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Leveraging pointwise prediction with learning to rank for top-N recommendation

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Abstract

Pointwise prediction and Learning to Rank (L2R) are two hot strategies to model user preference in recommender systems. Currently, these two types of approaches are often considered independently, and most existing efforts utilize them separately. Unfortunately, pointwise prediction tends to cause the problem of overfitting, while L2R is more prone to higher variance. On the other hand, the advantages of multi-task learning and ensemble learning inspire us to utilize multiple approaches jointly so that methods can promote together synergistically. Therefore, we propose a new framework called CPL, where pointwise prediction and L2R are inherently combined to improve the performance of top-N recommendations. To verify the effectiveness of CPL, an instantiation of CPL, which is named CPLmg, is introduced. CPLmg is based on two components, i.e., Factorized SLIM (Sparse LInear Method) and GAPfm (Graded Average Precision factor model), to perform pointwise prediction and L2R, respectively. Different from the original version of SLIM, FSLIM reconstructs a denser representation both for users and items. Moreover, the low-rank users’ and item’s latent factor matrices act as a bridge between FSLIM and GAPfm. Extensive experiments on four real-world datasets show that CPLmg significantly outperforms the compared methods. To explore other possible combinations for CPL further, we selected another pointwise method, i.e., FunkSVD, and L2R approach, i.e., BPR, to implement CPLdb. The experimental results demonstrate the superiority of CPL again as it can help improve the performance of its pointwise prediction and L2R components.

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Notes

  1. https://www.librec.net/datasets.html#filmtrust

  2. http://ir.ii.uam.es/hetrec2011

  3. https://sifter.org/%7Esimon/journal

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Acknowledgements

This work is partially supported by National Key Research and Development Plan (No. 2018YFB1003800).

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Authors and Affiliations

  1. Shanghai Institute for Advanced Communication and Data Science, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Nengjun Zhu, Jian Cao & Qi Gu

  2. Baidu Business Intelligence Lab, Baidu Research, Beijing, China

    Xinjiang Lu

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  1. Nengjun Zhu
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  2. Jian Cao
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Correspondence to Jian Cao.

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This article belongs to the Topical Collection: Special Issue on Web Information Systems Engineering 2019

Guest Editors: Reynold Cheng, Nikos Mamoulis, and Xin Huang

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Zhu, N., Cao, J., Lu, X. et al. Leveraging pointwise prediction with learning to rank for top-N recommendation. World Wide Web 24, 375–396 (2021). https://doi.org/10.1007/s11280-020-00846-3

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