Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction

Collaborative filtering (CF) in recommender systems boils down to analyzing the tabular data. These methods are based on the observed ratings in a rating matrix. the rating matrix is always extremely sparse. They consider how to alleviate the sparsity problem in collaborative filtering by transferring user-item rating knowledge from one task to other related tasks. The target task is represented as a spars rating matrix, containing few observed ratings. Then also get an auxiliary task from another domain, which is related to the target one and has a dense rating matrix. They show how to learn informative and yet compact cluster-level user-item rating patterns from the auxiliary rating matrix and transfer them to the target rating matrix and refer to this collection of patterns to be transferred as a “codebook”. By assuming the user-item rating patterns in target matrix is similar to auxiliary matrix, they can reconstruct the target rating matrix by expanding the codebook.