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An Effective Collaborative Filtering Via Enhanced Similarity and Probability Interval Prediction


Authors



Abstract

In recent years, as one of the most successful recommendation methods, collaborative filtering has been widely used in the recommendation system. Collaborative filtering predicts the active user preference for goods or services by collecting a historical data set of users0027 ratings for items; the underlying assumption is that the active user will prefer those items that the similar users prefer. Usually the data is quit sparse, which makes the computation of similarity between users or items imprecise and consequently reduces the accuracy of recommendations. In this paper, we propose an enhanced similarity method that the common ratings and the all ratings are both taken into account. Additionally, we present a generative probabilistic prediction framework in which we first predict a missing data probability value interval instead of a certain value by using the defined range of similar neighbors0027 ratings, and the final missing data rating is produced in the interval. Empirical studies on two datasets (MovieLens and Netflix) show that the proposed algorithm consistently outperforms other state-of-the-art collaborative filtering algorithms.


Keywords


Pages

Total Pages: 12
Pages: 555-566

DOI
10.1080/10798587.2014.934598


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Published

Volume: 20
Issue: 4
Year: 2014

Cite this document


References

Lee, Eric W.M., L.T. Wong, and K.W. Mui. "Development of a Hybrid Artificial Neural Network Model and Its Application to Data Regression." Intelligent Automation & Soft Computing 18.4 (2012): 319-332. Crossref. Web. https://doi.org/10.1080/10798587.2012.10643246

Koren, Yehuda. "Collaborative Filtering with Temporal Dynamics." Communications of the ACM 53.4 (2010): 89. Crossref. Web. https://doi.org/10.1145/1721654.1721677

Deshpande, Mukund, and George Karypis. "Item-Based Top-N Recommendation Algorithms." ACM Transactions on Information Systems 22.1 (2004): 143-177. Crossref. Web. https://doi.org/10.1145/963770.963776

Proceedings of the 16th international conference on World Wide Web, ACM

Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, ACM

Proceedings of the third ACM conference on Recommender systems, ACM

Koren, Yehuda. "Factorization Meets the Neighborhood." Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08 (2008): n. pag. Crossref. Web. https://doi.org/10.1145/1401890.1401944

Proceedings of the 10th international conference on intelligent user interfaces, ACM

Herlocker, Jon, Joseph A. Konstan, and John Riedl. Information Retrieval 5.4 (2002): 287-310. Crossref. Web. https://doi.org/10.1023/A:1020443909834

Gong, Songjie. "A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering." Journal of Software 5.7 (2010): n. pag. Crossref. Web. https://doi.org/10.4304/jsw.5.7.745-752

Linden, G., B. Smith, and J. York. "Amazon.com Recommendations: Item-to-Item Collaborative Filtering." IEEE Internet Computing 7.1 (2003): 76-80. Crossref. Web. https://doi.org/10.1109/MIC.2003.1167344

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM

Bobadilla, Jesus et al. "Improving Collaborative Filtering Recommender System Results and Performance Using Genetic Algorithms." Knowledge-Based Systems 24.8 (2011): 1310-1316. Crossref. Web. https://doi.org/10.1016/j.knosys.2011.06.005

Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, ACM

Su X. Advances in Artificial Intelligence

Jeong, Buhwan, Jaewook Lee, and Hyunbo Cho. "Improving Memory-Based Collaborative Filtering via Similarity Updating and Prediction Modulation." Information Sciences 180.5 (2010): 602-612. Crossref. Web. https://doi.org/10.1016/j.ins.2009.10.016

Proceedings of the fourth ACM conference on Recommender systems

Proceedings of the 10th international conference on World Wide Web, ACM

Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, ACM

Proceedings of the 10th Conference on Open Research Areas in Information Retrieval

arXiv preprint arXiv

Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence

Hofmann, Thomas. "Latent Semantic Models for Collaborative Filtering." ACM Transactions on Information Systems 22.1 (2004): 89-115. Crossref. Web. https://doi.org/10.1145/963770.963774

Ren, Dong et al. "An Improved Pca Fusion Method Based on Generalized Intensity-Hue-Saturation Fusion Technique." Intelligent Automation & Soft Computing 18.8 (2012): 1165-1175. Crossref. Web. https://doi.org/10.1080/10798587.2008.10643320

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)

TWO YEAR CITATIONS PER DOCUMENT (SJR DATA): 0.993 (2018)
SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."





Journal: 1995-Present


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