Weighted Similarity Schemes for High Scalability in User-Based Collaborative Filtering

Similarity-based algorithms, often referred to as memory-based collaborative filtering techniques, are one of the most successful methods in recommendation systems. When explicit ratings are available, similarity is usually defined using similarity functions, such as the Pearson correlation coeffici...

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Bibliographic Details
Published inMobile networks and applications Vol. 20; no. 4; pp. 497 - 507
Main Authors Pirasteh, Parivash, Hwang, Dosam, Jung, Jai E.
Format Journal Article
LanguageEnglish
Published New York Springer US 01.08.2015
Springer Nature B.V
Subjects
Algorithms
Analysis
Cold
Collaboration
Communications Engineering
Computer Communication Networks
Computer engineering
Electrical Engineering
Engineering
Filtering
Filtering software
Filtration
IT in Business
Methods
Networks
Ontology
Ratings
Ratings & rankings
Recommender systems
Similarity
Similarity measures
Sparsity
Studies
Symmetry
Trust
Weighting
Wireless networks
Collaborative filtering
User similarity
Asymmetric similarity
Recommender systems
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Summary:Similarity-based algorithms, often referred to as memory-based collaborative filtering techniques, are one of the most successful methods in recommendation systems. When explicit ratings are available, similarity is usually defined using similarity functions, such as the Pearson correlation coefficient, cosine similarity or mean square difference. These metrics assume similarity is a symmetric criterion. Therefore, two users have equal impact on each other in recommending new items. In this paper, we introduce new weighting schemes that allow us to consider new features in finding similarities between users. These weighting schemes, first, transform symmetric similarity to asymmetric similarity by considering the number of ratings given by users on non-common items. Second, they take into account the habit effects of users are regarded on rating items by measuring the proximity of the number of repetitions for each rate on common rated items. Experiments on two datasets were implemented and compared to other similarity measures. The results show that adding weighted schemes to traditional similarity measures significantly improve the results obtained from traditional similarity measures.
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ISSN:1383-469X
1572-8153
DOI:10.1007/s11036-014-0544-5