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Personalized recommendation with implicit feedback via learning pairwise preferences over item-sets

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Abstract

Preference learning is a fundamental problem in various smart computing applications such as personalized recommendation. Collaborative filtering as a major learning technique aims to make use of users’ feedback, for which some recent works have switched from exploiting explicit feedback to implicit feedback. One fundamental challenge of leveraging implicit feedback is the lack of negative feedback, because there is only some observed relatively “positive” feedback available, making it difficult to learn a prediction model. In this paper, we propose a new and relaxed assumption of pairwise preferences over item-sets, which defines a user’s preference on a set of items (item-set) instead of on a single item only. The relaxed assumption can give us more accurate pairwise preference relationships. With this assumption, we further develop a general algorithm called CoFiSet (collaborative filtering via learning pairwise preferences over item-sets), which contains four variants, CoFiSet(SS), CoFiSet(MOO), CoFiSet(MOS) and CoFiSet(MSO), representing “Set vs. Set,” “Many ‘One vs. One’,” “Many ‘One vs. Set”’ and “Many ‘Set vs. One”’ pairwise comparisons, respectively. Experimental results show that our CoFiSet(MSO) performs better than several state-of-the-art methods on five ranking-oriented evaluation metrics on three real-world data sets.

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Notes

  1. http://www.grouplens.org/.

  2. https://www.netflix.com/.

  3. https://www.xing.com/.

  4. We used two-sample t-test in the statistical significance test and calculated the p value according to the thirty result scores of two compared methods on thirty copies of data sets via the MATLAB function ttest2.m as shown at http://www.mathworks.com/help/stats/ttest2.html.

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Acknowledgements

We thank the support of Hong Kong RGC under the Project RGC/HKBU12200415, Natural Science Foundation of China Nos. 61272365, 61502307 and 61672358

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, 3688 Nanhai Avenue, Nanshan District, Shenzhen, People’s Republic of China

    Weike Pan & Zhong Ming

  2. Department of Computer Science, Hong Kong Baptist University, 224 Waterloo Road, Kowloon Tong, Kowloon, Hong Kong, People’s Republic of China

    Weike Pan & Li Chen

Authors
  1. Weike Pan
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  2. Li Chen
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  3. Zhong Ming
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Corresponding authors

Correspondence to Li Chen or Zhong Ming.

Additional information

This work is an extension of our previous work [29]. We have added the following new contents in this manuscript: (i) we have developed three new variants of the CoFiSet algorithm in Sections 2.3–2.6; (ii) we have included new experimental results (Tables 3, 4, 5; Figures 3, 4) and associated analysis in Section 3; (iii) we have added more related works and discussions in Section 1 and Section 4; and (iv) we have made many improvements throughout the whole paper.

Some of this work was done, while Weike Pan was a postdoctoral research fellow in the Department of Computer Science, Hong Kong Baptist University.

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Pan, W., Chen, L. & Ming, Z. Personalized recommendation with implicit feedback via learning pairwise preferences over item-sets. Knowl Inf Syst 58, 295–318 (2019). https://doi.org/10.1007/s10115-018-1154-5

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