Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Applying collaborative filtering techniques to movie search for better ranking and browsing
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
The YouTube video recommendation system
Proceedings of the fourth ACM conference on Recommender systems
Incremental collaborative filtering via evolutionary co-clustering
Proceedings of the fourth ACM conference on Recommender systems
Incremental collaborative filtering for highly-scalable recommendation algorithms
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Cloud based real-time collaborative filtering for item-item recommendations
Computers in Industry
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The traditional collaborative filtering approaches have been shown to suffer from two fundamental problems: data sparsity and difficulty in scalability. To address these problems, we present a novel scalable item-based collaborative filtering method by using incremental update and local link prediction. By subdividing the computations and analyzing the factors in different cases of item-to-item similarity, we design the incremental update strategies in item-based CF, which can make the recommender system more efficient and scalable. Based on the transitive structure of item similarity graph, we use the local link prediction method to find implicit candidates to alleviate the lack of neighbors in predictions and recommendations caused by the sparsity of data. The experiment results validate that our algorithm can improve the performance of traditional CF, and can increase the efficiency in recommendations.