, 3 (2024), 449–484. doi:10.1007/S40558-0...

    2024 doi

  103. [114]

    Bin Wang, Tiantian Dong, Yunyao Liu, Jay Kandampully, and Zhanlu Tang and. 2024. Males or females in solo or group travel: how do they impact travel intentions of potential tourists with different self-construals? Current Issues in Tourism (2024), 1–20. doi:10.1080/13683500.20...

    2024

  104. [115]

    Will Check-in for Badges

    Gang Wang, Sarita Yardi Schoenebeck, Haitao Zheng, and Ben Y. Zhao. 2016. "Will Check-in for Badges": Understanding Bias and Misbehavior on Location-Based Social Networks. In Tenth International Conference on Web and Social Media . AAAI, 417–426. Point of Interest Recommendati...

    2016

  105. [116]

    Shirui Wang, Bohan Xie, Ling Ding, Xiaoying Gao, Jianting Chen, and Yang Xiang. 2024. SeCor: Aligning Semantic and Collaborative Representations by Large Language Models for Next-Point-of-Interest Recommendations. In 18th ACM Conference on Recommender Systems . ACM, 1–11. doi:...

    2024

  106. [117]

    Shoujin Wang, Xiuzhen Zhang, Yan Wang, and Francesco Ricci. 2024. Trustworthy Recommender Systems. ACM Transactions on Intelligent Systems and Technology 15, 4 (2024), 84:1–84:20. doi:10.1145/3627826

    2024 doi

  107. [118]

    Xiaojie Wang, Rui Zhang, Yu Sun, and Jianzhong Qi. 2019. Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random. In 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97) . PMLR, 6638–6647

    2019

  108. [119]

    Zehui Wang, Wolfram Höpken, and Dietmar Jannach. 2025. A survey on point-of-interest recommendations leveraging heterogeneous data. Information Technology & Tourism 27, 1 (2025), 29–73. doi:10.1007/S40558-024-00301-3

    2025 doi

  109. [120]

    Heitor Werneck, Rodrigo Santos, Nícollas Silva, Adriano C. M. Pereira, Fernando Mourão, and Leonardo Rocha. 2021. Effective and diverse POI recommendations through complementary diversification models. Expert Systems with Applications 175 (2021), 114775. doi:10.1016/J.ESWA.202...

    2021 doi

  110. [121]

    Hannes Werthner and Stefan Klein. 1999. ICT and the Changing Landscape of Global Tourism Distribution. Electronic Markets 9, 4 (1999), 256–262. doi:10.1080/101967899358941

    1999 doi

  111. [122]

    Yanan Xiao, Lu Jiang, Kunpeng Liu, Yuanbo Xu, Pengyang Wang, and Minghao Yin. 2024. Hierarchical Reinforcement Learning for Point of Interest Recommendation. In 33rd International Joint Conference on Artificial Intelligence (IJCAI’24) . International Joint Conferences on Artif...

    2024 doi

  112. [123]

    Olmos, David Mateo, Alberto Hernando, Xiaokang Yang, and Marta C

    Yanyan Xu, Luis E. Olmos, David Mateo, Alberto Hernando, Xiaokang Yang, and Marta C. González. 2023. Urban dynamics through the lens of human mobility. Nature Computational Science 3, 7 (July 2023), 611–620. doi:10.1038/s43588-023-00484-5

    2023 doi

  113. [124]

    Takahiro Yabe, Kota Tsubouchi, Toru Shimizu, Yoshihide Sekimoto, Kaoru Sezaki, Esteban Moro, and Alex Pentland. 2024. YJMob100K: City-Scale and Longitudinal Dataset of Anonymized Human Mobility Trajectories. doi:10.5281/ZENODO.10836269

    2024 doi

  114. [125]

    Emre Yalcin and Alper Bilge. 2022. Evaluating unfairness of popularity bias in recommender systems: A comprehensive user-centric analysis. Information Processing and Management 59, 6 (2022), 103100. doi:10.1016/J.IPM.2022.103100

    2022

  115. [126]

    An Yan, Zhankui He, Jiacheng Li, Tianyang Zhang, and Julian McAuley. 2023. Personalized Showcases: Generating Multi-Modal Explanations for Recommendations. In 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’23) . ACM, New Yor...

    2023

  116. [127]

    Chenyi Zhang and Ke Wang. 2016. POI recommendation through cross-region collaborative filtering. Knowledge and Information Systems 46, 2 (2016), 369–387. doi:10.1007/S10115-015-0825-8

    2016 doi

  117. [128]

    Lu Zhang, Zhu Sun, Jie Zhang, Yiwen Wu, and Yunwen Xia. 2023. Conversation-Based Adaptive Relational Translation Method for Next POI Recommendation With Uncertain Check-Ins. IEEE Transactions on Neural Networks and Learning Systems 34, 10 (2023), 7810–7823. doi:10.1109/ TNNLS....

    2023

  118. [129]

    Vincent Wenchen Zheng, Yu Zheng, Xing Xie, and Qiang Yang. 2010. Collaborative location and activity recommendations with GPS history data. In 19th International Conference on World Wide Web . ACM, 1029–1038. doi:10.1145/1772690.1772795

    2010

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.