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A personalized recommendation method based on collaborative ranking with random walk

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Abstract

To improve the performance of recommender systems in a practical manner, many hybrid recommendation approaches have been proposed. Recently, some researchers apply the idea of ranking to recommender systems which yield plausible results. Collaborative ranking is a popular ranking based method, it regards that unrated items have lower rankings than rated items for a user. Unfortunately, the existing collaborative ranking approaches only focus on the partial associations with users and items, and thus fail to detect some features that could potentially improve the performance of the recommender systems. For this reason, these methods continue to suffer from data sparsity and do not work well for recommending an interesting item to an individual user. To address these issues, we present an Assembled Collaborative Ranking with Random Walk (ACR-RW) approach based on the combination of collaborative ranking and random walk method, which can be used to rank items according to expected user preferences by detecting both absolute and relative correlative information, in order to recommend top-ranked items to potentially interested users. On the basis of ACR-RW, we can improve the collaborative ranking approaches by adding absolute relationship information and defining the partial order relationship in assemblages rather than the global, so as to better describe and predict one user’s preference. Finally, we implement experiments on three real-world datasets, and the results show that our approach consistently outperforms all other comparative approaches, demonstrating its effectiveness for recommendation tasks.

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Notes

  1. https://grouplens.org/datasets/movielens/.

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Acknowledgements

This work was supported in part by Taishan Scholar Project of Shandong of China, and the National Natural Science Foundation of China under Grant U1836216.

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, China

    Runqing Jiang, Shanshan Feng, Shoujia Zhang & Huaxiang Zhang

  2. The Primary School attached to Shandong Normal University, Jinan, China

    Xi Li

  3. School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

    Yan Yao

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  1. Runqing Jiang
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  2. Shanshan Feng
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  4. Xi Li
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Correspondence to Shanshan Feng.

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Jiang, R., Feng, S., Zhang, S. et al. A personalized recommendation method based on collaborative ranking with random walk. Multimed Tools Appl 81, 7345–7363 (2022). https://doi.org/10.1007/s11042-022-11980-7

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