A Neighborhood-based Matrix Factorization Technique for Recommendation

The data sparsity and prediction quality are recognized as the key challenges in the existing recommender Systems. Most of the existing recommender systems depend on collaborating flitering (CF) method which mainly leverages the user-item rating matrix representing the relationship between users and items. However, the CF-based method sometimes fails to provide accurate information for predicting recommendations as there is an assumption that the relationship between attributes of items is independent and identically distributed. In real applications, there are often several kinds of coupling relationships or connections existed among users or items. In this paper, we incorporate the coupling relationship analysis to capture the under-discovered relationships between items and aim to make the ratings more reasonable. Next, we propose a neighborhood-based matrix factorization model, which considers both the explicit and implicit correlations between items, to suggest the more reasonable items to user. The experimental evaluations demonstrate that the proposed algorithms outperform the state-of-the-art algorithms in the warm- and cold-start settings.