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LeaderRank based k-means clustering initialization method for collaborative filtering
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DOI：10.1016/j.compeleceng.2017.12.001.png)
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Collaborative filtering based Recommender System is one of the most common technique used for personalized product ranking. It aids the consumer in decision-making process. It helps to choose a product according to the consumer's preference from a large pool of choices.Despite its success, collaborative filtering suffers from the sparsity problem which limits the quality of recommendations. In this paper, we investigate the application of clustering collaborative framework. A unique centroid selection approach for k-means clustering algorithm is proposed that aims to improve clustering quality. The results on three benchmark datasets depict the improvement in the quality of recommendations made. (C) 2017 Elsevier Ltd. All rights reserved.
Keyword:
Recommendation system
Collaborative filtering
Clustering
k-means
Information filtering
LeaderRank
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A new collaborative filtering metric that improves the behavior of recommender systems一种新的协同过滤度量，可改善推荐系统的行为
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