Generative Models for Cold-Start Recommendations

Systems for automatically recommending items (e.g., movies, products, or information) to users are becoming increasingly important in e-commerce applications, digital libraries, and other domains where personalization is highly valued. Such recommender systems typically base their suggestions on (1) collaborative data encoding which users like which items, and/or (2) content data describing item features and user demographics. Systems that rely solely on collaborative data fail when operating from a cold start|that is, when recommending items (e.g., rst-run movies) that no member of the community has yet seen. We develop several generative probabilistic models that circumvent the cold-start problem by mixing content data with collaborative data in a sound statistical manner. We evaluate the algorithms using MovieLens movie ratings data, augmented with actor and director information from the Internet Movie Database. We nd that maximum likelihood learning with the expectation maximization (EM) algorithm and variants tends to over t complex models that are initialized randomly. However, by seeding parameters of the complex models with parameters learned in simpler models, we obtain greatly improved performance. We explore both methods that exploit a single type of content data (e.g., actors only) and methods that leverage multiple types of content data (e.g., both actors and directors) simultaneously.