You Sound Like Someone Who Watches Drama Movies: Towards Predicting Movie Preferences from Conversational Interactions (2021)

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.

## introduction and background with the increasing popularity of voice-assistants, there has been a lot of research on making wellestablished user experiences, like recommendations, conversational @xcite @xcite . one such area is movie recommendation. movie recommendation in general has been an actively researched area in conversational recommendation systems @xcite and has been explored with a variety of approaches, most popular of which include collaborative filtering (cf) @xcite , content-based filtering @xcite , incorporating user reviews @xcite . several attempts at both conversational recommendation @xcite @xcite , and specifically conversational movie recommendations @xcite have been made. however, establishing the new user's preferences through a conversation, in order to make an effective recommendation, remains an open question, and there exists little conversational data for such a task. in our initial exploration, we examine if current well-researched approaches and algorithms can be adapted and used to help solve this problem and to establish a baseline of such approaches for future improvement and reference. we explore a new approach to conversational recommendation by incorporating preferences of other, external users with established preferences, via shared discussed entities, and the user's sentiment towards them, which also addresses the resulting "cold start" problem. in this setting, users do not ask for recommendations directly, but rather have a more natural conversation with a wizard, and receive recommendations based on this discussion. previous approaches such as the "hierarchy of recommendation goals" @xcite and "narrative-driven recommendation" @xcite are not applicable under these conditions. instead, we propose a novel knowledge-aware conversational recommendation approach, which combines conversational context with external knowledge, such as movie reviews, to predict users' ratings of unseen movies. to this end, we extended and refined an existing conversational dataset @xcite to make it amenable to perform experiments in conversational recom-mendation. a short snippet of one of the available conversations can be seen in the middle left box of figure 1 . the original dataset was created via an mturk experiment, where two workers (one playing the wizard and another playing the user) were asked to discuss several movies. the wizard is "coached" to ask the most informative questions, prompting the user to express their opinions towards a mentioned movie. we extended this dataset to include entity annotations linked to rottentomatoes foot_0 , and fine-grained user sentiment labels (details in section 3.1). we are making this dataset (moviesent ) publicly available. next, section 2 states the problem formally and describes our approach; section 3 provides details of the models evaluated, and construction of the moviesent dataset; section 4 outlines results and states the conclusions of our work, section 5 discusses the future work. ## experimental setup baselines: first, we establish natural baselines: ## results and discussion results for all discussed models are reported in we explored the problem of conversational movie recommendation, to take advantage of the vast amounts of external user-generated content on the web, such as movie reviews, to improve the recommendation quality. as a first critical step in that direction, we focused on estimating a user's preference for an unseen movie based on estimated sentiment towards other movies mentioned in the conversation. specifically, we applied sentiment analysis models to infer a user's sentiment towards movies mentioned in a conversation. these sentiment scores were used as a proxy for a user's explicit ratings which could be used by traditional collaborative filtering algorithms, applied to extensive external data of movie reviews and ratings. our second insight was that the actual conversation content could provide additional benefit in representing the user's interests, for improved recommendation quality. our results demonstrated that incorporating conversation content to select a more similar group of users for collaborative filtering, improves the recommendation performance, compared to using the inferred ratings alone. to advance research in this area, and for full transparency and reproducibility, the labeled conversation dataset and the code to retrieve the external review data are available on github 7 . together, our contributed dataset and experiments, and the resulting insights, offer a promising direction for improving conversational recommendation systems through augmentation with external data. ## future work this work was only a first step, with many potential areas for further improvement. the baseline comparison could be improved: adapting and using more sophisticated methods, like neural matrix factorization @xcite , or wide and deep learning models @xcite would make for a better baseline, and it might also improve the performance of our approach as well, as the cf score has one of the highest feature importance in our model. another direction is trying content-based approaches, which were not used in the current paper due to scarcity of data for each conversational user. we also observed that our model was biased against predicting lower ratings since the conversations tend to focus on movies that a user liked. in future work we plan to explore correcting for this positive bias and other extensions to predicting user sentiment from a conversation more robustly. while our initial attempts to represent the conversation content improved the prediction accuracy, a fruitful direction of research is improving the representation of the conversation content for recommendations (i.e., for retrieving similar reviewers). in section 2.1 we mention k-turn conversations and mention of m movies. so a natural direction for research would be analysis of recommendation 7 https://github.com/sergey-volokhin/ conversational-movies accuracy depending on the length of the conversation (number of turns), on the number of mentioned movies (does more movies equal better quality?), and on the ratio of sentiment bearing statements in a conversation (how many are factual/neutral?). as additional conversational data becomes available, our approach could be extended to include other sources of user preferences such as twitter/reddit-based conversations, and actual past conversations of other users with a recommender bot.