Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling | Paper Citation Record

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This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Sinkhorn distances: Lightspeed computation of optimal transport

Reference 1

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This paper cites Moshi: a speech-text foundation model for real-time dialogue.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Moshi: a speech-text foundation model for real-time dialogue

Reference 2

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This paper cites A review of modern recommender systems using generative models (gen-recsys).

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling A review of modern recommender systems using generative models (gen-recsys)

Reference 3

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This paper cites Recommender forest for efficient retrieval.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Recommender forest for efficient retrieval

Reference 4

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This paper cites Recommendation as language processing (RLP): A unified pretrain, personalized prompt & predict paradigm (P5).

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Recommendation as language processing (RLP): A unified pretrain, personalized prompt & predict paradigm (P5)

Reference 5

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This paper cites InProceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (2016).

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling InProceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (2016)

Reference 6

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This paper cites A survey on user behavior modeling in recommender systems.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling A survey on user behavior modeling in recommender systems

Reference 7

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Observation 376b56a3-2e5a-462b-8163-df27a2deb5bb · outbound

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

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Session-based Recommendations with Recurrent Neural Networks

Reference 8

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Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Unresolved cited work

Reference 9

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This paper cites Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders

Reference 10

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This paper cites In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (2023).

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (2023)

Reference 11

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This paper cites Billion-scale similarity search with GPUs.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Billion-scale similarity search with GPUs

Reference 12

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Observation dff4fcb2-91e6-447b-beba-44bed946c99e · outbound

This paper cites Self-attentive sequential recommendation.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Self-attentive sequential recommendation

Reference 13

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This paper cites Scaling Laws for Neural Language Models.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Scaling Laws for Neural Language Models

Reference 14

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This paper cites Turning dross into gold loss: is bert4rec really better than sasrec? In Proceedings of the 17th ACM Conference on Recommender Systems (2023).

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Turning dross into gold loss: is bert4rec really better than sasrec? In Proceedings of the 17th ACM Conference on Recommender Systems (2023)

Reference 15

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This paper cites Autoregressive image gener- ation using residual quantization.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Autoregressive image gener- ation using residual quantization

Reference 16

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This paper cites Text is all you need: Learning language representations for sequential recommendation.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Text is all you need: Learning language representations for sequential recommendation

Reference 17

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This paper cites Embedding optimization for training large-scale deep learning recommendation systems with embark.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Embedding optimization for training large-scale deep learning recommendation systems with embark

Reference 18

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This paper cites Hierarchical gating networks for sequential recom- mendation.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Hierarchical gating networks for sequential recom- mendation

Reference 19

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This paper cites Image-based recom- mendations on styles and substitutes.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Image-based recom- mendations on styles and substitutes

Reference 20

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This paper cites In Findings of the Association for Computational Linguistics (2022).

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling In Findings of the Association for Computational Linguistics (2022)

Reference 21

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This paper cites Representation Learning with Contrastive Predictive Coding.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Representation Learning with Contrastive Predictive Coding

Reference 22

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This paper cites TIGER reviewing process.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling TIGER reviewing process

Reference 23

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This paper cites H., Vu, T., Heldt, L., Hong, L., Tay, Y., Tran, V.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling H., Vu, T., Heldt, L., Hong, L., Tay, Y., Tran, V

Reference 24

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This paper cites Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy

Reference 25

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This paper cites Better generalization with semantic ids: A case study in ranking for recommendations.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Better generalization with semantic ids: A case study in ranking for recommendations

Reference 26

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This paper cites Improved Deep Metric Learning with Multi-class N-pair Loss Objective.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Improved Deep Metric Learning with Multi-class N-pair Loss Objective

Reference 27

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This paper cites In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (2019).

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (2019)

Reference 28

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This paper cites In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (2018).

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (2018)

Reference 29

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This paper cites Q., Dehghani, M., Ni, J., Bahri, D., Mehta, H., Qin, Z., Hui, K., Zhao, Z., Gupta, J., Schuster, T., Cohen, W.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Q., Dehghani, M., Ni, J., Bahri, D., Mehta, H., Qin, Z., Hui, K., Zhao, Z., Gupta, J., Schuster, T., Cohen, W

Reference 30

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Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Neural discrete representation learning

Reference 31

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This paper cites N., Kaiser, Ł., and Polosukhin, I.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling N., Kaiser, Ł., and Polosukhin, I

Reference 32

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This paper cites Learnable item tokenization for generative recommendation.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Learnable item tokenization for generative recommendation

Reference 33

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This paper cites Eager: Two-stream generative recommender with behavior- semantic collaboration.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Eager: Two-stream generative recommender with behavior- semantic collaboration

Reference 34

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This paper cites A survey on large language models for recommendation.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling A survey on large language models for recommendation

Reference 35

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This paper cites Session-based recom- mendation with graph neural networks.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Session-based recom- mendation with graph neural networks

Reference 36

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This paper cites Uniaudio: An audio foundation model toward universal audio generation.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Uniaudio: An audio foundation model toward universal audio generation

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This paper cites Soundstream: An end-to-end neural audio codec, 2021.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Soundstream: An end-to-end neural audio codec, 2021

Reference 38

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This paper cites In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2023).

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2023)

Reference 39

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This paper cites Actions speak louder than words: trillion-parameter sequential transducers for generative recommendations.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Actions speak louder than words: trillion-parameter sequential transducers for generative recommendations

Reference 40

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This paper cites Multi- modal quantitative language for generative recommendation.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Multi- modal quantitative language for generative recommendation

Reference 41

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This paper cites Sigmoid loss for language image pre-training.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Sigmoid loss for language image pre-training

Reference 42

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This paper cites X., and Wen, J.-R.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling X., and Wen, J.-R

Reference 43

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This paper cites S., Xu, J., W ang, D., Liu, G., and Zhou, X.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling S., Xu, J., W ang, D., Liu, G., and Zhou, X

Reference 44

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This paper cites Recommender systems in the era of large language models (llms).

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Recommender systems in the era of large language models (llms)

Reference 45

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This paper cites X., Chen, M., and Wen, J.-R.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling X., Chen, M., and Wen, J.-R

Reference 46

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This paper cites X., Zhu, Y., W ang, S., Zhang, F., W ang, Z., and Wen, J.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling X., Zhu, Y., W ang, S., Zhang, F., W ang, Z., and Wen, J

Reference 47

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This paper cites Learning tree-based deep model for recommender systems.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Learning tree-based deep model for recommender systems

Reference 48

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Observation 593661d6-9df6-4b58-8cd6-9d6c497d85c9 · outbound

This paper cites CoST: Contrastive quantiza- tion based semantic tokenization for generative recommendation.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling CoST: Contrastive quantiza- tion based semantic tokenization for generative recommendation

Reference 49

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Observation a466bb6e-2975-40c6-b528-4fc94e302246 · outbound

This paper cites Generative pre-trained speech language model with efficient hierarchical transformer.

Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling Generative pre-trained speech language model with efficient hierarchical transformer

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