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Splitting-merging clustering algorithm for collaborative filtering recommendation system

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.5220/0006599501650174
AuthorSearch for: 1; Search for: 1; Search for: 1; Search for: 2
Name affiliation
  1. National Research Council of Canada. Digital Technologies
  2. National Research Council of Canada. Information and Communication Technologies
FormatText, Article
Conference10th International Conference on Agents and Artificial Intelligence, 16-18 January, 2018, Funchal, Madeira, Portugal
Subjectinformation filtering; recommender systems; collaborative filtering; clustering; splitting-merging clustering
Abstract
Collaborative filtering (CF) is a well-known and successful filtering technique that has its own limits, especially in dealing with highly sparse and large-scale data. To address this scalability issue, some researchers propose to use clustering methods like K-means that has the shortcomings of having its performances highly dependent on the manual definition of its number of clusters and on the selection of the initial centroids, which leads in case of ill-defined values to inaccurate recommendations and an increase in computation time. In this paper, we will show how the Merging and Splitting clustering algorithm can improve the performances of recommendation with reasonable computation time by comparing it with K-means based approach. Our experiment results demonstrate that the performances of our system are independent on the initial partition by considering the statistical nature of data. More specially, results in this paper provide significant evidences that the proposed splitting-merging clustering based CF is more scalable than the well-known K-means clustering based CF.
Publication date
PublisherINSTICC
In
  • Proceedings of the 10th International Conference on Agents and Artificial Intelligence: 165–174.
LanguageEnglish
Peer reviewedYes
NPARC number23002789
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Record created2018-02-27
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