Research Area: Machine Learning
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.
Keywords:
Conversational Recommender Systems
Restaurant Recommendations
Recommendation space
Offline embeddings
Author(s) Name: Filip Radlinski , Katja Hofmann
Journal name:
Conferrence name: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher name: ACM Library
DOI: 10.1145/2939672.2939746
Volume Information:
