Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
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
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
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
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Online-updating regularized kernel matrix factorization models for large-scale recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative filtering adapted to recommender systems of e-learning
Knowledge-Based Systems
Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
Item-Based and User-Based Incremental Collaborative Filtering for Web Recommendations
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Fast online learning through offline initialization for time-sensitive recommendation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Expert Systems with Applications: An International Journal
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization
Knowledge-Based Systems
Incremental collaborative filtering for highly-scalable recommendation algorithms
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
A trust-semantic fusion-based recommendation approach for e-business applications
Decision Support Systems
Towards a user based recommendation strategy for digital ecosystems
Knowledge-Based Systems
A hybrid fuzzy-based personalized recommender system for telecom products/services
Information Sciences: an International Journal
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Recommender systems which can automatically match users with their potential favorites usually rely on Collaborative Filtering (CF). Since in real-world applications the data of historical user behavior are ever growing, it is important to study the incremental CF models which can adapt to this data explosion quickly and flexibly. The rating similarity based K-Nearest-Neighborhood (RS-KNN) is a classical but still popular approach to CF; therefore, to investigate the RS-KNN based incremental CF is significant. However, current incremental RS-KNN (I-KNN) models have the drawbacks of high storage complexity and relatively low prediction accuracy. In this work, we intend to boost the RS-KNN based incremental CF. We focus on two points which are respectively (a) reducing the storage complexity while maintaining the prediction accuracy by employing the generalized Dice coefficients, and (b) improving the prediction accuracy by integrating the similarity support and linear biases as well as implementing the corresponding incremental update. The efficiency of our strategies is supported by the positive results of the experiments conducted on two real datasets.