Collective Matrix Factorization Based on Knowledge Representation Learning

In order to solve the problem of user feedback data sparseness existed in collaborative filtering method, a collective matrix factorization method was proposed based on knowledge graph. The method was arranged to make up for the scarce of the user feedback data with additional item sematic information. Learning item embeddings from items’ knowledge graph, the method was designed to jointly factorize a user feedback matrix and an item relatedness matrix with the same item embeddings. Experimental results on two datasets show that the proposed method can significantly improve the performance of matrix factorization models, and it can solve the cold start problem to some extent.