LeaderRank based k-means clustering initialization method for collaborative filtering

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, coll...

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Bibliographic Details
Published in:Computers & electrical engineering 2018-07, Vol.69, p.598-609
Main Authors: Kant, Surya, Mahara, Tripti, Kumar Jain, Vinay, Kumar Jain, Deepak, Kumar Sangaiah, Arun
Format: Article
Language:English
Subjects:
Cluster analysis
Clustering
Collaborative filtering
Consumers
Datasets
Decision making
Filtration
Information filtering
k-means
LeaderRank
Recommendation system
Recommender systems
Sparsity
Vector quantization
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Summary: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.
ISSN:0045-7906
1879-0755
DOI:10.1016/j.compeleceng.2017.12.001