Novel Neighbor Selection Method to Improve Data Sparsity Problem in Collaborative Filtering

Memory-based collaborative filtering selects the top-k neighbors with high rank similarity in order to predict a rating for an item that the target user has not yet experienced. The most common traditional neighbor selection method for memory-based collaborative filtering is priority similarity. In...

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Bibliographic Details
Published in:International journal of distributed sensor networks 2013-01, Vol.2013 (-), p.1-6
Main Authors: Kwon, Hyeong-Joon, Hong, Kwang-Seok
Format: Article
Language:English
Subjects:
Accuracy
Errors
Experiments
Filtering
Filtration
Information retrieval
Networks
Priorities
Ratings
Ratings & rankings
Sensors
Similarity
Sparsity
Studies
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Summary:Memory-based collaborative filtering selects the top-k neighbors with high rank similarity in order to predict a rating for an item that the target user has not yet experienced. The most common traditional neighbor selection method for memory-based collaborative filtering is priority similarity. In this paper, we analyze various problems with the traditional neighbor selection method and propose a novel method to improve upon them. The proposed method minimizes the similarity evaluation errors with the existing neighbor selection method by considering the number of common items between two objects. The method is effective for the practical application of collaborative filtering. For validation, we analyze and compare experimental results between an existing method and the proposed method. We were able to confirm that the proposed method can improve the prediction accuracy of memory-based collaborative filtering by neighbor selection that prioritizes the number of common items.
ISSN:1550-1329
1550-1477
1550-1477
DOI:10.1155/2013/847965