Social Recommendation with Missing Not at Random Data（ICDM 2018）参考文献

最新推荐文章于 2025-03-30 08:53:06 发布

翻译最新推荐文章于 2025-03-30 08:53:06 发布 · 1k 阅读

论文翻译专栏收录该内容

6 篇文章

订阅专栏

本文综述了社交网络中推荐系统的最新研究进展，涵盖了从矩阵分解到深度学习的各种算法，探讨了信任传播、社交影响力和社会关系在个性化推荐中的作用。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

第一部分带图的文章链接：https://blog.youkuaiyun.com/ciecus_csdn/article/details/84454425

第二部分算法以及实验部分链接：https://blog.youkuaiyun.com/ciecus_csdn/article/details/84454128

第三部分参考文献：https://blog.youkuaiyun.com/ciecus_csdn/article/details/84480998

REFERENCES

[1] S.-H. Yang, B. Long, A. Smola, N. Sadagopan, Z. Zheng, and H. Zha, “Like like alike: joint friendship and interest propagation in social networks,” in Proceedings of the 20th international conference on World wide web. ACM, 2011, pp. 537–546.
[2] T. Zhao, J. McAuley, and I. King, “Leveraging social connections to improve personalized ranking for collaborative filtering,” in Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. ACM, 2014, pp. 261–270.
[3] G.Guo,J.Zhang,andN.Yorke-Smith,“Trustsvd:Collaborativefiltering with both the explicit and implicit influence of user trust and of item ratings,” in AAAI, 2015.
[4] B. M. Marlin and R. S. Zemel, “Collaborative prediction and ranking with non-random missing data,” in Proceedings of the third ACM conference on Recommender systems. ACM, 2009, pp. 5–12.
[5] B. M. Marlin, R. S. Zemel, S. Roweis, and M. Slaney, “Collaborative filtering and the missing at random assumption,” in UAI. AUAI Press, 2007, pp. 267–275.
[6] S. Ohsawa, Y. Obara, and T. Osogami, “Gated Probabilistic Matrix Factorization : Learning Users ’ Attention from Missing Values,” Ijcai, pp. 1888–1894, 2016.
[7] J.M.Herna ́ndez-Lobato,N.Houlsby,andZ.Ghahramani,“Probabilistic matrix factorization with non-random missing data.” in ICML, 2014, pp. 1512–1520.
[8] B. Pradel, N. Usunier, and P. Gallinari, “Ranking with non-random missing ratings: influence of popularity and positivity on evaluation metrics,” in Proceedings of the sixth ACM conference on Recommender systems. ACM, 2012, pp. 147–154.
[9] Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer, vol. 42, no. 8, 2009.
[10] A. Mnih and R. R. Salakhutdinov, “Probabilistic matrix factorization,” in NIPS, 2008, pp. 1257–1264.
[11] H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King, “Recommender systems with social regularization,” in Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 2011, pp. 287–296.
[12] X. Yang, H. Steck, and Y. Liu, “Circle-based recommendation in online social networks,” in Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012, pp. 1267–1275.
[13] X. Wang, W. Lu, M. Ester, C. Wang, and C. Chen, “Social recommen- dation with strong and weak ties,” in Proceedings of the 25th ACM In- ternational on Conference on Information and Knowledge Management. ACM, 2016, pp. 5–14.
[14] L. Xiao, Z. Min, Z. Yongfeng, L. Yiqun, and M. Shaoping, “Learning and transferring social and item visibilities for personalized recommen- dation,” in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 2017, pp. 337–346.
[15] M. Jamali and M. Ester, “Trustwalker: a random walk model for combining trust-based and item-based recommendation,” in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2009, pp. 397–406.
[16] H. Ma, H. Yang, M. R. Lyu, and I. King, “Sorec: social recommendation using probabilistic matrix factorization,” in Proceedings of the 17th ACM conference on Information and knowledge management. ACM, 2008, pp. 931–940.
[17] Y. Shen and R. Jin, “Learning personal+ social latent factor model for social recommendation,” in Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012, pp. 1303–1311.
[18] B. Yang, Y. Lei, J. Liu, and W. Li, “Social collaborative filtering by trust,” IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 8, pp. 1633–1647, 2017.
[19] M. Jamali and M. Ester, “A matrix factorization technique with trust propagation for recommendation in social networks,” in Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010, pp. 135–142.
[20] X. Wang, S. C. Hoi, M. Ester, J. Bu, and C. Chen, “Learning personal- ized preference of strong and weak ties for social recommendation,” in Proceedings of the 26th International Conference on World Wide Web, 2017, pp. 1601–1610.
[21] H.Ma,I.King,andM.R.Lyu,“Learningtorecommendwithsocialtrust ensemble,” in Proceedings of the 32nd international ACM SIGIR con- ference on Research and development in information retrieval. ACM, 2009, pp. 203–210.
[22] J. Tang, H. Gao, and H. Liu, “mtrust: discerning multi-faceted trust in a connected world,” in Proceedings of the fifth ACM international conference on Web search and data mining. ACM, 2012, pp. 93–102.
[23] A. J. Chaney, D. M. Blei, and T. Eliassi-Rad, “A probabilistic model for using social networks in personalized item recommendation,” in Proceedings of the 9th ACM Conference on Recommender Systems. ACM, 2015, pp. 43–50.
[24] Y. Bao, H. Fang, and J. Zhang, “Leveraging decomposed trust in prob- abilistic matrix factorization for effective recommendation,” in AAAI, 2014, p. 350.
[25] E. Tulving, “Episodic memory: From mind to brain,” Annual review of psychology, vol. 53, no. 1, pp. 1–25, 2002.
[26] U. M. Dholakia and D. Talukdar, “How social influence affects con- sumption trends in emerging markets: An empirical investigation of the consumption convergence hypothesis,” Psychology & Marketing, vol. 21, no. 10, pp. 775–797, 2004.
[27] S. Niwattanakul, J. Singthongchai, E. Naenudorn, and S. Wanapu, “Using of jaccard coefficient for keywords similarity,” in Proceedings of the International MultiConference of Engineers and Computer Scientists, vol. 1, no. 6, 2013.
[28] J. J. Louviere and G. Woodworth, “Design and analysis of simulated consumer choice or allocation experiments: an approach based on aggregate data,” Journal of marketing research, pp. 350–367, 1983.
[29] G. Palla, I. Dere ́nyi, I. Farkas, and T. Vicsek, “Uncovering the overlap- ping community structure of complex networks in nature and society,” Nature, vol. 435, no. 7043, pp. 814–818, 2005.
[30] M. D. Hoffman, D. M. Blei, C. Wang, and J. Paisley, “Stochastic variational inference,” The Journal of Machine Learning Research, vol. 14, no. 1, pp. 1303–1347, 2013.
[31] T. S. Jaakkola and M. I. Jordan, “Bayesian parameter estimation via variational methods,” Statistics and Computing, vol. 10, no. 1, pp. 25– 37, 2000.
[32] J.M.Herna ́ndez-Lobato,N.Houlsby,andZ.Ghahramani,“Stochastic inference for scalable probabilistic modeling of binary matrices,” in International Conference on Machine Learning, 2014, pp. 379–387.
[33] A. Honkela, M. Tornio, T. Raiko, and J. Karhunen, “Natural conjugate gradient in variational inference,” in International Conference on Neural Information Processing. Springer, 2007, pp. 305–314.