Abstract
Deep neural networks (DNN) can be used to model users’ behavior sequences and predict their interest based on the historical behavior. However, current DNN-based recommendation methods lack explainability, making them difficult to guarantee the credibility of the recommendation results. In this paper, a Multi-Timeslice Graph Embedding (MTGE) model is proposed. First, it can effectively obtain the embedded representations of user behavior (or items) on a single timeslice. Second, the dynamic evolution of user preferences can be analyzed through integrating the embedded representations on multi-timeslices. Then, an explainable recommendation algorithm based on MTGE is proposed, which can effectively improve the accuracy of recommendation and support the model-level explainability. The feasibility and effectiveness of the key technologies proposed in the paper are verified through experiments.
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Acknowledgment
This work is supported by the National Key R&D Program of China (2018YFB1003404) and the National Natural Science Foundation of China (61672142).
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Wang, H., Kou, Y., Shen, D., Nie, T. (2020). An Explainable Recommendation Method Based on Multi-timeslice Graph Embedding. In: Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds) Web Information Systems and Applications. WISA 2020. Lecture Notes in Computer Science(), vol 12432. Springer, Cham. https://doi.org/10.1007/978-3-030-60029-7_8
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