Please use this identifier to cite or link to this item: https://doi.org/10.1145/3397271.3401419
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dc.titleConversational Recommendation: Formulation, Methods, and Evaluation
dc.contributor.authorWenqiang Lei
dc.contributor.authorXiangnan He
dc.contributor.authorMaarten de Rijke
dc.contributor.authorTat-Seng Chua
dc.date.accessioned2020-10-21T04:04:03Z
dc.date.available2020-10-21T04:04:03Z
dc.date.issued2020-07-25
dc.identifier.citationWenqiang Lei, Xiangnan He, Maarten de Rijke, Tat-Seng Chua (2020-07-25). Conversational Recommendation: Formulation, Methods, and Evaluation. SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval : 2425 - 2428. ScholarBank@NUS Repository. https://doi.org/10.1145/3397271.3401419
dc.identifier.isbn9781450000000
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/178643
dc.description.abstractRecommender systems have demonstrated great success in information seeking. However, traditional recommender systems work in a static way, estimating user preferences on items from past interaction history. This prevents recommender systems from capturing dynamic and fine-grained preferences of users. Conversational recommender systems bring a revolution to existing recommender systems. They are able to communicate with users through natural languages during which they can explicitly ask whether a user likes an attribute or not. With the preferred attributes, a recommender system can conduct more accurate and personalized recommendations. Therefore, while they are still a relatively new topic, conversational recommender systems attract great research attention. We identify four emerging directions: (1) exploration and exploitation trade-off in the cold-start recommendation setting; (2) attribute-centric conversational recommendation; (3) strategy-focused conversational recommendation; and (4) dialogue understanding and response generation. This tutorial covers these four directions, providing a review of existing approaches and progress on the topic. By presenting the emerging and promising topic of conversational recommender systems, we aim to provide take-aways to practitioners to build their own systems. We also want to stimulate more ideas and discussions with audiences on core problems of this topic such as task formalization, dataset collection, algorithm development, and evaluation, with the ambition of facilitating the development of conversational recommender systems. © 2020 Owner/Author.
dc.publisherAssociation for Computing Machinery, Inc
dc.subjectConversational recommendation
dc.subjectDialog system
dc.subjectInteractive recommendation
dc.typeConference Paper
dc.contributor.departmentDEPT OF COMPUTER SCIENCE
dc.description.doi10.1145/3397271.3401419
dc.description.sourcetitleSIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
dc.description.page2425 - 2428
dc.grant.idR-252-300-002-490
dc.grant.fundingagencyInfocomm Media Development Authority
dc.grant.fundingagencyNational Research Foundation
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