[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.