Collaborative Filtering (CF) is a popular way to build recommender systems and has been successfully employed in many applications. Generally, two kinds of approaches to CF, the local neighborhood methods and the global matrix factorization models, have been widely studied. Though some previous researches target on combining the complementary advantages of both approaches, the performance is still limited due to the extreme sparsity of the rating data. Therefore, it is necessary to consider more information for better reflecting user preference and item content. To that end, in this paper, by leveraging the extra tagging data, we propose a novel unified two-stage recommendation framework, named Neighborhood-aware Probabilistic Matrix Factorization(NHPMF). Specifically, we first use the tagging data to select neighbors of each user and each item, then add unique Gaussian distributions on each user's (item's) latent feature vector in the matrix factorization to ensure similar users (items) will have similar latent features. Since the proposed method can effectively explores the external data source (i.e., tagging data) in a unified probabilistic model, it leads to more accurate recommendations. Extensive experimental results on two real world datasets demonstrate that our NHPMF model outperforms the state-of-the-art methods.
Recently, the neighborhood-aware probabilistic matrix factorization (NHPMF) model is proposed REF as a method to combine kNN and PMF.
Leveraging tagging for neighborhood-aware probabilistic matrix factorization
CIKM '12
Computer Science