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Extended matrix factorization with entity network construction for recommendation

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Abstract

In order to improve the performance of recommender systems, user social information and item attribute information should be integrated when building the prediction model, which is a hotspot and difficulty in the field of recommender systems. In this paper, we propose an extended matrix factorization model based on network representation learning. To characterize users and items comprehensively, we construct the user relation network and the item relation network from the multi-source data. Then the representation vectors of users and items are learned from two networks respectively. The representation vectors learned from the relation networks can characterize users and items more effectively. Since users and items belong to different vector spaces, a matrix is used to connect user and item representation vectors when predicting ratings. To obtain the connection matrix, stochastic gradient descent is applied to minimize the errors between the predicted and observed ratings. Experimental results on two real-world datasets, Yelp and Douban, demonstrate the effectiveness of our model compared to the state-of-the-art recommendation algorithms.

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Acknowledgements

Thanks to Professor Fenlin Liu and Dr. Daofu Gong for their guidance on this article. Thanks to Dr. Lei Tan for his suggestions on the revision of this article.

Funding

This work was supported in part by the National Natural Science Foundation of China (U1804263).

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Correspondence to Daofu Gong.

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Xu, J., Tan, L., Gong, D. et al. Extended matrix factorization with entity network construction for recommendation. J Ambient Intell Human Comput 13, 1763–1775 (2022). https://doi.org/10.1007/s12652-021-03345-z

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