个性化推荐的十大挑战，个性化十大挑战

个性化推荐的十大挑战

标签（空格分隔）： 大数据 个性化推荐 机器学习

1.数据稀疏问题

• Z. Huang, H. Chen, D. Zeng, Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering, ACM Transactions on Information Systems 22 (2004) 116-142.
• J. S. Breese, D. Heckerman, C. Kadie, Empirical analysis of predictive algorithms for collaborative filtering, in: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, 1998, pp. 43-52.
• J. Ren, T. Zhou, Y.-C. Zhang, Information filtering via self-consistent refinement, EPL 82 (2008) 58007.
• D. Sun, T. Zhou, J.-G. Liu, R.-R. Liu, C.-X. Jia, B.-H. Wang, Information filtering based on transferring similarity, Physical Review E 80 (2009) 017101

2.冷启动问题

• A. I. Schein, A. Popescul, L. H. Ungar, D. M. Pennock, Methods and metrics for cold-start recommendations, in: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM Press, New York, 2002, pp. 253-260.
• X. N. Lam, T. Vu, T. D. Le, A. D. Duong, Addressing cold-start problemin recommendation systems, in: Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, 2008, pp. 208-211.
• Z.-K. Zhang, C. Liu, Y.-C. Zhang, T. Zhou, Solving the cold-start problem in recommender systems with social tags, EPL 92 (2010) 28002.
• C.-J. Zhang, A. Zeng, Behavior patterns of online users and the effect on information filtering, Physica A 391 (2012) 1822-1830.

3.大数据处理和增量计算问题

• T. Zhou, J. Ren, M. Medo, Y.-C. Zhang, Bipartite network projection and personal recommendation, Physical Review E 76 (2007) 046115.
• B. Sarwar, J. Konstan, J. Riedl, Incremental singular value decomposition algorithms for highly scalable recommender systems, in: International Conference on Computer and Information Science, 2002, pp. 27-28.
• C.-H. Jin, J.-G. Liu, Y.-C. Zhang, T. Zhou, Adaptive information filtering for dynamics recommender systems, arXiv:0911.4910.

4.多样性与精确性的两难困境

• S.M. Mcnee, J. Riedl, J.A. Konstan, Being accurate is not enough: how accuracy metrics have hurt recommender systems, in: Proceedings of the CHI’06 Conference on Human Factors in Computing Systems, ACM Press, New York, 2006, pp. 1097-1101.
• B. Smyth, P. Mcclave, Similarity vs. diversity, in: D.W. Aha, I. Watson (Eds.), Case-Based Reasoning Research and Development, Springer, 2001, pp. 347-361.
• N. Ziegler, S.M. Mcnee, J.A. Konstan, G. Lausen, Improving recommendation lists through topic diversification, in: Proceedings of the 14th International Conference on World Wide Web, ACM Press, New York, 2005, pp. 22-32.
• N. Hurley,M. Zhang, Novelty and diversity in top-N recommendation—analysis and evaluation, ACMTransactions on Internet Technology 10 (2011) 14.
• T. Zhou, Z. Kuscsik, J.-G. Liu, M. Medo, J.R. Wakeling, Y.-C. Zhang, Solving the apparent diversity–accuracy dilemma of recommender systems, Proceedings of the National Academy of Sciences of the United States of America 107 (2010) 4511-4515.

5.推荐系统的脆弱性问题

• B. Mobasher, R. Burke, R. Bhaumik, C. Williams, Towards trustworthy recommender systems: an analysis of attackmodels and algorithm robustness, ACM Transactions on Internet Technology 7 (2007) 23.
• J. J. Sandvig, B. Mobasher, R. Burke, Robustness of collaborative recommendation based on association rule mining, in: Proceedings of the 2007 ACM Conference on Recommender Systems, ACM Press, 2007, pp. 105-112.
• S. K. Lam, D. Frankowski, J. Riedl, Do You Trust Your Recommendations? An Exploration of Security and Privacy Issues in Recommender Systems, in: Lecture Notes in Computer Science, vol. 3995, Springer, Heidelberg, Germany, 2006, pp. 14-29.
• P. Resnick, R. Sami, The influence limiter: provably manipulation-resistant recommender systems, in: Proceedings of the 2007 ACM Conference on Recommender Systems, ACM Press, 2007, pp. 25-32.
• C. Shi, M. Kaminsky, P. B. Gibbons, F. Xiao, DSybil: Optimal Sybil-Resistance for Recommendation Systems, IEEE Press, 2009, pp. 283-298.
• R. Burke, M. P. O’mahony, N. J. Hurley, Robust Collaborative Recommendation, in: F. Ricci, L. Rokach, B. Shapira, P. B. Kantor (Eds.), Recommender Systems Handbook, Part 5, Springer, 2011, pp. 805-835 (Chapter 25).

6.用户行为模式的挖掘和利用

• C.-J. Zhang, A. Zeng, Behavior patterns of online users and the effect on information filtering, Physica A 391 (2012) 1822-1830.
• T. Zhou, Z. Kuscsik, J.-G. Liu, M. Medo, J.R. Wakeling, Y.-C. Zhang, Solving the apparent diversity–accuracy dilemma of recommender systems, Proceedings of the National Academy of Sciences of the United States of America 107 (2010) 4511-4515.
• M.-S. Shang, L. Lü, Y.-C. Zhang, T. Zhou, Empirical analysis of web-based user-object bipartite networks, EPL 90 (2010) 48006.
• S.-H. Min, I. Han, Detection of the customer time-variant pattern for improving recommender systems, Expert Systems with Applications 28 (2005) 189-199.
• L. Xiang, Q. Yuan, S. Zhao, L. Chen, X. Zhang, Q. Yang, J. Sun, Temporal recommendation on graphs via long-and short-term preference fusion, in: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, New York, 2010, pp. 723-732.
• N. N. Liu, M. Zhao, E. Xiang, Q. Yang, Online evolutionary collaborative filtering, in: Proceedings of the 4th ACM Conference on Recommender Systems, ACM Press, New York, 2010, pp. 95-102.
• J. Liu, G. Deng, Link prediction in a user-object network based on time-weighted resource allocation, Physica A 39 (2009) 3643-3650.
• Y. Koren, Collaborative filtering with temporal dynamics, in: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, New York, 2009, pp. 447-456.
• C. Song, Z. Qu, N. Blumm, A.-L. Barabási, Limits of predictability in human mobility, Science 327 (2010) 1018-1021.
• E. Cho, S.A. Myers, J. Leskovec, Friendship and mobility: user movement in location-based social networks, in: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, New York, 2011, pp. 1082-1090.
• V. W. Zheng, Y. Zheng, X. Xie, Q. Yang, Collaborative location and activity recommendations with GPS history data, in: Proceedings of the 19th International Conference on World Wide Web, ACM Press, New York, 2010, pp. 1029-1038.
• M. Clements, P. Serdyukov, A. P. De Vries, M. J. T. Reinders, Personalised travel recommendation based on location co-occurrence, arXiv:1106.5213.
• Y. Koren, J. Sill, OrdRec: An ordinal model for predicting personalized item rating distributions, Proc. 5th ACM Conference on Recommender Systems, ACM Press, New York, 2011, pp. 117-124.
• Z. Yang, Z.-K. Zhang, T. Zhou, Uncovering Voting Patterns in Recommender Systems (unpublished).
• J. Vig, S. Sen, J. Riedl, Navigation the tag genome, in: Proceedings of the 16th International Conference on Intelligent User Interfaces, ACM Press, New York, 2011, pp. 93-102.
• L. Chen, P. Pu, Critiquing-based recommenders: survey and emerging trends, User Modeling and User-Adapted Interaction 22 (2012) 125-150.

7.推荐系统效果评估