Towards Conversational Recommender Systems
published: Sept. 27, 2016, recorded: August 2016, views: 2112
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
People often ask others for restaurant recommendations as a way to discover new dining experiences. This makes restaurant recommendation an exciting scenario for recommender systems and has led to substantial research in this area. However, most such systems behave very differently from a human when asked for a recommendation. The goal of this paper is to begin to reduce this gap.
In particular, humans can quickly establish preferences when asked to make a recommendation for someone they do not know. We address this cold-start recommendation problem in an online learning setting. We develop a preference elicitation framework to identify which questions to ask a new user to quickly learn their preferences. Taking advantage of latent structure in the recommendation space using a probabilistic latent factor model, our experiments with both synthetic and real world data compare different types of feedback and question selection strategies. We ﬁnd that our framework can make very effective use of online user feedback, improving personalized recommendations over a static model by 25% after asking only 2 questions. Our results demonstrate dramatic beneﬁts of starting from ofﬂine embeddings, and highlight the beneﬁt of bandit-based explore-exploit strategies in this setting.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !