基于多方面评分的景点协同推荐算法

doi:10.6040/j.issn.1672-3961.0.2016.311

山东大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (6): 54-61.doi: 10.6040/j.issn.1672-3961.0.2016.311

基于多方面评分的景点协同推荐算法

王志强¹,文益民^1,2*,李芳^1,2

1.桂林电子科技大学计算机信息与安全学院, 广西桂林 541004;2.广西可信软件重点实验室, 广西桂林 541004

收稿日期:2016-07-22 出版日期:2016-12-20 发布日期:2016-07-22
通讯作者: 文益民(1969— ),男,湖南益阳人,教授,博士,主要研究方向为机器学习,数据挖掘与推荐系统. E-mail:ymwen2004@aliyun.com E-mail:wzq2016@aliyun.com
作者简介:王志强(1991— ),男,湖北襄阳人,硕士研究生,主要研究方向为机器学习,数据挖掘与旅游推荐. E-mail:wzq2016@aliyun.com
基金资助:
国家自然科学基金资助项目(61363029);广西科学研究与技术开发资助项目(桂科攻14124005-2-1);广西自然科学基金资助项目(2014GXNSFAA118395);广西高校图像图形智能处理重点实验室课题资助项目(GIIP201505)

Collaborative recommendation for scenic spots based on multi-aspect ratings

WANG Zhiqiang¹, WEN Yimin^1,2*, LI Fang^1,2

1. School of Computer Information and Security, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China;
2. Guangxi Key Laboratory of Trusted Software, Guilin 541004, Guangxi, China

Received:2016-07-22 Online:2016-12-20 Published:2016-07-22

摘要/Abstract

摘要： 针对传统的协同过滤(collaborative filtering, CF)推荐模型中利用单一的总体评分进行相似性计算,但总体评分不能准确反映用户对物品喜好的问题,提出基于多方面评分的景点协同推荐算法。该算法综合利用用户对景点在景色、趣味性、性价比三个方面的评分计算用户或景点之间的相似性,进而计算目标用户对目标景点的总体评分。试验结果表明:在相似性计算中引入景点在这三个方面的评分信息后,推荐结果的均方根误差、平均绝对误差、覆盖率、准确率和F-度量指标都得到了改善。

关键词: 景点推荐, 多方面评分, 评分预测, 协同推荐, 相似性度量

Abstract: The simplex overall ratings are used to compute the similarities between users and items in the model of traditional collaborative filtering recommendation, but it can't correctly depict the users' true preferences. In order to solve this problem, a collaborative scenic spots recommendation algorithm based on multi-aspect ratings was proposed, which integrated the ratings of the scenery, interesting and cost performance of spots to compute the similarities to predict the overall ratings of an active user for a target spot. Experimental results showed that, after introducing the information of multi-aspect ratings, the proposed method improved the accuracy of prediction score, coverage and F-measure and reduced the predicting error of root-mean-square and mean-absolute.

Key words: rating prediction, similarity metrics, scenic spots recommendation, multi-aspect ratings, collaborative recommendation

中图分类号:

TP181

王志强,文益民,李芳. 基于多方面评分的景点协同推荐算法[J]. 山东大学学报(工学版), 2016, 46(6): 54-61.

WANG Zhiqiang, WEN Yimin, LI Fang. Collaborative recommendation for scenic spots based on multi-aspect ratings[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(6): 54-61.

参考文献

[1] 乔向杰,张凌云. 近十年国外旅游推荐系统的应用研究[J].旅游学刊,2014,29(8):117-127. QIAO Xiangjie, ZHANG Lingyun. Study on the application of foreign tourism recommendation system in recent ten years[J]. Tourism Tribune, 2014, 29(8):117-127.
[2] 文益民,史一帆,蔡国永,等.个性化旅游推荐研究综述[EB/OL].[2014-07-03].http://www.paper.edu.cn/releasepaper/content/201407-56.
[3] 史一帆,文益民.基于景点标签的协同过滤推荐[J].计算机应用,2014, 34(10):2854-2858. SHI Yifan, WEN Yimin. Collaborative filtering recommendation based on tags of scenic spots[J]. Journal of Computer Applications, 2014, 34(10): 2854-2858.
[4] 雷震,文益民,王志强,等.基于影响力控制的热传导算法[J].智能系统学报,2016, 11(3):328-335. LEI Zhen, WEN Yimin, WANG Zhiqiang, et al. Heat conduction controlled by the influence of users and items[J]. CAAI Transactions on Intelligent Systems, 2016, 11(3):328-335.
[5] 庞俊涛,张晖,杨春明,等.基于概率矩阵分解的多指标协同过滤算法[J].山东大学学报(工学版),2016,46(3):65-73. PANG Juntao, ZHANG Hui, YANG Chunming, et al. Multi-criteria collaborative filtering algorithm based on probabilistic matrix factorization[J]. Journal of Shandong University(Engineering Science), 2016, 46(3):65-73.
[6] MAGID A, CHEN L, CHEN G, et al. A context-aware personalized travel recommendation system based on geotagged social media data mining[J]. International Journal of Geographical Information Science, 2013, 27(4):662-684.
[7] 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.
[8] LINDEN G, SMITH B, YORK J. Amazon.com recommendations: item-to-item collaborative filtering[J]. IEEE Internet Computing, 2003, 7(1):76-80.
[9] DAS A S, DATAR M, GARG A, et al. Google news personalization: scalable online collaborative filtering[C] //Proceedings of the 16th International Conference on World Wide Web. New York, USA: ACM, 2007: 271-280.
[10] BENNETT J, ELKAN C, LIU B, et al. KDD Cup and workshop 2007[J]. ACM SIGKDD Explorations Newsletter, 2007, 9(2):51-52.
[11] RESNICK P, IACOVOU N, SUCHAK M, et al. GroupLens: an open architecture for collaborative filtering of netnews[C] // Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work. New York, USA: ACM, 1994: 175-186.
[12] 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. Hong Kong, China: ACM, 2001: 285-295.
[13] CHOI K, SUH Y. A new similarity function for selecting neighbors for each target item in collaborative filtering[J]. Knowledge-Based Systems, 2013, 37(1):146-153.
[14] LIU H F, HU Z, MIAN A, et al. A new user similarity model to improve the accuracy of collaborative filtering[J]. Knowledge-Based Systems, 2014, 56(3):156-166.
[15] ZHANG J, PENG Q, SUN S Q, et al. Collaborative filtering recommendation algorithm based on user preference derived from item domain features[J]. Physica A: Statistical Mechanics and its Applications, 2014, 396(15):66-76.
[16] YU X, WANG Z. A enhanced trust model based on social network and online behavior analysis for recommendation[C] // Proceedings of International Conference on Computational Intelligence and Software Engineering. Wuhan, China: IEEE, 2010: 1-4.
[17] CHANG Q, WANG X K, YIN D, et al. The new similarity measure based on user preference models for collaborative filtering[C] // Proceedings of the 2015 IEEE International Conference on Information and Automation. Lijiang, China: IEEE, 2015: 557-582.
[18] 文俊浩, 舒珊.一种改进相似性度量的协同过滤推荐算法[J]. 计算机科学, 2014,41(5):68-71. WEN Junhao, SHU Shan. Improved collaborative filtering recommendation algorithm of similarity measure[J]. Computer Science, 2014, 41(5):68-71.
[19] BREESE J S, HECKERMAN D, KADIE C. Empirical analysis of predictive algorithms for collaborative filtering[C] // Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence. Madison, USA: Morgan Kaufmann Publishers Inc, 1998: 43-52.
[20] 项亮.推荐系统实践[M].北京:人民邮电出版社,2012.
[21] 李聪,梁昌勇,马丽.基于领域最近邻的协同过滤推荐算法[J].计算机研究与发展,2008,45(9):1532-1538. LI Cong, LIANG Changyong, MA Li. A collaborative filtering recommendation algorithm based on domain nearest neighbor[J]. Journal of Computer Research and Development, 2008, 45(9):1532-1538.

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基于多方面评分的景点协同推荐算法

Collaborative recommendation for scenic spots based on multi-aspect ratings

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