Less is More: Sparse Representative based Preference Elicitation for Cold Start Recommendation

Cold start recommendation is a challenging but crucial problem for recommender systems. Preference Elicitation, as a commonly used approach to address the problem, solicits preference of cold user by interviewing them with some elaborately selected items. How to select minimum items to reflect user preference as much as possible is the essential goal of preference elicitation. In this paper, we propose a novel Structured Sparse Representative Selection(SSRS) model to select a sparse set of items based on their ability of representation. Moreover, a ℓ2,1-norm is utilized on both loss function and regularization to make the model insensitive to outliers and avoid selecting redundant queries respectively. Empirical results on benchmark movie rating datasets Movielens and Flixster verify the promising performance of our proposed preference elicitation method for cold start recommendation.