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Semi-supervised collaborative filtering ensemble

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Abstract

Collaborative filtering (CF) plays a central role in recommender systems, but often suffers from the data sparsity issue that dramatically degrades the recommendation performance. In this paper, we propose a Semi-Supervised Ensemble Filtering (SSEF) method to improve the recommendation performance by assembling three popular CF techniques in a co-training framework. Concretely, SSEF first initializes three weak predictors with labeled examples by three different CF algorithms independently. Two predictors generated by neighborhood methods are then merged, along with the remaining one generated by latent factor model, serve as two base recommenders, each of which labels the unlabeled examples for the other recommender during the co-training process. To exploit unlabeled data safely, the labeling confidence is estimated by validating the influence of the pseudo-labeled examples on the labeled ones. The final prediction is made by blending the outputs from the three predictors enhanced with unlabeled data. Extensive experiments on three public benchmarks demonstrate the effectiveness of the proposed SSEF by comparing to a number of state-of-the-art CF techniques, including semi-supervised, ensemble, and side-information based solutions.

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Notes

  1. Without loss of generality, we rescale all ratings in the interval [0, 1].

  2. Pearson correlation coefficient (2) is different from adjusted cosine similarity (4), though their equations are very similar. Concretely, the former conducts decentering on rows of rating matrix while the later does that on columns.

  3. We do not run trust-based algorithms on ML-100K, because the trust links are not available in such a dataset, as described in Table 2

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Authors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China

    Jun Wu, Xiankai Sang & Wei Cui

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  1. Jun Wu
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  2. Xiankai Sang
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  3. Wei Cui
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Correspondence to Jun Wu.

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This work was supported in part by the National Natural Science Foundation of China (Grants No. 61671048 and U1934220 )and the Fundamental Research Funds for the Central Universities (Grant No. 2019JBM316).

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Wu, J., Sang, X. & Cui, W. Semi-supervised collaborative filtering ensemble. World Wide Web 24, 657–673 (2021). https://doi.org/10.1007/s11280-021-00866-7

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