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Iterative rating prediction for neighborhood-based collaborative filtering

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Abstract

This paper investigates the issue of rating prediction for neighborhood-based collaborative filtering in recommendation systems. A novel rating prediction algorithm, called iterative rating prediction (IRP), is proposed for neighborhood-based collaborative filtering. The main idea behind IRP is neighborhood propagation. To predict ratings of items for target users, IRP relies on not only the rating information of direct neighbors but also that of indirect neighbors with different propagation depth. To implement the idea, IRP iteratively updates the ratings of items for users. The efficiency of the proposed method is examined through extensive experiments. Experimental results demonstrate the superior performance of our method, especially on small-scaled and sparse datasets.

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  1. http://www.netflixprize.com

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Authors and Affiliations

  1. School of Computer Science and Technology, Joint International Research Laboratory of Machine Learning and Neuromorphic Computing, Soochow University, Suzhou, 215006, Jiangsu, China

    Li Zhang, Zepeng Li & Xiaohan Sun

  2. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, 215006, Jiangsu, China

    Zepeng Li & Xiaohan Sun

Authors
  1. Li Zhang
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  2. Zepeng Li
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  3. Xiaohan Sun
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Correspondence to Li Zhang.

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Supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 19KJA550002, the Six Talent Peak Project of Jiangsu Province of China under Grant No. XYDXX-054, and the Priority Academic Program Development of Jiangsu Higher Education Institutions.

Appendix A.: Proof of Theorem 1

Appendix A.: Proof of Theorem 1

Proof

According to the definition of the neighborhood relation, R can be represented by

$$ R=\{(u_{i},u_{j})|u_{j} \in N_{K}(u_{i}) \subset U, u_{i}\in U \} $$

The construction of neighborhood relations depends on K nearest neighbors of users. In the following, we prove the properties of R.

  1. 1.

    Whatever measurement is adopted to search K nearest neighbors, the distance between a user ui and itself is always zero. For any ui in U, it is true that ui ∈ NK(ui). Thus, (ui,ui) ∈ R. In other words, R is reflexive.

  2. 2.

    Whatever measurement is adopted to search K nearest neighbors, the statement that if uj is one of K nearest neighbors of ui but ui may be not one of K nearest neighbors of uj is true. In other words, there are ui and uj in U such that (ui,uj) ∈ R but (uj,ui)∉R. Thus, R is not symmetric.

  3. 3.

    Whatever measurement is adopted to search K nearest neighbors, the statement that if uj ∈ UK(ui) and uk ∈ UK(uj) but uk∉UK(ui) is true. In other words, there are ui and uj in U such that (ui,uj) ∈ R and (uj,uk) ∈ R but (ui,uk)∉R. Thus, R is not transitive.

That completes the proof of Theorem 1. □

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Zhang, L., Li, Z. & Sun, X. Iterative rating prediction for neighborhood-based collaborative filtering. Appl Intell 51, 6810–6822 (2021). https://doi.org/10.1007/s10489-021-02237-1

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