Abstract
Text data resources in the power domain have become increasingly abundant in recent years with the large scale popularization of information office in the power sector, but workers are facing an increasingly severe problem of data information overload. Since the concept of knowledge graph have been proposed, researchers have used professional datasets in various fields to construct corresponding knowledge graphs and proposed various knowledge graph completion algorithms to solve the problem of missing entity and relation links. In this paper, we introduce the knowledge graph as auxiliary information into the recommendation system of power domain. Our method uses translation-based models to learn the representations of users and items and applies them to optimize the recommender system. In addition, to address users diverse interests, we also build user profiles in our method to aggregate a users history with respect to candidate items. According to the characteristics of the data and the representativeness and universality of the data, extensive experiments are conducted on the Citeulike. We apply our approach to the power domain and construct the knowledge graph of the power domain dataset. The results validate the effectiveness of our approach on recommendation.
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References
Ma, K., Ilievski, F., Francis, J., et al.: Knowledge-driven data construction for zero-shot evaluation in commonsense question answering. In: 35th AAAI Conference on Artificial Intelligence (AAAI-21) (2020)
Gu, H., Wang, J., Wang, Z., Zhuang, B., Su, F.: Modeling of user portrait through social media. In: IEEE International Conference on Multimedia and Expo, pp. 1–6 (2018)
Li, J., et al.: Deep hybrid knowledge graph embedding for top-N recommendation. In: Workshop on Information Security Applications, pp. 59–70 (2020)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Annual Conference on Neural Information Processing Systems, pp. 2787–2795 (2013)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: AAAI Conference on Artificial Intelligence, pp. 1112–1119 (2014)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: AAAI Conference on Artificial Intelligence, pp. 2181–2187 (2015)
Wang, X., He, X., Cao, Y., Liu, M., Chua, T.S.: KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019)
Trisedya, B.D., Qi, J., Zhang, R.: Entity alignment between knowledge graphs using attribute embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33 (2019)
Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp. 687–696 (2015)
Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semantic Web 8, 489–508 (2017)
Zhao, B., Xu, Z., Tang, Y., Li, J., Liu, B., Tian, H.: Effective Knowledge-Aware Recommendation via Graph Convolutional Networks. In: Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds.) WISA 2020. LNCS, vol. 12432, pp. 96–107. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60029-7_9
Shubha, C.A., et al.: Analysis and implementation of recommender system in E-commerce. In: Lecture Notes in Engineering and Computer Science: Proceedings of The World Congress on Engineering and Computer Science 2018, 23–25 October 2018, San Francisco, USA, pp. 143–148 (2018)
Khatib, K.A.l., et al.: End-to-end argumentation knowledge graph construction. In: AAAI Technical Track: Natural Language Processing, vol. 34, no. 05 (2020)
Wang, H., Zhang, F., Xie, X., Guo, M.: DKN: deep knowledge-aware network for news recommendation. In: Proceedings of the 2018 World Wide Web Conference, pp. 1835–1844 (2018)
Wang, H., et al.: RippleNet: propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 417–426 (2018)
Wang, H., et al.: Multi-task feature learning for knowledge graph enhanced recommendation. In: International World Wide Web Conferences, pp. 2000–2010 (2019)
Acknowledgments
This work is supported by the Science and Technology Project of State Grid Corporation of China (Contract No.: SGSDWF00FCJS2000155).
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Wang, Y., Gao, S., Li, W., Jiang, T., Yu, S. (2021). Research and Application of Personalized Recommendation Based on Knowledge Graph. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds) Web Information Systems and Applications. WISA 2021. Lecture Notes in Computer Science(), vol 12999. Springer, Cham. https://doi.org/10.1007/978-3-030-87571-8_33
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DOI: https://doi.org/10.1007/978-3-030-87571-8_33
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