Authors:
Nabil Belacel
;
Guillaume Durand
;
Serge Leger
and
Cajetan Bouchard
Affiliation:
National Research Council, Canada
Keyword(s):
Information Filtering, Recommender Systems, Collaborative Filtering, Clustering, Splitting-merging Clustering.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge-Based Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
;
Web Information Systems and Technologies
;
Web Intelligence
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 propo
sed splitting-merging clustering based CF is more scalable than the well-known K-means clustering
based CF.
(More)