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Diversified recommendation method combining topic model and random walk

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Abstract

As one of the most widely used algorithms in recommendation field, collaborative filtering (CF) predicts the unknown rating of items based on similar neighbors. Although many CF-based recommendation methods have been proposed, there still be room for improvement. Firstly, the data sparsity problem still remains a big challenge for CF algorithms to find similar neighbors. Secondly, there are many redundant similar items in the recommendation list generated by traditional CF algorithms, which cannot meet the user wide interest. Therefore, we propose a diversified recommendation method combining topic model and random walk. A weighted random walk model is presented to find all direct and indirect similar neighbors on the sparse data, improving the accuracy of rating prediction. By taking both users’ behavior data and items’ lags into account, we give a diversity measurement method based on the topic distribution of items discovered by Linked-LDA model. Furthermore, a diversified ranking algorithm is developed to balance the accuracy and diversity of recommendation results. We compare our method with six other recommendation methods on a real-world dataset. Experimental results show that our method outperforms the other methods and achieves the best personalized recommendation effect.

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Acknowledgements

The authors appreciate the reviewers for their valuable comments which have greatly improved the quality of this paper. This work was supported in part by the National Natural Science Foundation of China [61303074, 61309013] and the Programs for Science and Technology Development of Henan province [12210231003, 13210231002].

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Authors and Affiliations

  1. State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou Information Science and Technology Institute, Zhengzhou, People’s Republic of China

    Chen Fang, Hengwei Zhang, Jindong Wang & Na Wang

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  1. Chen Fang
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  2. Hengwei Zhang
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  3. Jindong Wang
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  4. Na Wang
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Correspondence to Hengwei Zhang.

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Fang, C., Zhang, H., Wang, J. et al. Diversified recommendation method combining topic model and random walk. Multimed Tools Appl 77, 4355–4378 (2018). https://doi.org/10.1007/s11042-017-5504-1

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