{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/you-sound-like-someone-who-watches-drama","title":"You Sound Like Someone Who Watches Drama Movies: Towards Predicting Movie Preferences from Conversational Interactions","arxiv_id":null,"date":"2021-06-01","proceeding":"NAACL 2021 4","authors":["Sergey Volokhin","Joyce Ho","Oleg Rokhlenko","Eugene Agichtein"],"abstract":"The increasing popularity of voice-based personal assistants provides new opportunities for conversational recommendation. One particularly interesting area is movie recommendation, which can benefit from an open-ended interaction with the user, through a natural conversation. We explore one promising direction for conversational recommendation: mapping a conversational user, for whom there is limited or no data available, to most similar external reviewers, whose preferences are known, by representing the conversation as a user{'}s interest vector, and adapting collaborative filtering techniques to estimate the current user{'}s preferences for new movies. We call our proposed method ConvExtr (Conversational Collaborative Filtering using External Data), which 1) infers a user{'}s sentiment towards an entity from the conversation context, and 2) transforms the ratings of {``}similar{''} external reviewers to predict the current user{'}s preferences. We implement these steps by adapting contextual sentiment prediction techniques, and domain adaptation, respectively. To evaluate our method, we develop and make available a finely annotated dataset of movie recommendation conversations, which we call MovieSent. Our results demonstrate that ConvExtr can improve the accuracy of predicting users{'} ratings for new movies by exploiting conversation content and external data.","url_abs":"https://aclanthology.org/2021.naacl-main.246","url_pdf":"https://aclanthology.org/2021.naacl-main.246.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"you-sound-like-someone-who-watches-drama","repo_url":"https://github.com/sergey-volokhin/conversational-movies","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"conversational-recommendation","task_name":"Conversational Recommendation"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"movie-recommendation","task_name":"Movie Recommendation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}