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

  • 朱郁筱，吕琳媛，推荐系统评价指标综述，电子科技大学学报（已接收）.

8.用户界面与用户体验

  • R. Sinha, K. Swearingen, The role of transparency in recommender systems, in: Proceedings of the CHI’06 Conference on Human Factors in Computing Systems, 2002, pp. 830-831.
  • A. D. J. Cooke, H. Sujan, M. Sujan, B. A. Weitz, Marketing the unfamiliar: the role of context and item-specific information in electronic agent recommendations, Journal of Marketing Research 39 (2002) 488-497.

9.多维数据的交叉利用

  • S. V. Buldyrev, R. Parshani, G. Paul, H. E. Stanley, S. Havlin, Catastrophic cascade of failures in interdependent networks, Nature 464 (2010) 1025-1028.
  • C.-G. Gu, S.-R. Zou, X.-L. Xu, Y.-Q. Qu, Y.-M. Jiang, D.-R. He, H.-K. Liu, T. Zhou, Onset of cooperation between layered networks, Physical Review E 84 (2011) 026101.
  • M. Mognani, L. Rossi, The ML-model for multi-layer social networks, 2011 International Conference on Advances in Social Networks Analysis and Mining, IEEE Press, 2011, pp. 5-12.
  • S. J. Pan, Q. Yang, A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering 22 (2010) 1345-1359.
  • 张亮，柏林森，周涛，基于跨电商行为的交叉推荐算法，电子科技大学学报（已接收）.
  • T. Zhou, L. Zhang, L.-S. Bai, L. Gong, C. Li, S. Huang, M.-S. Shang, S. Guo, Crossing Recommendation via Local Diffusion (unpublished).

10.社会推荐

  • R. Sinha, K. Swearingen, Comparing recommendations made by online systems and friends, in: Proceedings of the DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries, 2001.
  • M.J. Salganik, P.S.Dodds,D.J.Watts, Experimental study of inequality and unpredictability in an artificial culturalmarket, Science 311 (2006) 854-856.
  • P. Bonhard,M.A. Sasse, “Knowingme knowing you”—using profiles and social networking to improve recommender systems, BT Technology Journal 24 (2006) 84-98.
  • S.-Y. Hwang, C.-P. Wei, Y.-F. Liao, Coauthorship networks and academic literature recommendation, Electronic Commerce Research and Applications 9 (2010) 323-334.
  • P. Symeonidis, E. Tiakas, Y.Manolopoulos, Product recommendation and rating prediction based onmulti-modal social networks, in: Proceedings of the 5th ACM Conference on Recommender Systems, ACM Press, New York, 2011, pp. 61-68.
  • J. Leskovec, L.A. Adamic, B.A. Huberman, The dynamics of viral marketing, ACM Transactions on Web 1 (2007) 5.
  • J. Huang, X.-Q. Cheng, H.-W. Shen, T. Zhou, X. Jin, Exploring social influence via posterior effect ofword-of-mouth recommendations, in: Proceedings of the 5th ACM International Conference onWeb Search and Data Mining, ACM Press, New York, 2012, pp. 573-582.
  • S. Guo, M. Wang, J. Leskovec, The role of social networks in online shopping: information passing, price of trust, and consumer choice, in: Proceedings of the 12th ACM Conference on Electronic Commerce, ACM Press, New York, 2011, pp. 157-166.
  • M. Medo, Y.-C. Zhang, T. Zhou, Adaptive model for recommendation of news, EPL 88 (2009) 38005.
  • T. Zhou, M. Medo, G. Cimini, Z.-K. Zhang, Y.-C. Zhang, Emergence of scale-free leadership strcuture in social recommender systems, PLoS ONE 6 (2011) e20648.
  • J. O’Donovan, B. Smyth, Trust in recommender systems, Proceedings of the 10th international conference on Intelligent user interfaces, ACM Press, 2005, pp. 167-174.
  • P. Massa, P. Avesani, Trust-aware recommender systems, in: Proceedings of the 2007 ACM conference on Recommender systems, ACM Press, 2007, pp. 17-24.
  • J. He, W. W. Chu, A social network-based recommender system (SNRS), Annals of Information Systems 12 (2010) 47-74.

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在此感谢原作者的贡献～！

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