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

Title
Weighted Similarity Schemes for High Scalability in User-Based Collaborative Filtering
Author(s)
황도삼파리바쉬피라스데정재은[정재은]
Keywords
RECOMMENDER SYSTEMS; PERSONALIZED RECOMMENDATION; MATRIX FACTORIZATION; TRUST METRICS; ALGORITHM; SELECTION; NETWORK; IMPROVE; MODEL
Issue Date
201508
Publisher
SPRINGER
Citation
MOBILE NETWORKS & APPLICATIONS, v.20, no.4, pp.497 - 507
Abstract
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.
URI
http://hdl.handle.net/YU.REPOSITORY/31320http://dx.doi.org/10.1007/s11036-014-0544-5
ISSN
1383-469X
Appears in Collections:
공과대학 > 컴퓨터공학과 > Articles
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