计算机科学 ›› 2014, Vol. 41 ›› Issue (7): 270-274.doi: 10.11896/j.issn.1002-137X.2014.07.056
刘慧婷,岳可诚
LIU Hui-ting and YUE Ke-cheng
摘要： 推荐系统帮助用户在海量信息中找到与用户相关的、个性化的产品,现有推荐技术大多致力于改进推荐系统的预测准确度。最近,推荐质量的另一个重要方面——推荐的多样性,越来越受到人们的重视。提出了一种基于物品推荐期望的top-N推荐方法,在向用户进行top-N推荐时,可以通过控制全体物品的推荐期望,来达到提高推荐总体多样性的目的。 结合多种评价方法,使用不同的评分预测算法在真实的电影评分数据集上对提出的算法 进行了实验,结果证明提出的算法能够在保证推荐准确度的同时,显著提高推荐的总体多样性。
| [1] Herlocker J L,Konstan J A,Terveen L G,et al．Evaluating Col-laborative Filtering Recommender Systems[J]．ACM Transactions on Information Systems,2004,22(1):5-53 [2] Koren Y,Bell R,Volinsky C．Matrix Factorization Techniques For Recommender Systems[J]．IEEE Computer,2009,42(8):30-37 [3] Guy Shani,Gunawardana A．Evaluating Recommendation Systems[M]∥Recommender Systems Handbook．Springer,2011:257-297 [4] Adomavicius G,Kwon Y．Improving Aggregate Recommenda-tion Diversity Using Ranking-Based Techniques[J]．IEEE Transactions on Knowledge and Data Engineering,2012,24(5):896-911 [5] Bradley K,Smyth B．Improving Recommendation Diversity[C]∥Proceeding of 12th Irish Conference on Artificial Intelligence and Cognitive Science．Ireland,2001 [6] Brynjolfsson E,Hu Y J,Simester D．Goodbye Pareto Principle,Hello Long Tail:The Effect of Search Costs on the Concentration of Product Sales[J]．Management Science,2011,57(8):1373-1386 [7] Fleder D,Hosanagar K．Blockbuster Culture’s Next Rise or Fall:The Impact of Recommender Systems on Sales Diversity[J]．Management Science,2009,55(5):697-712 [8] Zhang M,Hurley N．Avoiding monotony:improving the diversity of recommendation lists[C]∥Proceedings of the 2nd ACM Conference on Recommender Systems．Lausanne,2008 [9] Goldstein D G,Goldstein D C．Profiting from the Long Tail[J]．Harvard Business Review,2006,84(6):24-28 [10] Kim H K,Kim J K,Ryu Y U．A Local Scoring Model for Re-commendation[C]∥Proceedings of the 20th Workshop on Information Technologies and Systems．St．Louis,Missouri,USA,2010 [11] Park Y J,Tuzhilin A．The Long Tail of Recommender Systems and How to Leverage It[C]∥Proceedings of the 2nd ACM Conference on Recommender Systems．Lausanne,2008 [12] Adomavicius G,Kwon Y．Maximizing aggregate recommenda-tion diversity:a graph-theoretic approach[C]∥Proceedings of Workshop on Novelty and Diversity in Recommender Systems．Chicago,Illinois,USA,2011 [13] Cremonesi P,Koren Y,Turrin R．Performance of Recommender Algorithms on Top-N Recommendation Tasks[C]∥Proceedings of the fourth ACM conference on Recommender systems．Barcelona,2010 [14] Smyth B,McClave P．Similarity vs．Diversity[C]∥Proceedings of the 4th International Conference on Case-Based Reasoning:Case-Based Reasoning Research and Development．2001 [15] Shannon C E．A Mathematical Theory of Communication [J]．Reprinted with corrections from The Bell System Technical Journal,1948,27:379-423,623-656 [16] Brynjolfsson E,Hu Yu,Smith M D．Long Tails vs．Superstars:The Effect of Information Technology on Product Variety and Sales Concentration Patterns[J]．Information Systems Research,2010,21(4):736-747 [17] Adomavicius G,Tuzhilin A．Toward the Next Generation ofRecommender Systems:A Survey of the State-of-the-Art and Possible Extensions[J]．IEEE Transactions on Knowledge and Data Engineering,2005,17(6):734-749 [18] Sarwar B,Karypis G,Konstan J,et al.Item-Based Collaborative Filtering Recommendation Algorithm[C]∥Proceedings of WWW10．Hong Kong,2001 |
| No related articles found! |
|
||
首页