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SIGIR
2011
ACM

Functional matrix factorizations for cold-start recommendation

7 years 9 months ago
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...
Ke Zhou, Shuang-Hong Yang, Hongyuan Zha
Added 17 Sep 2011
Updated 17 Sep 2011
Type Journal
Year 2011
Where SIGIR
Authors Ke Zhou, Shuang-Hong Yang, Hongyuan Zha
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