Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
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)
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A recursive prediction algorithm for collaborative filtering recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Unified relevance models for rating prediction in collaborative filtering
ACM Transactions on Information Systems (TOIS)
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Boosting collaborative filtering based on statistical prediction errors
Proceedings of the 2008 ACM conference on Recommender systems
A cross-cultural user evaluation of product recommender interfaces
Proceedings of the 2008 ACM conference on Recommender systems
TANGENT: a novel, 'Surprise me', recommendation algorithm
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Ensemble methods for improving the performance of neighborhood-based collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Constructing and exploring composite items
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Combining predictions for accurate recommender systems
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Using lower-bound similarity to enhance the performance of recommender systems
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
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In the literature, collaborative filtering (CF) approach and its variations have been proposed for building recommender systems. In CF, recommendations for a given user are computed based on the ratings of k nearest neighbours. The nearest neighbours of target user are identified by computing the similarity between the product ratings of the target user and the product ratings of every other user. In this paper, we have proposed an improved approach to compute the neighborhood by exploiting the categories of products. In the proposed approach, ratings given by a user are divided into different sub-groups based on the categories of products. We consider that the ratings of each sub-group are given by a virtual user. For a target user, the recommendations of the corresponding virtual user are computed by employing CF. Next, the recommendations of the corresponding virtual users of the target user are combined for recommendation. The experimental results on MovieLens dataset show that the proposed approach improves the performance over the existing CF approach.