Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places

Ilya Ilyankou; Junyuan Liu; Lu Yin; Natchapon Jongwiriyanurak; Stephen Law; Tao Cheng; Weiming Huang; Xinglei Wang; Zichao Zeng

REVIEW 3 major objections 5 minor 44 references

Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper claims that CaLLiPer, a multimodal location embedding learned by aligning coordinates with point-of-interest text through contrastive learning, improves next-location prediction, and that its main advantage appears when the…

desk verdict A useful application study whose inductive claim is not yet established: the held-out POIs are likely not held out from CaLLiPer's pretraining, and baselines are left without a mechanism to embed unseen locations. read the letter →

arxiv 2506.14070 v1 pith:ZEZ37FVL submitted 2025-06-17 cs.AI

Xinglei Wang , Tao Cheng , Stephen Law , Zichao Zeng , Ilya Ilyankou , Junyuan Liu , Lu Yin , Weiming Huang

show 1 more author

Natchapon Jongwiriyanurak

This is my paper · ORCID

classification cs.AI

keywords humanmobilitynextlocationpredictionembeddingpointsofinterestcontrastivelearninginductivespatialsemanticslocation-basedservices

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 tries to show that location embeddings for next-location prediction need not be learned from historical mobility traces alone. It applies CaLLiPer, a contrastively trained multimodal encoder that maps coordinates and point-of-interest (POI) text into a shared space, as a drop-in replacement for the lookup-table embeddings used by most predictors. Experiments on four mobility datasets compare this embedding with six baselines under a conventional split and under an inductive split that hides ten percent of locations from training. The claim is that CaLLiPer matches or beats the baselines in most settings, and that its advantage is largest when the test set contains locations the predictor has never seen. If true, it means predictors can keep working when users visit new places without retraining the embedding layer.

What carries the argument

The load-bearing object is CaLLiPer, a multimodal representation-learning framework that aligns a coordinate-based location encoder with a text encoder over point-of-interest descriptions. The location encoder maps geographic coordinates through a multi-scale Grid positional encoding and a fully connected network; the text encoder is a Sentence Transformer; and the two modalities are pulled together by a bidirectional InfoNCE contrastive objective. Because the encoder maps any coordinate rather than only locations in a fixed vocabulary, it is inductive: an embedding exists for any unseen location. The downstream mechanism is MHSA, a multi-head self-attention Transformer that ingests these frozen embeddings and outputs a softmax over candidate next locations; the paper uses its performance as the measurement of embedding quality.

What would settle it

Re-run the FSQ-NYC inductive experiment after deleting from CaLLiPer's pre-training corpus every POI whose coordinates overlap the held-out set L_new, while keeping the downstream training identical. If the Acc@1 advantage over the best baseline (reportedly 16.07 versus 15.25) disappears, the claimed generalisation to unseen places fails the 'unseen' test.

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Extended reading notes

Core claim

The central discovery is that location embeddings built from general spatial and semantic data, rather than from individual mobility traces, can serve as drop-in substitutes for conventional lookup-table embeddings in next-location prediction. The paper applies CaLLiPer, pre-trained by contrasting coordinates with accompanying point-of-interest text, to four public mobility datasets in both a conventional setting and an inductive setting where ten percent of locations are absent from the training and validation sequences. The results show CaLLiPer attains the best scores on all metrics in both settings for FSQ-NYC and Gowalla-LD; on FSQ-TKY it becomes best on most metrics in the inductive setting; and on Geolife it is best on three of five metrics in both settings. From these numbers the paper concludes that the inductive, spatially explicit, semantically enriched nature of CaLLiPer embeddings makes them particularly suited to scenarios where new locations emerge.

Load-bearing premise

The inductive comparison is only fair if every method gets the same kind of information about unseen locations; the paper leaves open whether baselines receive only random or zero vectors for held-out locations while CaLLiPer still benefits from their coordinates and POI semantics.

Editorial extensions

If this is right

Predictive systems can serve new locations without retraining the embedding layer, because the location encoder produces vectors for any coordinate at inference time.
Embedding pre-training no longer depends on mobility traces, so it can exploit publicly available POI data even when detailed movement data are privacy-restricted.
On two datasets the inductive gains are larger than the conventional gains, and on a third the method moves from behind in the conventional setting to leading most metrics in the inductive setting, indicating the spatial-semantic encoder carries much of the generalisation load.
The same frozen embeddings feed the same downstream predictor across datasets and settings, so the representation can act as reusable infrastructure rather than a task-specific lookup table.

Reading between the lines

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

A stronger inductive test would also withhold the held-out locations' POI descriptions from CaLLiPer's pre-training corpus; the paper does not report whether such overlap exists, so part of the 'unseen' advantage could come from the semantic channel rather than from spatial continuity alone.
Comparing CaLLiPer against a coordinate-only location encoder (without POI text) would separate the contribution of spatial continuity from semantic enrichment, an ablation the paper does not run.
The same embeddings could be tested for cold-start mobility prediction, where a new user's first check-in is to an unseen location; if the inductive mechanism works, the predictor should degrade far less for users with few observations than lookup-table baselines.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague, you should know two things: the paper is a legitimate application study of a strong prior model, and its headline inductive result is undercut by a protocol gap that is easy to miss.

What's new: the authors take CaLLiPer, their earlier POI-contrastive location encoder, and apply it as the embedding layer for next-location prediction. That application is new, and so is the inductive split they design to simulate unseen locations. They run four public datasets, use a common downstream model, release code, and the conventional-setting results are reasonably strong: CaLLiPer is best on all metrics on FSQ-NYC and Gowalla-LD, and is competitive on the other two. That is real evidence that POI-based spatial-semantic embeddings help for mobility, and the code will be useful to the community.

The soft spot is the inductive protocol. Section 4.3 defines L_new as locations whose mobility sequences are removed from training and validation, but it never says what happens to the embeddings of those locations. For CaLLiPer, pretraining uses the POI data from these same datasets; for FSQ-NYC and FSQ-TKY the POIs are the dataset's own locations, so the held-out L_new POIs were almost certainly fed into CaLLiPer's contrastive pretraining. Calling them 'unseen' is therefore misleading. For the baselines, all six are lookup-table models with fixed vocabularies; after removing sequences they have no way to produce vectors for L_new. The paper is silent on whether they get random, zero, or re-normalized vectors. That asymmetry alone could explain much of CaLLiPer's inductive advantage. This is not a minor detail; it is the load-bearing claim of the paper.

The abstract also overstates the results. Table 2 shows CaLLiPer loses on FSQ-TKY conventional and is mixed on Geolife, so 'consistently outperforms' is not accurate. The body is more careful, but the abstract and conclusion are not.

Bottom line: as a conventional embedding comparison, the paper is useful and mostly sound. As a demonstration of generalization to unseen locations, it does not yet establish the claim. I would not cite the inductive numbers until the protocol is fixed or the claim is reframed. For peer review, I'd send it out — the idea is timely and the benchmark is valuable — but I'd expect substantial revision: exclude the held-out POIs from CaLLiPer's pretraining, specify how baselines handle out-of-vocabulary locations, and tone down the abstract.

Referee Report

3 major / 5 minor

Summary. The paper proposes using CaLLiPer, a contrastive location encoder pre-trained on POI coordinates and textual descriptions, as an inductive location embedding for individual next-location prediction. On four public mobility datasets, it compares CaLLiPer with six baselines under conventional and inductive train/validation/test splits, using MHSA as a common downstream predictor, and reports Acc@1/5/10, MRR, and nDCG@10 in Table 2, together with UMAP visualisations. The abstract claims consistent superiority, with a particular advantage in inductive scenarios involving previously unseen locations, and the authors release code and data for reproducibility.

Significance. If the central claim were established, this would be a useful contribution: it would be the first application of a multimodal, spatially explicit and semantically informed location encoder to individual mobility prediction, and it would offer a practical way to handle emerging locations without retraining lookup-table embeddings. The manuscript is transparent about releasing code and data, and the evaluation uses public datasets with a common downstream model. However, the inductive protocol is currently underspecified in a way that directly affects the validity of the main claim, and the abstract overstates the results reported in Table 2. The significance is therefore conditional on a corrected, equal-information inductive evaluation.

major comments (3)

[4.3 (with 4.1 and A.1)] The inductive setting does not enforce an equal-information condition. Section 4.3 removes mobility sequences containing L_new from the train/validation sets and states that locations in L_new are 'not seen during neither the pre-training phase nor the downstream model training phase.' However, CaLLiPer's pre-training is not on mobility sequences but on POI coordinates and textual descriptions (Sections 3.3.1 and A.1). For FSQ-NYC and FSQ-TKY the locations are the dataset's own POIs, and Section 4.1 states that no additional POI sourcing is needed because these datasets already include POI information; for Gowalla-LD and Geolife, external Foursquare POI data covering London and Beijing are used for CaLLiPer pre-training. The paper never states that the POIs corresponding to L_new are removed from CaLLiPer's contrastive pre-training set. If they are not removed, CaLLiPer has already seen the coordinates and textual descriptions of the supposedly unseen test locations, and the inductive comparison measures data leakage rather than generalisation.
[4.3 (with Eqs. 4-5)] The paper does not explain how the non-inductive baselines obtain embeddings for locations in L_new. Vanilla-E2E, Skip-gram, POI2Vec, Geo-Teaser, TALE, and CTLE all rely on a fixed vocabulary learned from the training sequences; after the inductive filtering, no sequence containing an L_new location remains, so these methods have no embedding for those locations. The downstream model's output layer, defined in Eq. (4) and trained with Eq. (5), has one logit per location, and the paper does not specify whether L_new are in the output space for the baselines, whether their embeddings are randomly initialised, or whether the final layer is truncated. Without this information, the inductive columns of Table 2 are difficult to interpret as a comparison of generalisation ability.
[Abstract and Section 5.1 / Table 2] The abstract's claim that CaLLiPer 'consistently outperforms strong baselines' and 'particularly excelling in inductive scenarios' is not supported by Table 2. On FSQ-TKY conventional, the relative differences are negative for all five metrics (Acc@1 -8.31%, Acc@5 -1.09%, Acc@10 -0.30%, MRR -3.52%, nDCG@10 -2.47%); on Geolife, Acc@5 and Acc@10 are negative in both conventional and inductive settings. The more careful wording in Section 5.1, which says CaLLiPer has the best result in the majority of cases and performs well on a subset of metrics in the remaining datasets, is accurate, but it should replace the abstract's overclaim.

minor comments (5)

[4.3] The sentence 'not seen during neither the pre-training phase nor the downstream model training phase' should read 'not seen during either the pre-training phase or the downstream model training phase.'
[Figures 4 and 5] Both figure captions contain the typo 'conventinoal setting,' which should be corrected to 'conventional setting.'
[4.1] The dataset enumeration lists only FSQ-NYC, Gowalla-LD, and Geolife, but Table 1 and the rest of the paper also use FSQ-TKY; the fourth dataset should be named explicitly in the text.
[5.1] The phrase 'significantly outperforms' is used without reporting significance tests; several key comparisons show overlapping standard deviations, for example FSQ-NYC inductive Acc@1 with CaLLiPer 16.07 ± 0.69 versus Vanilla-E2E 15.25 ± 1.04, so either significance testing or explicit wording that the differences are not tested would be appropriate.
[A.1] Appendix A.1 reports dataset-specific hyperparameters for CaLLiPer, but the accompanying text should be cross-checked for FSQ-TKY, which is omitted from the dataset list in Section 4.1.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation: downstream accuracy is an independent external benchmark; self-citation to CaLLiPer is context, not the load-bearing argument.

full rationale

The paper's central claim is that CaLLiPer embeddings, pre-trained by contrastive alignment of coordinates and POI text, improve next-location prediction when plugged into a downstream MHSA model. The target metrics (Acc@k, MRR, nDCG@10) are computed on held-out mobility sequences and are not used to fit any CaLLiPer parameter: Section 3.3 defines the pretraining objective as InfoNCE over POI pairs, Section 4 lists the downstream model and metrics, and the embeddings are frozen during downstream training. No equation in the paper reduces the prediction output to the pretraining objective by construction. The only self-citation is reference [34] for the CaLLiPer architecture and its prior urban-characterisation result; that citation is not the evidence for the empirical comparison here, which stands on Table 2 against external baselines. The inductive-setting concern in Section 4.3 — that L_new sequences are removed from mobility training/validation but CaLLiPer's POI pretraining set is not explicitly filtered — is a potential data-leakage/fairness problem affecting the validity of the inductive comparison, not a circular definition or a fitted input renamed as a prediction. Under the defined circularity patterns, no specific reduction can be exhibited, so the appropriate finding is no significant circularity.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new theoretical entities. Its free parameters are the CaLLiPer coordinate-encoding scales, which are standard hyperparameters of the reused model. The inductive protocol rests on the unverified assumption that the held-out locations are truly unseen by all methods, including the POI-based pre-training.

free parameters (2)

CaLLiPer Grid scale radii r_min and r_max = 0.01 and 10 for FSQ-NYC, FSQ-TKY, and Geolife; 1 and 1000 for Gowalla-LD
Hand-chosen per dataset in Appendix A.1 to match coordinate scale; affects the multi-scale coordinate encoding and therefore the resulting embeddings.
Inductive sample fraction = 10%
Arbitrary choice in Section 4.3 for simulating unseen locations; the paper does not test sensitivity to this value.

assumptions (3)

domain assumption POI textual descriptions capture the semantic function of a location relevant to mobility behavior
Central premise for using CaLLiPer; the paper does not directly test whether POI semantics are the cause of the observed gains.
ad hoc to paper The inductive split makes test locations unseen for all embedding methods
Section 4.3 assumes that removing mobility sequences containing L_new yields a valid inductive test, but does not state how baselines obtain embeddings for L_new or whether CaLLiPer's POI pre-training excluded L_new.
domain assumption MHSA is a representative and fair downstream predictor
Section 3.3.2 fixes a single Transformer-based downstream model; results might not transfer to other predictors, and the comparisons are only valid under this common backbone.

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Cite this review

Pith. "Pith review of Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places." pith.science (2026). https://pith.science/paper/ZEZ37FVL

@misc{pith2026250614070,
  author       = {Pith},
  title        = {Pith review of: Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZEZ37FVL}},
  note         = {Machine review of arXiv:2506.14070}
}

read the original abstract

Predicting individuals' next locations is a core task in human mobility modelling, with wide-ranging implications for urban planning, transportation, public policy and personalised mobility services. Traditional approaches largely depend on location embeddings learned from historical mobility patterns, limiting their ability to encode explicit spatial information, integrate rich urban semantic context, and accommodate previously unseen locations. To address these challenges, we explore the application of CaLLiPer -- a multimodal representation learning framework that fuses spatial coordinates and semantic features of points of interest through contrastive learning -- for location embedding in individual mobility prediction. CaLLiPer's embeddings are spatially explicit, semantically enriched, and inductive by design, enabling robust prediction performance even in scenarios involving emerging locations. Through extensive experiments on four public mobility datasets under both conventional and inductive settings, we demonstrate that CaLLiPer consistently outperforms strong baselines, particularly excelling in inductive scenarios. Our findings highlight the potential of multimodal, inductive location embeddings to advance the capabilities of human mobility prediction systems. We also release the code and data (https://github.com/xlwang233/Into-the-Unknown) to foster reproducibility and future research.

Figures

Figures reproduced from arXiv: 2506.14070 by the authors.

**Figure 2.** The model framework of CaLLiPer. 𝑓 𝑃 Θ𝑃 (𝑓 𝑇 (𝑥 𝑇 )) = 𝑧 𝑇 ∈ R 𝑁 ×𝑑 (2) The parameters of the location encoder and the projection layer, i.e., Θ 𝐿 and Θ 𝑃 , are optimised, while the text encoder is frozen and do not receive parameter update. The simple yet highly effective bi-directional InfoNCE is adopted as the training objective [30]: 𝑂𝑏 𝑗(Θ 𝐿 , Θ 𝑃 ) = − 1 2𝑁 ∑︁ 𝑁 𝑖=1 log exp(𝑧 𝐿 𝑖 𝑧 𝑇 𝑖 /𝜏) Í𝑁 𝑗=1 exp(𝑧 𝐿 𝑖 𝑧 … view at source ↗

**Figure 3.** The illustration of the process of constructing train, [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: 2D visualisation of the embedding vectors pre-trained under [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

**Figure 5.** Figure 5: 2D visualisation of the embedding vectors pre-trained under [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

Discussion (0). Continue with ORCID to comment.

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

Hugo Barbosa, Marc Barthelemy, Gourab Ghoshal, Charlotte R James, Maxime Lenormand, Thomas Louail, Ronaldo Menezes, José J Ramasco, Filippo Simini, and Marcello Tomasini. 2018. Human mobility: Models and applications. Physics Reports 734 (2018), 1–74

2018

[2] [2]

Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence 35, 8 (2013), 1798–1828

2013

[3] [3]

Maike Buchin, Somayeh Dodge, and Bettina Speckmann. 2012. Context-aware similarity of trajectories. In Geographic Information Science: 7th International Conference, GIScience 2012, Columbus, OH, USA, September 18-21, 2012. Proceedings

work page 2012

[4] [4]

Eunjoon Cho, Seth A Myers, and Jure Leskovec. 2011. Friendship and mobility: user movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining . 1082–1090

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