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Recommender systems perform well for popular items and users with ample interactions (likes, ratings etc.).
Zhang, Y., Ai, Q., Chen, X., Croft, W.B.: Joint representation learning for top-n recommendation with heterogeneous information sources. In: CIKM ’17. ACM (2017)
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Chen, C., Zhang, M., Liu, Y., Ma, S.: Neural attentional rating regression with review-level explanations. In: WWW ’18. ACM (2018)
2018
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Lu, Y., Dong, R., Smyth, B.: Coevolutionary recommendation model: Mutual learning between ratings and reviews. In: WWW ’18. ACM (2018)
2018
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Shalom, O.S., Uziel, G., Karatzoglou, A., Kantor, A.: A word is worth a thousand ratings: Augmenting ratings using reviews for collaborative filtering. In: SIGIR ’18 (2018)
2018
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Wan, M., McAuley, J.: Item recommendation on monotonic behavior chains. In: RecSys ’18 (2018)
2018
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Hu, G., Zhang, Y., Yang, Q.: Transfer meets hybrid: A synthetic approach for cross-domain collaborative filtering with text. In: WWW ’19. ACM (2019)
2019
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Li, J., Jing, M., Lu, K., Zhu, L., Yang, Y., Huang, Z.: From zero-shot learning to cold-start recommendation. In: AAAI ’19 (2019)
2019
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Liu, D., Li, J., Du, B., Chang, J., Gao, R.: DAML: dual attention mutual learning between ratings and reviews for item recommendation. In: KDD ’19. ACM (2019)
2019
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Ni, J., Li, J., McAuley, J.: Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In: EMNLP-IJCNLP ’19 (2019)
2019
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Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. In: EMNLP-IJCNLP ’19. ACL (2019)
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Shalom, O.S., Uziel, G., Kantor, A.: A generative model for review-based recommendations. In: RecSys ’19. ACM (2019)
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Wu, C., Wu, F., Ge, S., Qi, T., Huang, Y., Xie, X.: Neural news recommendation with multi-head self-attention. In: EMNLP-IJCNLP ’19 (2019)
2019
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Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: A survey and new perspectives. ACM Comput. Surv. (2019)
2019
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Bekker, J., Davis, J.: Learning from positive and unlabeled data: a survey. Mach. Learn. 109
2020
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Cao, E., Wang, D., Huang, J., Hu, W.: Open knowledge enrichment for long-tail entities. In: WWW ’20. ACM / IW3C2 (2020)
2020
Cited alongside, same era.
Liu, H., Wang, Y., Peng, Q., Wu, F., Gan, L., Pan, L., Jiao, P.: Hybrid neural recommendation with joint deep representation learning of ratings and reviews. Neurocomputing (2020)
2020
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Peña, F.J., O’Reilly-Morgan, D., Tragos, E.Z., Hurley, N., Duriakova, E., Smyth, B., Lawlor, A.: Combining rating and review data by initializing latent factor models with topic models for top-n recommendation. In: RecSys ’20. ACM (2020)
2020
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Penha, G., Hauff, C.: What does BERT know about books, movies and music? Probing BERT for conversational recommendation. In: RecSys ’20. ACM (2020)
2020
Cited alongside, same era.
Pugoy, R.A., Kao, H.Y.: BERT-based neural collaborative filtering and fixed-length contiguous tokens explanation. In: ACL ’20 (2020)
Liu, B., Bai, B., Xie, W., Guo, Y., Chen, H.: Task-optimized user clustering based on mobile app usage for cold-start recommendations. In: KDD ’22. ACM (2022)
2022
Later among the works it cites.
McAuley, J.: Recommender Systems and Personalization Datasets (2022), https://cseweb.ucsd.edu/˜jmcauley/datasets.html, , accessed on Sep 29, 2022
2022
Later among the works it cites.
Ricci, F., Rokach, L., Shapira, B. (eds.): Recommender Systems Handbook. Springer US (2022)
2022
Later among the works it cites.
Shuai, J., Zhang, K., Wu, L., Sun, P., Hong, R., Wang, M., Li, Y.: A review-aware graph contrastive learning framework for recommendation. In: SIGIR ’22. ACM (2022)
2022
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2020
Cited alongside, same era.
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research (2020)
2020
Cited alongside, same era.
Sachdeva, N., McAuley, J.J.: How useful are reviews for recommendation? A critical review and potential improvements. In: SIGIR ’20. ACM (2020)
2020
Cited alongside, same era.
Pugoy, R.A., Kao, H.: Unsupervised extractive summarization-based representations for accurate and explainable collaborative filtering. In: ACL/IJCNLP ’21. ACL (2021)
2021
Cited alongside, same era.
Raziperchikolaei, R., Liang, G., Chung, Y.: Shared neural item representations for completely cold start problem. In: RecSys ’21. ACM (2021)
2021
Cited alongside, same era.
Wang, X., Ounis, I., Macdonald, C.: Leveraging review properties for effective recommendation. In: WWW ’21. ACM / IW3C2 (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2023
Closest in time.
Luo, S., Ma, C., Xiao, Y., Song, L.: Improving long-tail item recommendation with graph augmentation. In: CIKM ’23. ACM (2023)
2023
Closest in time.
2023
Closest in time.
2024
Closest in time.
Hou, Y., Zhang, J., Lin, Z., Lu, H., Xie, R., McAuley, J.J., Zhao, W.X.: Large language models are zero-shot rankers for recommender systems. In: ECIR ’24. Springer (2024)
2024
Closest in time.
2024
Closest in time.
Lin, J., Chen, B., Wang, H., Xi, Y., Qu, Y., Dai, X., Zhang, K., Tang, R., Yu, Y., Zhang, W.: Clickprompt: CTR models are strong prompt generators for adapting language models to CTR prediction. In: WWW ’24. ACM (2024)
2024
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Ramos, J., Rahmani, H.A., Wang, X., Fu, X., Lipani, A.: Transparent and scrutable recommendations using natural language user profiles. In: ACL ’24 (2024)
2024
Closest in time.
Torbati, G.H., Tigunova, A., Weikum, G.: SIRUP: search-based book recommendation playground. In: WSDM ’24. ACM (2024)
2024
Closest in time.
Dong, H.V., Fang, Y., Lauw, H.W.: A contrastive framework with user, item and review alignment for recommendation. In: WSDM ’25. ACM (2025)
2025
Closest in time.