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

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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.

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Notes

  1. http://www.grouplens.org

  2. https://www.cse.cuhk.edu.hk/irwin.king/pub/data/Douban

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Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2014R1A2A2A05007154).

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Authors and Affiliations

  1. Department of Computer Engineering, Yeungnam University, Gyeongsan, Republic of Korea

    Parivash Pirasteh & Dosam Hwang

  2. Department of Computer Engineering, Chung-Ang University, Seoul, Republic of Korea

    Jai E. Jung

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  1. Parivash Pirasteh
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  2. Dosam Hwang
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  3. Jai E. Jung
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Correspondence to Jai E. Jung.

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These authors contributed equally to this work as the first author.

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Pirasteh, P., Hwang, D. & Jung, J.E. Weighted Similarity Schemes for High Scalability in User-Based Collaborative Filtering. Mobile Netw Appl 20, 497–507 (2015). https://doi.org/10.1007/s11036-014-0544-5

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