Efficient Rectangular Maximal-Volume Algorithm for Rating Elicitation in Collaborative Filtering
This addresses the cold start problem for recommendation systems, offering an incremental improvement over prior matrix factorization approaches.
The paper tackles the cold start problem in collaborative filtering by introducing Rectangular Maxvol, a fast algorithm that allows the factorization rank to be lower than the seed set size, improving efficiency and flexibility compared to existing methods.
Cold start problem in Collaborative Filtering can be solved by asking new users to rate a small seed set of representative items or by asking representative users to rate a new item. The question is how to build a seed set that can give enough preference information for making good recommendations. One of the most successful approaches, called Representative Based Matrix Factorization, is based on Maxvol algorithm. Unfortunately, this approach has one important limitation --- a seed set of a particular size requires a rating matrix factorization of fixed rank that should coincide with that size. This is not necessarily optimal in the general case. In the current paper, we introduce a fast algorithm for an analytical generalization of this approach that we call Rectangular Maxvol. It allows the rank of factorization to be lower than the required size of the seed set. Moreover, the paper includes the theoretical analysis of the method's error, the complexity analysis of the existing methods and the comparison to the state-of-the-art approaches.