A multiturn recommender system with explanations
Resumo
Recommendations engines use interactions between users and items to predict the preferences of the users and generate recommendations for them. However, because they rely on historical data, the user’s interest at the moment may not be captured. In this context, Conversational Recommender Systems (CRSs) have been proposed in order to provide recommendations that provide suggestions based on the user’s current interests by eliciting information from the user in turns in which the system can ask for the user to understand more about the current interest in the time or recommend. In that regard, we propose CRSs for cold-start users as a Knowledge Graph search. The system also employs a Natural Language Processing module to explain the recommendations, bringing transparency to the recommendation algorithm.
Palavras-chave:
Conversational Recommender Systems, Explainable Recommendation, Multi-turn Recommendation
Referências
Charu C Aggarwal et al. 2016. Recommender systems. Vol. 1. Springer.
Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, and Tat-Seng Chua. 2021. Advances and challenges in conversational recommender systems: A survey. AI Open 2 (2021), 100–126.
Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, and Li Chen. 2021. A survey on conversational recommender systems. ACM Computing Surveys (CSUR) 54, 5 (2021), 1–36.
Wenqiang Lei, Xiangnan He, Maarten de Rijke, and Tat-Seng Chua. 2020. Conversational recommendation: Formulation, methods, and evaluation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2425–2428.
Wenqiang Lei, Xiangnan He, Yisong Miao, QingyunWu, Richang Hong, Min-Yen Kan, and Tat-Seng Chua. 2020. Estimation-action-reflection: Towards deep interaction between conversational and recommender systems. In Proceedings of the 13th International Conference on Web Search and Data Mining. 304–312.
Wenqiang Lei, Gangyi Zhang, Xiangnan He, Yisong Miao, XiangWang, Liang Chen, and Tat-Seng Chua. 2020. Interactive path reasoning on graph for conversational recommendation. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 2073–2083.
Kai Luo, Scott Sanner, Ga Wu, Hanze Li, and Hojin Yang. 2020. Latent linear critiquing for conversational recommender systems. In Proceedings of The Web Conference 2020. 2535–2541.
Anna Sepliarskaia, Julia Kiseleva, Filip Radlinski, and Maarten de Rijke. 2018. Preference elicitation as an optimization problem. In Proceedings of the 12th ACM Conference on Recommender Systems. 172–180.
Yueming Sun and Yi Zhang. 2018. Conversational recommender system. In The 41st international acm sigir conference on research & development in information retrieval. 235–244.
William R Thompson. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25, 3-4 (1933), 285–294.
Ivan Vendrov, Tyler Lu, Qingqing Huang, and Craig Boutilier. 2020. Gradient-based optimization for bayesian preference elicitation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 10292–10301.
Yongfeng Zhang, Xu Chen, et al. 2020. Explainable recommendation: A survey and new perspectives. Foundations and Trends® in Information Retrieval 14, 1 (2020), 1–101.
Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, and Tat-Seng Chua. 2021. Advances and challenges in conversational recommender systems: A survey. AI Open 2 (2021), 100–126.
Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, and Li Chen. 2021. A survey on conversational recommender systems. ACM Computing Surveys (CSUR) 54, 5 (2021), 1–36.
Wenqiang Lei, Xiangnan He, Maarten de Rijke, and Tat-Seng Chua. 2020. Conversational recommendation: Formulation, methods, and evaluation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2425–2428.
Wenqiang Lei, Xiangnan He, Yisong Miao, QingyunWu, Richang Hong, Min-Yen Kan, and Tat-Seng Chua. 2020. Estimation-action-reflection: Towards deep interaction between conversational and recommender systems. In Proceedings of the 13th International Conference on Web Search and Data Mining. 304–312.
Wenqiang Lei, Gangyi Zhang, Xiangnan He, Yisong Miao, XiangWang, Liang Chen, and Tat-Seng Chua. 2020. Interactive path reasoning on graph for conversational recommendation. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 2073–2083.
Kai Luo, Scott Sanner, Ga Wu, Hanze Li, and Hojin Yang. 2020. Latent linear critiquing for conversational recommender systems. In Proceedings of The Web Conference 2020. 2535–2541.
Anna Sepliarskaia, Julia Kiseleva, Filip Radlinski, and Maarten de Rijke. 2018. Preference elicitation as an optimization problem. In Proceedings of the 12th ACM Conference on Recommender Systems. 172–180.
Yueming Sun and Yi Zhang. 2018. Conversational recommender system. In The 41st international acm sigir conference on research & development in information retrieval. 235–244.
William R Thompson. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25, 3-4 (1933), 285–294.
Ivan Vendrov, Tyler Lu, Qingqing Huang, and Craig Boutilier. 2020. Gradient-based optimization for bayesian preference elicitation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 10292–10301.
Yongfeng Zhang, Xu Chen, et al. 2020. Explainable recommendation: A survey and new perspectives. Foundations and Trends® in Information Retrieval 14, 1 (2020), 1–101.
Publicado
23/10/2023
Como Citar
DE SOUZA, Luan Soares; ZANON, André Levi; DE ARAÚJO, Lucas Padilha Modesto; MANZATO, Marcelo Garcia.
A multiturn recommender system with explanations. In: WORKSHOP DE FERRAMENTAS E APLICAÇÕES - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 29. , 2023, Ribeirão Preto/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 77-80.
ISSN 2596-1683.
DOI: https://doi.org/10.5753/webmedia_estendido.2023.234736.
