Minimizing Required User Effort for Cold-Start Recommendation by Identifying the Most Important Latent Factors

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Recommender systems have the effect of guiding users to interesting objects in a large space of possible options based on their preferences. However, providing accurate recommendations for new users, who do not have any records, is one of the most challenging problems in recommender systems. To retrieve sufficient information of the new users, existing solutions often require significant user effort to answer numerous questions. In this paper, we aim to propose a novel method that can build initial user profiles by requiring minimum user effort. In particular, non-negative matrix factorization is first adopted to extract only a few representative latent factors of the available items, which effectively reduces the problem dimensionality. A hybrid of personalized and non-personalized approach has then been proposed to iteratively collect users' feedback on the most important latent factors. In comparison with two state-of-the-art approaches, the proposed method requires less user effort while significantly improving the accuracy of the initial user profile.