An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Lessons from the Netflix prize challenge
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Tag-based social interest discovery
Proceedings of the 17th international conference on World Wide Web
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Matrix factorization and neighbor based algorithms for the netflix prize problem
Proceedings of the 2008 ACM conference on Recommender systems
TagRec: Leveraging Tagging Wisdom for Recommendation
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
TagiCoFi: tag informed collaborative filtering
Proceedings of the third ACM conference on Recommender systems
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
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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.