When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs | Paper Citation Record

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs LLaMA: Open and Efficient Foundation Language Models

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This paper cites Training language models to follow instructions with human feedback.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Training language models to follow instructions with human feedback

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This paper cites A Comprehensive Overview of Large Language Models.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs A Comprehensive Overview of Large Language Models

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This paper cites Recommender systems: An overview.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Recommender systems: An overview

Reference 4

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This paper cites Self- Attentive Sequential Recommendation.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Self- Attentive Sequential Recommendation

Reference 5

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This paper cites Llara: Large language- recommendation assistant.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Llara: Large language- recommendation assistant

Reference 6

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This paper cites BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer

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This paper cites Self-supervised Learning for Large-scale Item Recommendations.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Self-supervised Learning for Large-scale Item Recommendations

Reference 8

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This paper cites S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization

Reference 9

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This paper cites Recommender Systems in the Era of Large Language Models (LLM4Rec).

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Recommender Systems in the Era of Large Language Models (LLM4Rec)

Reference 10

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This paper cites Tallrec: An effective and effi- cient tuning framework to align large language model with recommendation.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Tallrec: An effective and effi- cient tuning framework to align large language model with recommendation

Reference 11

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This paper cites Aggarwal.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Aggarwal

Reference 12

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This paper cites Sequential Recommender Systems: Challenges, Progress, and Prospects.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Sequential Recommender Systems: Challenges, Progress, and Prospects

Reference 13

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This paper cites Recommender Systems Handbook.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Recommender Systems Handbook

Reference 14

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This paper cites Markov Chain Recommendation System (MCRS).

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Markov Chain Recommendation System (MCRS)

Reference 15

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This paper cites Session-based Recommendations with Recurrent Neural Networks.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Session-based Recommendations with Recurrent Neural Networks

Reference 16

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This paper cites Attention is all you need.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Attention is all you need

Reference 17

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This paper cites Transformers4Rec: Bridging the Gap Between NLP and Sequential Recommendation.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Transformers4Rec: Bridging the Gap Between NLP and Sequential Recommendation

Reference 18

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This paper cites The Application of Large Language Models in Recommendation Systems.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs The Application of Large Language Models in Recommendation Systems

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This paper cites Large language models are competitive near cold-start recommenders for language-and item-based preferences.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Large language models are competitive near cold-start recommenders for language-and item-based preferences

Reference 20

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This paper cites Harnessing the power of llms in practice: A survey on chatgpt and beyond.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Harnessing the power of llms in practice: A survey on chatgpt and beyond

Reference 21

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This paper cites Towards Semantic Equivalence of Tokenization in Multimodal LLM.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Towards Semantic Equivalence of Tokenization in Multimodal LLM

Reference 22

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Neural network methods for natural language processing

Reference 23

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This paper cites Quick start guide to large lan- guage models: strategies and best practices for us- ing ChatGPT and other LLMs.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Quick start guide to large lan- guage models: strategies and best practices for us- ing ChatGPT and other LLMs

Reference 24

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This paper cites Full Parameter Fine-tuning for Large Language Models with Limited Resources.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Full Parameter Fine-tuning for Large Language Models with Limited Resources

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Lora: Low-rank adaptation of large language models

Reference 26

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Lecture Slides on Transformers

Reference 27

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This paper cites A comprehensive recommender system model: Improving accuracy for both warm and cold start users.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs A comprehensive recommender system model: Improving accuracy for both warm and cold start users

Reference 28

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs A system- atic review and taxonomy of explanations in decision support and recommender systems

Reference 29

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This paper cites A systematic review of explainable artificial intelligence in terms of different application domains and tasks.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs A systematic review of explainable artificial intelligence in terms of different application domains and tasks

Reference 30

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This paper cites How Can Recommender Systems Benefit from Large Language Models: A Survey.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs How Can Recommender Systems Benefit from Large Language Models: A Survey

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Language models are few- shot learners

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Improving Sequential Recommendations with LLMs

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Adapting large language models by integrating collaborative semantics for recommendation

Reference 36

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Empowering news recom- mendation with pre-trained language models

Reference 37

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Request for the limitations of training all the components of a model

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Cost-Sensitive Learning for Predictive Maintenance

Reference 39

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs MovieLens 1M Dataset

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Amazon Review Data (2018 and ear- lier)

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs TinyLlama: An Open-Source Small Language Model

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Trustworthy recom- mender systems

Reference 43

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Matrix factorization model in collaborative filtering algorithms: A survey

Reference 44

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Neural collaborative fil- tering

Reference 45

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs An MDP-based recommender sys- tem

Reference 46

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This paper cites Receiver operating characteristic (ROC) area under the curve (AUC): A diagnostic measure for evalu- ating the accuracy of predictors of education outcomes.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Receiver operating characteristic (ROC) area under the curve (AUC): A diagnostic measure for evalu- ating the accuracy of predictors of education outcomes

Reference 47

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This paper cites The uniform AUC: Dealing with the representativeness effect in presence–absence models.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs The uniform AUC: Dealing with the representativeness effect in presence–absence models

Reference 48

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This paper cites Self-Attentive Sequential Recommendation.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Self-Attentive Sequential Recommendation

Reference 206

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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Unresolved cited work

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Observation 421f3b38-ae27-4fe5-ae79-7791119547da · outbound

This paper cites an unresolved cited work.

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:22:31.187691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 23b353a8-a40e-4943-a86a-37327b0b07a1 · outbound

This paper cites arXiv: 2105.12853 [cs.IR].

When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs arXiv: 2105.12853 [cs.IR]

Reference 2034

Resolution: unresolved; no resolver link, observed 2026-08-06T19:22:28.657359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers