Unlocking the Potential of Large Language Models for Explainable Recommendations

Resnick, P., Varian, H.R.: Recommender systems. Communications of the ACM 40

1997

Earlier work this paper cites.

Bilgic, M., Mooney, R.J.: Explaining recommendations: Satisfaction vs. promotion. In: Beyond personalization workshop, IUI. vol. 5, p. 153 (2005)

2005

Earlier work this paper cites.

Tintarev, N., Masthoff, J.: Designing and evaluating explanations for recommender systems. In: Recommender systems handbook, pp. 479–510. Springer (2010)

2010

Earlier work this paper cites.

Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012)

Original

2012

Earlier work this paper cites.

McAuley, J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM conference on Recommender systems. pp. 165–172 (2013)

2013

Earlier work this paper cites.

Sharma, A., Cosley, D.: Do social explanations work? studying and modeling the effects of social explanations in recommender systems. In: Proceedings of the 22nd international conference on World Wide Web. pp. 1133–1144 (2013)

2013

Earlier work this paper cites.

Diao, Q., Qiu, M., Wu, C.Y., Smola, A.J., Jiang, J., Wang, C.: Jointly modeling aspects, ratings and sentiments for movie recommendation (jmars). In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 193–202 (2014)

2014

Earlier work this paper cites.

Zhang, Y., Lai, G., Zhang, M., Zhang, Y., Liu, Y., Ma, S.: Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. pp. 83–92 (2014)

2014

Earlier work this paper cites.

Harper, F.M., Konstan, J.A.: The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5

2015

Earlier work this paper cites.

McAuley, J., Targett, C., Shi, Q., Van Den Hengel, A.: Image-based recommendations on styles and substitutes. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. pp. 43–52 (2015)

2015

Earlier work this paper cites.

Wong, T.T.: Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern recognition 48

2015

Earlier work this paper cites.

Chen, C., Zhang, M., Liu, Y., Ma, S.: Neural attentional rating regression with review-level explanations. In: Proceedings of the 2018 world wide web conference. pp. 1583–1592 (2018)

2018

Cited alongside, same era.

Chen, X., Zhang, Y., Xu, H., Cao, Y., Qin, Z., Zha, H.: Visually explainable recommendation. arXiv preprint arXiv:1801.10288 (2018)

Original

2018

Cited alongside, same era.

Kang, W.C., McAuley, J.: Self-attentive sequential recommendation. In: 2018 IEEE international conference on data mining (ICDM). pp. 197–206. IEEE (2018)

2018

Cited alongside, same era.

Wang, X., Chen, Y., Yang, J., Wu, L., Wu, Z., Xie, X.: A reinforcement learning framework for explainable recommendation. In: 2018 IEEE international conference on data mining (ICDM). pp. 587–596. IEEE (2018)

2018

Cited alongside, same era.

Cheng, M., Liu, Z., Liu, Q., Ge, S., Chen, E.: Towards automatic discovering of deep hybrid network architecture for sequential recommendation. In: Proceedings of the ACM Web Conference 2022. pp. 1923–1932 (2022)

2022

Later among the works it cites.

Cui, Z., Ma, J., Zhou, C., Zhou, J., Yang, H.: M6-rec: Generative pretrained language models are open-ended recommender systems. arXiv preprint arXiv:2205.08084 (2022)

Original

2022

Later among the works it cites.

Geng, S., Liu, S., Fu, Z., Ge, Y., Zhang, Y.: Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). In: Proceedings of the 16th ACM Conference on Recommender Systems. pp. 299–315 (2022)

2022

Later among the works it cites.

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q.V., Zhou, D., et al.: Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems 35

2022

Later among the works it cites.