Abstract
Recommendation System (RS) is of great significance for screening adequate information and improving the efficiency of information acquisition. The existing recommendation methods can improve the accuracy of the recommendation results to a certain extent. However, due to the lack of interpretability, the recommendation results are insufficient and burdensome to satisfy the desire of some users to understand the recommendation basis. For this reason, the Knowledge Graph (KG) is introduced as auxiliary information of the RS. It calculates the importance of entities and relations and uses them as the recommendation’s basis. First, item categories are introduced to enhance the impact on user preferences. Second, an Attention Mechanism (AM) is proposed to distinguish users’ interests in different contextual entity-relation sets in the KG. Finally, multiple feature information is input into the Convolutional Neural Network (CNN) to extract users’ preferences. The experimental results prove that the model can improve the accuracy of the recommendation effect, and at the same time, it can better explain the reasons for the recommendation.
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Acknowlegements
This work is supported by the National Natural Science Foundation of China under Grant No. 62162046, the Inner Mongolia Science and Technology Project under Grant No. 2021GG0155, the Natural Science Foundation of Major Research Plan of Inner Mongolia under Grant No. 2019ZD15, and the Inner Mongolia Natural Science Foundation under Grant No. 2019GG372.
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Wu, Y., Li, J., Zhou, J. (2022). KIR: A Knowledge-Enhanced Interpretable Recommendation Method. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13368. Springer, Cham. https://doi.org/10.1007/978-3-031-10983-6_3
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DOI: https://doi.org/10.1007/978-3-031-10983-6_3
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