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Efficient collaborative filtering recommendations with multi-channel feature vectors

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Abstract

Feature vectors and similarity measures are the two key issues of most existing collaborative filtering (CF) algorithms. In item-based CF algorithms, the feature vector is often defined as the ratings of all users for a given item. For a recommender system with n users, m items, and c ratings, the length of the feature vector is n; hence, the time complexity of the similarity computation is O(n). Consequently, the overall time complexity is \(O(m^2n^2)\), which may be computationally prohibitive for recommender systems with millions of users. In this paper, we define the multi-channel feature vector (MCFV), which is a vector of channel length c, and calculate the similarity between items using the respective MCFVs. Each element of an MCFV corresponds to the number of users with respective ratings for the item. The time complexity for the similarity computation is O(c), and the overall time complexity is \(O(m^2nc)\) when the k-nearest neighbors and weighted average algorithms are used. Experiments were conducted on four movie recommender systems, where n ranges from a few hundred to half a million, and c is five. Results show that the recommendation algorithms using our new similarity measure are significantly faster than their counterparts without sacrificing prediction accuracy in terms of mean absolute error and root mean square error.

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Authors and Affiliations

  1. School of Computer Science, Southwest Petroleum University, Chengdu, 610500, China

    Heng-Ru Zhang, Fan Min & Zhi-Heng Zhang

  2. School of Science, Southwest Petroleum University, Chengdu, 610500, China

    Heng-Ru Zhang & Zhi-Heng Zhang

  3. Box Inc., 900 Jefferson Ave, Redwood City, CA, 94063, USA

    Song Wang

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  1. Heng-Ru Zhang
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  2. Fan Min
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  3. Zhi-Heng Zhang
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  4. Song Wang
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Correspondence to Fan Min.

Additional information

This work is supported in part by the National Natural Science Foundation of China (Grants 61379089, 41604114), the Innovation and Entrepreneurship Foundation of Southwest Petroleum University (Grant SWPUSC16-003) and the Natural Science Foundation of the Department of Education of Sichuan Province (Grant 16ZA0060).

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Zhang, HR., Min, F., Zhang, ZH. et al. Efficient collaborative filtering recommendations with multi-channel feature vectors. Int. J. Mach. Learn. & Cyber. 10, 1165–1172 (2019). https://doi.org/10.1007/s13042-018-0795-8

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  • DOI: https://doi.org/10.1007/s13042-018-0795-8

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