An Improved Matrix Approximation for Recommender Systems Based on Context-Information and Transfer Learning

Abstract

As we all known, transfer learning is an effective way to alleviate the sparsity problem in recommender systems by transferring the shared knowledge cross multiple related domains. However, additional related domain is not always available, and auxiliary data may be noisy and this leads to negative transfer. In this paper, we suppose that different parts of one domain also have the shared knowledge and put forward a novel in-domain collaborative filtering framework, which utilizes contextual information to divide an original user-item interaction matrix into some smaller sub-matrices and regards the selected sub-matrices as ''multiple domains'' to establish transfer learning. The proposed framework no longer needs additional domain information and has a better adaptive ability. Also, considering more actual situation that users may have multiple personalities and items may have diverse attributes, we resort to Rating-Matrix Generative Model (RMGM) to generate the shared cluster-level rating pattern. Experiments on dataset Douban with three different categories demonstrate that the proposed framework can improve the prediction accuracy as well as the top-N recommendation performance.INDEX TERMS Recommender systems, context-aware, transfer learning, in-domain collaborative filtering.

Cyclic Transfer Learning for Recommender Systems with Heterogeneous Feedbacks

“…RMGM [16]: It divides the high-dimensional rating matrix into multiple sub-matrices and treats each submatrix as different domains. It utilizes the common rating features of each sub-domain to complete knowledge transfer and makes rating prediction within the domain.…”

“…Although MF achieves good prediction results, its performance will be limited in the case of data sparsity and cold start. Many researchers try to combine different types of data with ratings to design recommendation algorithms [12]- [16]. Transfer learning can extract valuable knowledge from different information domains to help improve the learning performance in the target domain.…”