华东师范大学学报（自然科学版） >

2019 , Vol. 2019 >Issue 3: 63 - 77

DOI: https://doi.org/10.3969/j.issn.1000-5641.2019.03.008

计算机科学

基于反向最远邻的商品推荐算法研究

  • 王凯 ,
  • 李博涵 ,
  • 万朔 ,
  • 张安曼 ,
  • 关东海
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  • 1. 南京航空航天大学 计算机科学与技术学院, 南京 211106;
    2. 软件新技术与产业化协同创新中心, 南京 211106;
    3. 江苏易图地理信息科技股份有限公司, 江苏 扬州 225000
王凯,男,硕士研究生,主要研究方向为时空数据库、推荐系统.E-mail:hxyrqx123@sina.com.

收稿日期: 2018-08-03

  网络出版日期: 2019-05-30

基金资助

国家自然科学基金（61728204，61672284，41301407）；南京航空航天大学科研基地创新基金（NJ20160028）；南京航空航天大学青年科技创新基金（NT2018028）；智能电网保护和运行控制国家重点实验室基金（国防预研领域）；江苏省高校优势学科建设工程资助项目；高安全系统的软件开发与验证技术工业和信息化部重点实验室项目（NJ2018014）

收起

Research on a commodity recommendation algorithm based on reverse furthest neighbor

  • WANG Kai ,
  • LI Bo-han ,
  • WAN Shuo ,
  • ZHANG An-man ,
  • GUAN Dong-hai
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  • 1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
    2. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 211106, China;
    3. Jiangsu Easymap Geographic Information Technology Corp. Ltd, Yangzhou Jiangsu 225000, China

Received date: 2018-08-03

  Online published: 2019-05-30

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摘要

有效的推荐算法可以最大限度地发掘商品的价值.通过研究用户的偏好，分析了从海量商品信息中为用户推荐感兴趣内容的方法.目前大多数推荐系统向用户推荐的是较为流行的商品，而忽略了那些当下不"热门"，却有着巨大潜力的商品.以发掘小众中的大众商品为目的，提出了一种基于反向最远邻（ReverseFurthest Neighbor，RFN）查询的商品推荐算法：基于专家用户的信任协同过滤算法，替代传统用户相似匹配的协同过滤推荐算法；利用幂律对商品进行范围缩减，优化系统筛选的效率，实现了对有潜在价值商品的推荐，使小众商品属性的分布得到更深层次的挖掘.实验结果表明本文推荐算法输出结果质量较高，适用于解决部分"长尾问题".

关键词： 推荐系统; 协同过滤; 反向最远邻; 幂律

本文引用格式

王凯 , 李博涵 , 万朔 , 张安曼 , 关东海 . 基于反向最远邻的商品推荐算法研究[J]. 华东师范大学学报（自然科学版）, 2019 , 2019(3) : 63 -77 . DOI: 10.3969/j.issn.1000-5641.2019.03.008

Abstract

Effective recommendation algorithms can help maximize the value of a product. By studying the user's preferences, we can recommend underlying contents for the user from mass merchandise information. However, most recommendation systems focus on popular products, ignoring those products that are currently not popular but have huge potential. Our recommendation system, based on reverse furthest neighbor (RFN) queries, uses the idea of mining popular products in niche markets. We improve the traditional collaborative filtering recommendation algorithm and adopt a collaborative filtering algorithm based on expert users. The modified algorithm can recommend products with potential value based on the power law, which makes the distribution of minority mined products more visible to users. Experimental results show that the quality of the proposed algorithm is high and is suitable for partially addressing the long tail problem.

Key words： recommendation system; collaborative filtering; reverse furthest neighbor (RFN); power law

参考文献

[1] BOBADILLA J, ORTEGA F, HERNANDO A. Recommender systems survey[J]. Knowledge-Based Systems, 2013, 46(1):109-132.
[2] XU H L, WU X, LI X D, et al. Comparison study of Internet recommendation system[J]. Journal of Software, 2009, 20(2):350-363.
[3] HUSSEIN T. Context-Aware Recommender Systems 2011[C]//ACM Conference on Recommender Systems. ACM, 2011:349-350.
[4] MENG X W, XUN H U, WANG L C, et al. Mobile recommender systems and their applications[J]. Journal of Software, 2013, 24(1):91-108.
[5] ANDERSON C. The long tail[J]. Wired Magazine, 2004, 12(10):170-177.
[6] NEWMAN M E J. Power laws, Pareto distributions and Zipf's law[J]. Contemporary Physics, 2005, 46(5):323-351.
[7] YANG X W, STECK H, GUO Y, et al. On top-k recommendation using social networks[C]//Proceedings of the 6th ACM Conference on Recommender Systems. ACM, 2012:67-74.
[8] KHRIBI M K, JEMNI M, NASRAOUI O. Automatic recommendations for e-learning personalization based on web usage mining techniques and information retrieval[C]//Advanced Learning Technologies, 2008, ICALT'08, 8th IEEE International Conference on. IEEE, 2008:241-245.
[9] ABEL F, GAO Q, HOUBEN G J, et al. Analyzing user modeling on twitter for personalized news recommendations[C]//International Conference on User Modeling, Adaptation, and Personalization. Berlin:Springer, 2011:1-12.
[10] YAO B, LI F F, KUMAR P. Reverse furthest neighbors in spatial databases[C]//IEEE, International Conference on Data Engineering. IEEE, 2009:664-675.
[11] LIU J Q, CHEN H X, FURUSE K, et al. An efficient algorithm for arbitrary reverse furthest neighbor queries[C]//Asia-Pacific Web Conference. Berlin:Springer, 2012:60-72.
[12] WANG S L, CHEEMA M A, LIN X M, et al. Efficiently computing reverse k furthest neighbors[C]//Data Engineering (ICDE), 2016 IEEE 32nd International Conference on. IEEE, 2016:1110-1121.
[13] GOLDBERG D, NICHOLS D, OKI B M, et al. Using collaborative filtering to weave an information tapestry[J]. Communications of the ACM, 1992, 35(12):61-70.
[14] SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms[C]//Proceedings of the 10th international conference on World Wide Web. ACM, 2001:285-295.
[15] XIAO P, SHAO L S, LI X R. Improved collaborative filtering algorithm in the research and application of personalized movie recommendations[C]//Intelligent Systems Design and Engineering Applications, 20134th International Conference on. IEEE, 2013:349-352.
[16] ZHAO Z D, SHANG M S. User-based collaborative-filtering recommendation algorithms on hadoop[C]//20103rd International Conference on Knowledge Discovery and Data Mining. IEEE, 2010:478-481.
[17] PIRASTEH P, JUNG J J, HWANG D. Item-based collaborative filtering with attribute correlation:A case study on movie recommendation[C]//Asian Conference on Intelligent Information and Database Systems. Cham:Springer, 2014:245-252.
[18] VOZALIS E G, MARGARITIS K G. Recommender systems:An experimental comparison of two filtering algorithms[C]//Proceedings of the 9th Panhellenic Conference in Informatics-PCI 2003. 2003.
[19] MA H F, JIA M H Z, ZHANG D, et al. Combining tag correlation and user social relation for microblog recommendation[J]. Information Sciences, 2017, 385/386:325-337.
[20] 郭娣, 赵海燕. 融合标签流行度和时间权重的矩阵分解推荐算法[J]. 小型微型计算机系统, 2016, 37(2):293-297.
[21] FALOUTSOS M, FALOUTSOS P, FALOUTSOS C. On power-law relationships of the internet topology[J]. ACM SIGCOMM Computer Communication Review, 1999, 29(4):251-262.
[22] CLEMENTI F, GALLEGATI M. Power law tails in the Italian personal income distribution[J]. Physica A:Statistical Mechanics and its Applications, 2005, 350(2/3/4):427-438.
[23] NEWMAN M E J. Power laws, Pareto distributions and Zipf's law[J]. Contemporary physics, 2005, 46(5):323-351.
[24] CLAUSET A, SHALIZI C R, NEWMAN M E J. Power-law distributions in empirical data[J]. SIAM review, 2009, 51(4):661-703.
[25] LI B H, ZHANG C, CHEN W H, et al. Dynamic reverse furthest neighbor querying algorithm of moving objects[C]//ADMA 2016:Advanced Data Mining and Applications. Cham:Springer, 2016:266-279.
[26] 郑伟, 李博涵, 王雅楠, 等. 融合偏好交互的组推荐算法模型[J]. 小型微型计算机系统, 2018, 2(39):372-378.
[27] CLAUSET A, SHALIZI C R, NEWMAN M E J. Power law distributions in empirical data[J]. SIAM Review, 2009, 51(4):661-703.
[28] 高珂. 基于消费者行为分析的电子商务市场研究[D]. 山东青岛:青岛理工大学, 2010.
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