Functional matrix factorizations for cold-start recommendation

A key challenge in recommender system research is how to effectively profile new users, a problem generally known as cold-start recommendation. Recently the idea of progressively querying user responses through an initial interview process has been proposed as a useful new user preference elicitation strategy. In this paper, we present functional matrix factorization (fMF), a novel cold-start recommendation method that solves the problem of initial interview construction within the context of learning user and item profiles. Specifically, fMF constructs a decision tree for the initial interview with each node being an interview question, enabling the recommender to query a user adaptively according to her prior responses. More importantly, we associate latent profiles for each node of the tree — in effect restricting the latent profiles to be a function of possible answers to the interview questions — which allows the profiles to be gradually refined through the interview...

• Type: Journal
• Year: 2011
• Where: SIGIR
• Authors: Ke Zhou, Shuang-Hong Yang, Hongyuan Zha
• Keywords: Decision Tree Construction, Information Technology, Matrix Factorization, Optimization Algorithm, SIGIR 2011