A Survey of Transfer Learning for Collaborative Recommendation with Auxiliary Data,
- W. Pan
Factorization- and neighborhood-based methods have been recognized as state-of-the-art approaches for collaborative recommendation tasks. In this article, the authors take user ratings as categorical multiclass preferences and propose a novel method called matrix factorization with multiclass preference context (MF-MPC), which integrates an enhanced neighborhood based on the assumption that users with similar past multiclass preferences (instead of one-class preferences in SVD++) will have similar tastes in the future. The main merit of MF-MPC is its ability to make use of the multiclass preference context in the factorization framework in a fine-grained manner and thus inherit the advantages of those two methods. Experimental results on three real-world datasets show that their solution can perform significantly better than factorization-based methods, neighborhood-based methods, and integrated methods with a one-class preference context.