推荐系统概述7

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原创最新推荐文章于 2026-03-04 00:31:02 发布 3.1k 阅读 AI 写同款· GEO 优化 ›

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本篇是第7篇

截止目前，推荐系统概述系列全部完毕，这一节，主要列出引用和看过的参考文献：

[1] Shi, Chuan, et al. “A survey of heterogeneous information network analysis.“IEEE Transactions on Knowledge and Data Engineering 29.1 (2017): 17-37.

[2] M. Jamali and L. Lakshmanan, “HeteroMF: Recommendation in heterogeneous information networks using context dependent factor models,” in Proc. 22nd Int. Conf. World Wide Web, 2013, pp. 643–654

[3] C. Shi, C. Zhou, X. Kong, P. S. Yu, G. Liu, and B. Wang, “HeteRecom: A semantic-based recommendation system in heterogeneous networks,” in Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2012, pp. 1552–1555.

[4] X. Yu, et al., “Recommendation in heterogeneous information networks with implicit user feedback,” in Proc. 7th ACM Conf. Recommender Syst., 2013, pp. 347–350.

[5] C. Shi, Z. Zhang, P. Luo, P. S. Yu, Y. Yue, and B. Wu, “Semantic path based personalized recommendation on weighted heterogeneous information networks,” in Proc. 24th ACM Int. Conf. Inf. Knowl. Manage., 2015, pp. 453–462.

[6] N. Srebro and T. Jaakkola, “Weighted low-rank approximations,” in Proc. 20th Int. Conf. Mach. Learn., 2003, pp. 720–727.
[7] H. Ma, I. King, and M. R. Lyu, “Learning to recommend with social trust ensemble,” in Proc. 32nd Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2009, pp. 203–210.

[8] X. Yang, H. Steck, and Y. Liu, “Circle-based recommendation in online social networks,” in Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2012, pp. 1267–1275.

[9] X. Yu, et al., “Personalized entity recommendation: A heterogeneous information network approach,” in Proc. 7th ACM Int. Conf. Web Search Data Mining, 2014, pp. 283-292.

[10] R. Burke, F. Vahedian, and B. Mobasher, “Hybrid recommendation in heterogeneous networks,” in Proc. 22nd Int. Conf. User Modeling Adaptation Personalization, 2014, pp. 49-60.

[11] X. Ren, et al., “ClusCite: Effective citation recommendation by information network-based clustering,” in Proc. 20th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2014, pp. 821-830.

[12] J. Wu, L. Chen, Q. Yu, P. Han, and Z. Wu, “Trust-aware media recommendation in heterogeneous social networks,” World Wide Web, vol. 18, no. 1, pp. 139–157, 2015.

[13] C. Yang, J. Sun, J. Ma, S. Zhang, G. Wang, and Z. Hua, “Scientific collaborator recommendation in heterogeneous bibliographic networks,” in Proc. 48th Hawaii Int. Conf. Syst Sci., 2015, pp. 552–561.

[14] C. Luo, W. Pang, Z. Wang, and C. Lin, “Hete-CF: Social-based collaborative filtering recommendation using heterogeneous relations,” in Proc. Int. Conf. Data Mining, 2014, pp. 917–922.

[15] C. Shi, J. Liu, F. Zhuang, P. S. Yu, and B. Wu, “Integrating heterogeneous information via flexible regularization framework for recommendation,” Knowl. Inf. Syst., pp. 1–25, 2016.

[16] J. Zheng, J. Liu, C. Shi, F. Zhuang, J. Li, and B. Wu, “Dual similarity regularization for recommendation,” in Proc. 20th Pacific-Asia Conf. Advances Knowl. Discovery Data Mining, 2016, pp. 542–554.

[17] Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, 42:30–37, 2009.

[18] A. P. Singh and G. J. Gordon. Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD’08), pages 650–658, 2008.
[19] S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In UAI,2009.

[20]凸优化理论，共轭函数
http://www.cnblogs.com/murongxixi/p/3617054.html

[21] Meet the Bregman Divergences
http://mark.reid.name/blog/meet-the-bregman-divergences.html

[22]Inderjit S. Learning with Bregman Divergences http://www.cs.utexas.edu/users/inderjit/Talks/bregtut.pdf

[23] Y. Sun, J. Han, X. Yan, S. P. Yu, and T. Wu. PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks. In VLDB, 2011

[24] C. Shi, X. Kong, P. S. Yu, and S. Xie. Relevance search in heterogeneous networks. In EDBT, 2012.

[25] Covington, Paul, Jay Adams, and Emre Sargin. “Deep neural networks for youtube recommendations.” Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016.

[26] Christakopoulou, Evangelia, and George Karypis. “Local item-item models for top-n recommendation.” Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016.

[27] Teo, Choon Hui, et al. “Adaptive, Personalized Diversity for Visual Discovery.” Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016.

[28] Kim, Donghyun, et al. “Convolutional matrix factorization for document context-aware recommendation.” Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016.

[29] Davidson, James, et al. “The YouTube video recommendation system.” Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010.

[30]项亮. 推荐系统实践[M]. 人民邮电出版社, 2012.

[31]弗朗西斯科・里奇等. 推荐系统：技术、评估及高效算法. 机械工业出版社, 2015.

[32] Steck, Harald. “Evaluation of recommendations: rating-prediction and ranking.” Proceedings of the 7th ACM conference on Recommender systems. ACM, 2013.

[33] Brovman, Yuri M., et al. “Optimizing Similar Item Recommendations in a Semi-structured Marketplace to Maximize Conversion.” Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016.

[34] Cremonesi, Paolo, Yehuda Koren, and Roberto Turrin. “Performance of recommender algorithms on top-n recommendation tasks.” Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010.

[35] Liang, Dawen, et al. “Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence.” Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016.

[36] Puthiya Parambath, Shameem A., Nicolas Usunier, and Yves Grandvalet. “A coverage-based approach to recommendation diversity on similarity graph.” Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016.

[37] Massa, Paolo, and Paolo Avesani. “Trust-aware recommender systems.” Proceedings of the 2007 ACM conference on Recommender systems. ACM, 2007.

[38] Jamali, Mohsen, and Martin Ester. “A matrix factorization technique with trust propagation for recommendation in social networks.” Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010.

[39] Karatzoglou, Alexandros, et al. “Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering.” Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010.

[40] Shepitsen, Andriy, et al. “Personalized recommendation in social tagging systems using hierarchical clustering.” Proceedings of the 2008 ACM conference on Recommender systems. ACM, 2008.

[41] McAuley, Julian, and Jure Leskovec. “Hidden factors and hidden topics: understanding rating dimensions with review text.” Proceedings of the 7th ACM conference on Recommender systems. ACM, 2013.

[42] Hannon, John, Mike Bennett, and Barry Smyth. “Recommending twitter users to follow using content and collaborative filtering approaches.” Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010.

[43] Gantner, Zeno, et al. “MyMediaLite: A free recommender system library.” Proceedings of the fifth ACM conference on Recommender systems. ACM, 2011.

[44] Krestel, Ralf, Peter Fankhauser, and Wolfgang Nejdl. “Latent dirichlet allocation for tag recommendation.” Proceedings of the third ACM conference on Recommender systems. ACM, 2009.

[45]Massa, Paolo, and Paolo Avesani. “Trust-aware collaborative filtering for recommender systems.” OTM Confederated International Conferences” On the Move to Meaningful Internet Systems”. Springer Berlin Heidelberg, 2004.

[46]P. Massa and P. Avesani. Trust-aware recommender systems. In Proc. of RecSys, pages 17–24, New York, NY, USA, 2007. ACM.

[47]郑捷. 机器学习算法原理与编程实践[M].电子工业出版社,2015.

[48]扎法拉尼, 阿巴西, 刘刘挺,等. 社会媒体挖掘 : Social media mining an introduction[M]. 人民邮电出版社, 2015.

[49] Maya Hristakeva, Kris JackA practical and Michael Ekstrand.”guide to building recommender systems:From Algorithms to Product.”
https://buildingrecommenders.wordpress.com/

[50]刘建平. 协同过滤算法总结.
http://www.cnblogs.com/pinard/p/6349233.html#undefined

[51] Adomavicius, Gediminas, and Alexander Tuzhilin. “Context-aware recommender systems.” Recommender systems handbook. Springer US, 2015. 191-226.

[52] Covington, Paul, Jay Adams, and Emre Sargin. “Deep neural networks for youtube recommendations.” Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016.

[53] X. Su and T. M. Khoshgoftaar. A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009:4, 2009.

[54] K. J. Oh, W. J. Lee, C. G. Lim, and H. J. Choi. Personalized news recommendation using classified keywords to capture user preference. In 16th International Conference on Advanced Communication Technology, pages 1283-1287, Feb 2014.

[55] W. Huang, Z. Wu, L. Chen, P. Mitra, and C. L. Giles.A neural probabilistic model for context based citation recommendation. In AAAI, pages 2404-2410, 2015

[56] D. Tang, B. Qin, T. Liu, and Y. Yang. User modeling with neural network for review rating prediction. In Proc. IJCAI, pages 1340-1346, 2015

[57] H. Wang, N. Wang, and D.-Y. Yeung. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’15, pages 1235-1244, New York, NY, USA, 2015. ACM.

[58] S. Sedhain, A. K. Menon, S. Sanner, and L. Xie. Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th International Conference on World Wide Web, WWW ’15 Companion, pages 111-112, New York, NY, USA, 2015. ACM.

[59] A. M. Elkahky, Y. Song, and X. He. A multi-view deep learning approach for cross domain user modeling in recommendation systems. In Proceedings of the 24th International Conference on World Wide Web, WWW’15, pages 278-288, New York, NY, USA, 2015. ACM.

[60] A. van den Oord, S. Dieleman, and B. Schrauwen. Deep content-based music recommendation. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 26, pages 2643-2651. Curran Associates, Inc., 2013.

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