Neural User Preference Modeling Framework Based on Knowledge Graph
ZHU Guiming1,2, BIN Chenzhong2, GU Tianlong2, CHEN Wei2, JIA Zhonghao2
1.School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004
2.Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004
An end-to-end neural user preference modeling framework incorporating knowledge graph into recommender systems, neural user preference modeling framework based on knowledge graph(NUPM), is proposed aiming at the limitations of the current feature-based and path-based knowledge aware recommendation method. Historical interaction items of users in knowledge graph are considered as preference origin of NUPM. Then, potential preferences of users are learned by propagating user interests through relational links between entities in knowledge graph. Furthermore, an attention network is exploited to combine the preference features of different propagation stages to construct final user preference vector. The experimental results on real dataset show the effectiveness of NUPM in personalized recommendation for characterizing user preference.
[1] HE X N, LIAO L Z, ZHANG H W, et al. Neural Collaborative Filtering // Proc of the 26th International Conference on World Wide Web. New York, USA: ACM, 2017: 173-182.
[2] WANG X, HE X N, FENG F L, et al. TEM: Tree-Enhanced Embedding Model for Explainable Recommendation // Proc of the 27th International Conference on World Wide Web. New York, USA: ACM, 2018: 1543-1552.
[3] BAYER I, HE X N, KANAGAL B, et al. A Generic Coordinate Descent Framework for Learning from Implicit Feedback // Proc of the 26th International Conference on World Wide Web. New York, USA: ACM, 2017: 1341-1350.
[4] WANG H W, ZHANG F Z, HOU M, et al. SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction // Proc of the 11th International Conference on Web Search and Data Mining. New York, USA: ACM, 2018: 592-600.
[5] JAMALI M, ESTER M. A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks // Proc of the 4th ACM Conference on Recommender Systems. New York, USA: ACM, 2010: 135-142.
[6] ZHANG F Z, YUAN N J, LIAN D F, et al. Collaborative Know-ledge Base Embedding for Recommender Systems // Proc of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2016: 353-362.
[7] SUN Z, YANG J, ZHANG J, et al. Recurrent Knowledge Graph Embedding for Effective Recommendation // Proc of the 12th ACM Conference on Recommender Systems. New York, USA: ACM, 2018: 297-305.
[8] WANG H W, ZHANG F Z, WANG J L, et al. RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems // Proc of the 27th International Conference on Information and Knowledge Management. New York, USA: ACM, 2018: 417-426.
[9] RENDLE S. Factorization Machines with LibFM. ACM Transactions on Intelligent Systems and Technology, 2012, 3(3). DOI: 10.1145/2168752.2168771.
[10] YU X, REN X, SUN Y Z, et al. Personalized Entity Recommendation: A Heterogeneous Information Network Approach // Proc of the 7th ACM International Conference on Web Search and Data Mining. New York, USA: ACM, 2014: 283-292.
[11] ZHAO H, YAO Q M, LI J D, et al. Meta-Graph Based Reco-mmendation Fusion over Heterogeneous Information Networks // Proc of the 23th ACM SIGKDD International Conference on Know-ledge Discovery and Data Mining. New York, USA: ACM, 2017: 635-644.
[12] WANG H W, ZHANG F Z, XIE X, et al. DKN: Deep Know-ledge-Aware Network for News Recommendation // Proc of the 27th International Conference on World Wide Web. New York, USA: ACM, 2018: 1835-1844.