Conversational Recommender Systems (CRS) aim to provide personalized and contextualized recommendations through natural language conversations with users. The objective of my proposed dissertation is to capitalize on the recent developments in conversational interfaces to advance the field of Recommender Systems in several directions. I aim to address several problems in recommender systems: user and item representation, justification generation, and data sparsity.

Providing a justification or explanation for a recommendation has been shown to improve the users’ experience with recommender systems, in particular by increasing confidence in the recommendations. However, in order to be effective in a conversational setting, the justifications have to be appropriate for the conversation so far. Previous approaches rely on a user history of reviews and ratings of related items to personalize the recommendation, but this information is not generally available when conversing with a new user, and as such a cold-start problem imposes a challenge in generating suitable justifications. To address this problem, we propose and validate a new method, CONJURE (CONversational JUstificatons for REcommendations) to generate contextually relevant justifications for conversational recommendations. Specifically, we investigate whether the conversation itself can be used effectively to model the user, identify relevant review content from other users, and generate a justification that boosts the user’s confidence in and understanding of the recommendation. To implement CONJURE, we test several novel extensions to prior algorithms, by exploiting an auxiliary corpus of movie reviews to construct the justifications from extracted pieces of those reviews. In particular, we explore different conversation representations and ranking approaches. To evaluate CONJURE, we developed a pairwise crowd task to compare justifications. Our results show large, significant improvements in Efficiency and Transparency metrics over the previous non-contextualized template-based methods. We plan to release our code and an augmented conversation corpus on Github.

Sergey Volokhin, Joyce Ho, Oleg Rokhlenko, Eugene Agichtein. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

Why do we listen to music? This question has as many answers as there are people, which may vary by time of day, and the activity of the listener. We envision a contextual music search and recommendation system, which could suggest appropriate music to the user in the current context. As an important step in this direction, we set out to understand what are the users» intents for listening to music, and how they relate to common daily activities. To accomplish this, we conduct and analyze a survey of why and when people of different ages and in different countries listen to music. The resulting categories of common musical intents, and the associations of intents and activities, could be helpful for guiding the development and evaluation of contextual music recommendation systems.

While activity-aware music recommendation has been shown to improve the listener experience, we posit that modeling the \em listening intent can further improve recommendation quality. In this paper, we perform initial exploration of the dominant music listening intents associated with common activities, using music retrieved from popular online music services. We show that these intents can be approximated through audio features of the music itself, and potentially improve recommendation quality. Our initial results, based on 10 common activities and 5 popular listening intents associated with these activities, support our hypothesis, and open a promising direction towards intent-aware contextual music recommendation.