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一种由长尾分布约束的推荐方法 被引量：10

A Long Tail Distribution Constrained Recommendation Method
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摘要 由于在线商品销售的长尾效应,冷门商品的总销量非常巨大,因而对冷门商品的推荐十分重要,然而由于对冷门商品的评价数量少,导致现存的推荐算法对其推荐权重接近平均推荐权重,所以很难使用户关注冷门商品,影响了冷门商品的销售,因此合理地提高冷门商品的推荐权重十分重要.提出一种由长尾分布约束的推荐方法(long tail distribution constrained recommendation method,LTDCR),由用户行为的相似度确定用户间相似关系,并应用不信任关系约束用户相似关系的传播,通过长尾分布约束由用户间相似关系计算的推荐权重,并给出一种精确描述长尾分布的方法.在包含大量冷门商品的数据集的实验结果表明,LTDCR在训练集较小的情况下,有效地提高了对冷门商品的推荐效果. The sales of on-line shopping follow the rule of long tail distribution, therefore the total sales of unpopular goods are very large. Recommendations for unpopular goods are as important as recommendations for popular goods. However, many existing recommendation methods only focus on the recommendations for popular goods, and assign an average weight of recommendation to unpopular goods which have small number of ratings, thus it is hard to bring unpopular goods to user＇s attention and the sales of unpopular goods are depressed. So it is very important to improve the weight of recommendation for unpopular goods. In this paper, a long tail distribution constrained recommendation （LTDCR） method is proposed for improving the weight of recommendation for unpopular goods appropriately. The weight of recommendation in LTDCR is calculated using similarity relationship among users, where the similarity relationship is determined by the similarity of users＇ behaviors and is propagated under the constraint of distrust relationship. In order to improve the weight of recommendation for unpopular goods, the weight of recommendation is constrained by the long tail distribution. An accurate description of long tail distribution is also given in this paper. The experimental results in dataset containing large number of unpopular goods show that LTDCR need fewer training set to improve the effectiveness of recommendations for unpopular goods.
作者 印桂生 张亚楠 董红斌 董宇欣
机构地区 哈尔滨工程大学计算机科学与技术学院
出处 《计算机研究与发展》 EI CSCD 北大核心 2013年第9期1814-1824,共11页 Journal of Computer Research and Development
基金 国家自然科学基金项目(61272186 61100007) 黑龙江省博士后基金项目(LBH-Z12068) 哈尔滨工程大学自由探索基金项目(HEUCF100608)
关键词 长尾分布 冷门商品 推荐权重 相似关系 不信任关系约束 long tail distribution unpopular goods weight of recommendation similarity relationship constraint of distrust relationship
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献20

  • 1Balabanovic M, Shoham Y. Fab: Content-based, collaborative recommendation[J]. Communications of the ACM, 1997, 40(3): 66-72.
  • 2Ma H, Yang H, Lyu M R, et al. Scree, Social recommendation using probabilistic matrix factorization[CJ IIProc of the 17th ACM Conf on Information and Knowledge Management. New York: ACM, 2008: 931-940.
  • 3Salakhutdinov R, Mnih A. Probabilistic matrix factorization[J]. Advances in Neural Information Processing Systems, 2008,200): 1257-1264.
  • 4amali M, Ester M. A matrix factorization technique with trust propagation for recommendation in social networks[CJ IIProc of the 4th ACM Conf on Recommender Systems. New York: ACM, 2010: 135-142.
  • 5BobadillaJ, Ortega F, Hernando A, et al, A collaborative filtering approach to mitigate the new user cold start problem[J]. Knowledge-Based Systems, 2012, 26(10): 225-238.
  • 6朱锐,王怀民,冯大为.基于偏好推荐的可信服务选择[J].软件学报,2011,22(5):852-864. 被引量：69
  • 7罗辛,欧阳元新,熊璋,袁满.通过相似度支持度优化基于K近邻的协同过滤算法[J].计算机学报,2010,33(8):1437-1445. 被引量：126
  • 8Ma H, King I, Lyu M R. Effective missing data prediction for collaborative filtering[CJ IIProc of the 30th Annual Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2007: 39-46.
  • 9WangJ, de Vries A p, Reinders MJ T. Unifying user-based and item-based collaborative filtering approaches by similarity fusion[CJ IIProc of the 29th Annual lnt ACM SIGlR Conf on Research and Development in Information Retrieval. New York: ACM, 2006: 501-508.
  • 10Brynjolfsson E, HuJeffrey v, Smith M. From niches to riches anatomy of the long tail[J]. Sloan Management Review, 2006, 47(4): 67-71.

二级参考文献97

共引文献343

同被引文献60

  • 1杨博,赵鹏飞.推荐算法综述[J].山西大学学报（自然科学版）,2011,34(3):337-350. 被引量：87
  • 2蔺丰奇,刘益.信息过载问题研究述评[J].情报理论与实践,2007,30(5):710-714. 被引量：65
  • 3BOBADILLA J S, ORTEGA F, HERNANDO A, et al. A collaborative filtering approach to mitigate the new user cold start problem [J]. KnowledgeBased Systems, 2012, 26(1): 225-238.
  • 4LIKA B, KOLOMVATSOS K, HADJIEFTHYMIADES S. Facing the cold start problem in recommender systems [J]. Expert Systems with Applications, 2014, 41(4): 20652073.
  • 5REN Y L, LI G, ZHOU W L. PRICAI 2012: Trends in Artificial Intelligence[M]. Berlin Heidelberg: Springer, 2012: 887-890.
  • 6LING Y X, GUO D K, CAI F, et al. Userbased Clustering with TopN Recommendation on ColdStart Problem[C]∥Proceedings of the 2013 3rd international conference on intelligent system design and engineering applications. Hong Kong:IEEE Computer Society, 2013: 1585-1589.
  • 7LOPS P, DE GEMMIS M, SEMERARO G. Recommender systems handbook [M]. Berlin Heidelberg: Springer, 2011: 73-105.
  • 8YIN H, CUI B, CHEN L, et al. A temporal contextaware model for user behavior modeling in social media systems[C]∥Proceedings of the 2014 ACM SIGMOD international conference on Management of data. Snowbird, USA: ACM, 2014: 1543-1554.
  • 9WANG J, DE VRIES A P, REINDERS M J T. Unifying userbased and itembased collaborative filtering approaches by similarity fusion[C]∥Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. Washington, USA: ACM, 2006: 501-508.
  • 10JAMALI M, ESTER M. A matrix factorization technique with trust propagation for recommendation in social networks[C]∥Proceedings of the 4th ACM conference on Recommender systems. Barcelona, Spain: ACM, 2010: 135-142.

引证文献10

二级引证文献33

计算机研究与发展
2013年 第9期

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