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Measure prediction capability of data for collaborative filtering

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Abstract

Collaborative filtering (CF) approaches have been widely been employed in e-commerce to help users find items they like. Whereas most of existing work focuses on improving algorithmic performance, it is important to know whether the recommendation for users and items can be trustworthy. In this paper, we propose a metric, “relatedness,” to measure the potential that a user’s preference on an item can be accurately predicted. The relatedness of a user–item pair is determined by a community which consists of users and items most related to the pair. The relatedness is computed by solving a constrained \(\ell _{1}^{}\)-regularized least square problem with a generalized homotopy algorithm, and we design the homotopy-based community search algorithm to identify the community by alternately selecting the most related users and items. As an application of the relatedness metric, we develop the data-oriented combination (DOC) method for recommender systems by integrating a group of benchmark CF methods based on the relatedness of user–item pairs. In experimental studies, we examine the effectiveness of the relatedness metric and validate the performance of the DOC method by comparing it with benchmark methods.

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Authors and Affiliations

  1. College of Science, China University of Petroleum, Qingdao, 266555, China

    Xijun Liang

  2. Department of Computer Science, Western Kentucky University, Bowling Green, KY, 42101, USA

    Zhonghang Xia

  3. School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China

    Liping Pang, Liwei Zhang & Hongwei Zhang

Authors
  1. Xijun Liang
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  2. Zhonghang Xia
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  3. Liping Pang
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  4. Liwei Zhang
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  5. Hongwei Zhang
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Corresponding author

Correspondence to Xijun Liang.

Additional information

This work is partially supported by the Natural Science Foundation of China under Grant 11171049 and 61503412, Natural Science Foundation of Shandong Province under Grant No. ZR2014AP004, and Fundamental Research Funds for the Central Universities under Grant No. 15CX02051A.

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Liang, X., Xia, Z., Pang, L. et al. Measure prediction capability of data for collaborative filtering. Knowl Inf Syst 49, 975–1004 (2016). https://doi.org/10.1007/s10115-016-0920-5

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  • DOI: https://doi.org/10.1007/s10115-016-0920-5

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