Stochastic on-line knapsack problems
Mathematical Programming: Series A and B
Dynamic programming revisited: improving knapsack algorithms
Computing - Special issue on combinatorial optimization
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Collaborative Filtering Using Principal Component Analysis and Fuzzy Clustering
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
A Maximum Entropy Approach for Collaborative Filtering
Journal of VLSI Signal Processing Systems
Maximum entropy for collaborative filtering
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Collaborative Filtering with Maximum Entropy
IEEE Intelligent Systems
An efficient parallel algorithm for solving the Knapsack problem on hypercubes
Journal of Parallel and Distributed Computing
IEEE Transactions on Knowledge and Data Engineering
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Proposing an ESL recommender teaching and learning system
Expert Systems with Applications: An International Journal
Locally Adaptive Neighborhood Selection for Collaborative Filtering Recommendations
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Predicting Neighbor Goodness in Collaborative Filtering
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
An Approach of Selecting Right Neighbors for Collaborative Filtering
ICICIC '09 Proceedings of the 2009 Fourth International Conference on Innovative Computing, Information and Control
An effective threshold-based neighbor selection in collaborative filtering
ECIR'07 Proceedings of the 29th European conference on IR research
Review Article: Solving 0-1 knapsack problem by a novel global harmony search algorithm
Applied Soft Computing
Expert Systems with Applications: An International Journal
Privacy-Preserving Trust-Based Recommendations on Vertically Distributed Data
ICSC '11 Proceedings of the 2011 IEEE Fifth International Conference on Semantic Computing
A new criteria for selecting neighborhood in memory-based recommender systems
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
A collaborative filtering similarity measure based on singularities
Information Processing and Management: an International Journal
Stochastic search for global neighbors selection in collaborative filtering
Proceedings of the 27th Annual ACM Symposium on Applied Computing
A comparison of clustering-based privacy-preserving collaborative filtering schemes
Applied Soft Computing
A scalable privacy-preserving recommendation scheme via bisecting k-means clustering
Information Processing and Management: an International Journal
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Collaborative filtering is an emerging technology to deal with information overload problem guiding customers by offering recommendations on products of possible interest. Forming neighborhood of a user/item is the crucial part of the recommendation process. Traditional collaborative filtering algorithms solely utilize entity similarities in order to form neighborhoods. In this paper, we introduce a novel entropy-based neighbor selection approach which focuses on measuring uncertainty of entity vectors. Such uncertainty can be interpreted as how a user perceives rating domain to distinguish her tastes or diversification of items' rating distributions. The proposed method takes similarities into account along with such uncertainty values and it solves the optimization problem of gathering the most similar entities with minimum entropy difference within a neighborhood. Described optimization problem can be considered as combinatorial optimization and it is similar to 0-1 knapsack problem. We perform benchmark data sets-based experiments in order to compare our method's accuracy with the conventional user- and item-based collaborative filtering algorithms. We also investigate integration of our method with some of previously introduced studies. Empirical outcomes substantiate that the proposed method significantly improves recommendation accuracy of traditional collaborative filtering algorithms and it is possible to combine the entropy-based method with other compatible works introducing new similarity measures or novel neighbor selection methodologies.