基于位置的非对称相似性度量的协同过滤推荐算法

doi:10.11772/j.issn.1001-9081.2016.01.0171

计算机应用 ›› 2016, Vol. 36 ›› Issue (1): 171-174.DOI: 10.11772/j.issn.1001-9081.2016.01.0171

基于位置的非对称相似性度量的协同过滤推荐算法

王付强, 彭甫镕, 丁小焕, 陆建峰

南京理工大学计算机科学与工程学院, 南京 210094

收稿日期:2015-07-16 修回日期:2015-09-15 出版日期:2016-01-10 发布日期:2016-01-09
通讯作者: 陆建峰(1969-),男,江苏无锡人,教授,博士,主要研究方向:智能系统、数据挖掘
作者简介:王付强(1989-),男,山东费县人,硕士研究生,主要研究方向:数据挖掘;彭甫镕(1987-),男,贵州遵义人,博士研究生,主要研究方向:数据挖掘、推荐系统;丁小焕(1991-),女,浙江衢州人,硕士研究生,主要研究方向:数据挖掘。
基金资助:
江苏省六大人才高峰项目。

Location-based asymmetric similarity for collaborative filtering recommendation algorithm

WANG Fuqiang, PENG Furong, DING Xiaohuan, LU Jianfeng

School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing Jiangsu 210094, China

Received:2015-07-16 Revised:2015-09-15 Online:2016-01-10 Published:2016-01-09
Supported by:
This work is partially supported by the Six Talents Peak Project in Jiangsu Province.

摘要/Abstract

摘要： 为提升推荐系统的准确率,针对传统协同过滤(CF)推荐算法没有有效使用位置信息的问题,提出了一种基于位置的非对称相似性度量的协同过滤推荐算法(LBASCF)。首先,分别利用用户-商品评分矩阵和用户历史消费位置,计算出用户间的余弦相似性和基于位置的非对称相似性;其次,将余弦相似性与基于位置的相似性融合,得到一个新的非对称用户相似性,融合后的相似性能够同时反映用户在位置上和兴趣上的偏好;最后,根据用户的最近邻居对商品的评分向用户推荐新的商品。用某点评数据集和Foursquare数据集对算法的有效性进行了评估。在某点评数据集实验结果证明,与CF相比,LBASCF的召回率和精确率分别提高了1.64%和0.37%;与位置感知协同过滤推荐系统(LARS)方法比较,LBASCF的召回率和精确率分别提高了1.53%和0.35%。实验结果表明,LBASCF相对于CF和LARS在基于位置服务的应用中能够有效提高系统的推荐质量。

关键词: 协同过滤, 基于位置服务, 个性化推荐, 位置感知, 基于位置的用户相似性

Abstract: To improve the accuracy of the recommendation system, a Location-Based Asymmetric Similarity for Collaborative Filtering (LBASCF) recommendation algorithm was proposed for the problem that traditional Collaborative Filtering (CF) recommendation algorithm does not consider the location information. Firstly, the cosine similarity and the Location-Based Asymmetric Similarity (LBAS) between users were calculated by the user-item rating matrix and the user's historical consumption location; secondly, a new user similarity measure was obtained by fusing the cosine similarity and location-based similarity. The blended similarity could reflect the user's preferences in both location and interest. Finally, based on the ratings of the user's nearest neighbors, new items were recommended to the user. The effectiveness of the algorithm was evaluated by using a dianping dataset and Foursquare dataset. The experimental results on the dianping dataset show that, compared with CF, the recall and precision of LBASCF were increased by 1.64% and 0.37% respectively; compared with the Location-Aware Recommender System (LARS), the recall and precision of LBASCF were increased by 1.53% and 0.35% respectively. The experimental results show that LBASCF can achieve better recommendation quality of the system based on the application of location-based services than CF and LARS.

Key words: Collaborative Filtering (CF), location-based service, personalized recommendation, location-aware, location-based user similarity

中图分类号:

TP311

王付强, 彭甫镕, 丁小焕, 陆建峰. 基于位置的非对称相似性度量的协同过滤推荐算法[J]. 计算机应用, 2016, 36(1): 171-174.

WANG Fuqiang, PENG Furong, DING Xiaohuan, LU Jianfeng. Location-based asymmetric similarity for collaborative filtering recommendation algorithm[J]. Journal of Computer Applications, 2016, 36(1): 171-174.

参考文献

[1] LIU B, XIONG H, PAPADIMITRIOU S, et al. A general geographical probabilistic factor model for point of interest recommendation [J]. IEEE transactions on knowledge and data engineering, 2015, 27(5): 1167-1179.
[2] WAN C, KAO B, CHEUNG D W, et al. Location-sensitive re-sources recommendation in social tagging systems [C]// Proceedings of the 21th ACM Conference on Information and Knowledge Management. New York: ACM, 2012: 1960-1964.
[3] CHO E, MYERS S A, LESKOVEC J. Friendship and mobility: user movement in location-based social networks [C]// KDD'11: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2011: 1082-1090.
[4] BAO J, ZHENG Y, MOKBEL M F. Location-based and preference-aware recommendation using sparse geo-social networking data [C]// Proceedings of the 20th International Conference on Advances in Geographic Information Systems. New York: ACM, 2012: 199-208.
[5] CACHEDA F, CARMEIRO V, FEMANDEZ D, et al. Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender systems [J]. ACM transactions on the Web, 2011, 5(1): Article No. 2.
[6] 梁慧.基于位置服务的电子商务推荐算法研究[D].厦门:厦门大学,2014.(LIANG H. The research on the recommendation technology of e-commerce based on LBS [D]. Xiamen: Xiamen University, 2014.)
[7] 孙小华.协同过滤系统的稀疏性与冷启动问题研究[D].杭州:浙江大学,2005.(SUN X H. Research of sparsity and cold start problem in collaborative filtering [D]. Hangzhou: Zhejiang University, 2005.)
[8] YE M, YIN P, LEE W, et al. Exploiting geographical influence for collaborative point-of-interest recommendation [C]// SIGIR'11: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2011: 325-334.
[9] BERJANI B, STRUFE T. A recommendation system for spots in location-based online social networks [C]// SNS'11: Proceedings of the 4th Workshop on Social Network Systems. New York: ACM, 2011: Article No. 4.
[10] YE M, YIN P, LEE W. Location recommendation for location-based social networks [C]// GIS'10: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York: ACM, 2010: 458-461.
[11] PARK M, HONG J, CHO S. Location-based recommendation system using Bayesian user's preference model in mobile devices [C]// UIC 2007: Proceedings of the 4th International Conference on Ubiquitous Intelligence and Computing. Berlin: Springer, 2007: 1130-1139.
[12] YUAN Q, CONG G, MA Z, et al. Time-aware point-if-interest recommendation [C]// SIGIR'13: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2013: 363-372.
[13] LEVANDOSKI J J, SARWAT M, ELDAWY A, et al. LARS: a location-aware recommender system [C]// ICDE'12: Proceedings of the 2012 IEEE 28th International Conference on Data Engineering. Washington, D.C.: IEEE Computer Society, 2012: 450-461.
[14] LIU H, HU Z, MIAN A, et al. A new user similarity model to improve the accuracy of collaborative filtering [J]. Knowledge-based systems, 2014, 56: 156-166.
[15] 邓爱林,朱扬勇,施伯乐.基于项目评分预测的协同过滤推荐算法[J].软件学报,2003,14(9):1621-1628.(DENG A L, ZHU Y Y, SHI B L. A collaborative filtering recommendation algorithm based on item rating prediction [J]. Journal of software, 2003, 14(9): 1621-1628.)

基于位置的非对称相似性度量的协同过滤推荐算法

Location-based asymmetric similarity for collaborative filtering recommendation algorithm

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