您的位置:山东大学 -> 科技期刊社 -> 《山东大学学报(工学版)》

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

• • 上一篇    下一篇

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. 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 Yimin1,2*, LI Fang1,2   

  1. 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: 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
[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.
[1] 王婷婷,翟俊海,张明阳,郝璞. 基于HBase和SimHash的大数据K-近邻算法[J]. 山东大学学报(工学版), 2018, 48(3): 54-59.
[2] 何正义,曾宪华,郭姜. 一种集成卷积神经网络和深信网的步态识别与模拟方法[J]. 山东大学学报(工学版), 2018, 48(3): 88-95.
[3] 崔晓松,王颖,孟佳, 邹丽. 基于语言值相似度推理的网络商家自评价方法[J]. 山东大学学报(工学版), 2018, 48(1): 1-7.
[4] 姚宇,冯健,张化光,韩克镇. 一种基于椭球体支持向量描述的异常检测方法[J]. 山东大学学报(工学版), 2017, 47(5): 195-202.
[5] 李素姝,王士同,李滔. 基于LS-SVM与模糊补准则的特征选择方法[J]. 山东大学学报(工学版), 2017, 47(3): 34-42.
[6] 刘英霞,王希常,唐晓丽,常发亮. 基于小波域特征和贝叶斯估计的目标检测算法[J]. 山东大学学报(工学版), 2017, 47(2): 63-70.
[7] 何正义,曾宪华,曲省卫,吴治龙. 基于集成深度学习的时间序列预测模型[J]. 山东大学学报(工学版), 2016, 46(6): 40-47.
[8] 王梅,曾昭虎,孙莺萁,杨二龙,宋考平. 基于输入K-近邻的正则化路径上SVR贝叶斯组合[J]. 山东大学学报(工学版), 2016, 46(6): 8-14.
[9] 陈泽华,尚晓慧,柴晶. 基于混合Hausdorff距离的多示例学习近邻分类器[J]. 山东大学学报(工学版), 2016, 46(6): 15-22.
[10] 黄丹,王志海,刘海洋. 一种局部协同过滤的排名推荐算法[J]. 山东大学学报(工学版), 2016, 46(5): 29-36.
[11] 莫小勇,潘志松,邱俊洋,余亚军,蒋铭初. 基于在线特征选择的网络流异常检测[J]. 山东大学学报(工学版), 2016, 46(4): 21-27.
[12] 庞俊涛, 张晖, 杨春明, 李波, 赵旭剑. 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.
[13] 翟俊海,张素芳,胡文祥,王熙照. 核心集径向基函数极限学习机[J]. 山东大学学报(工学版), 2016, 46(2): 1-5.
[14] 江峰,杜军威,刘国柱,眭跃飞. 基于加权的K-modes聚类初始中心选择算法[J]. 山东大学学报(工学版), 2016, 46(2): 29-34.
[15] 朱红, 丁世飞. 变粒度二次聚类方法[J]. 山东大学学报(工学版), 2015, 45(3): 1-6.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 李可,刘常春,李同磊 . 一种改进的最大互信息医学图像配准算法[J]. 山东大学学报(工学版), 2006, 36(2): 107 -110 .
[2] 岳远征. 远离平衡态玻璃的弛豫[J]. 山东大学学报(工学版), 2009, 39(5): 1 -20 .
[3] 王勇, 谢玉东.

大流量管道煤气的控制技术研究

[J]. 山东大学学报(工学版), 2009, 39(2): 70 -74 .
[4] 刘新1 ,宋思利1 ,王新洪2 . 石墨配比对钨极氩弧熔敷层TiC增强相含量及分布形态的影响[J]. 山东大学学报(工学版), 2009, 39(2): 98 -100 .
[5] 赵治广,王登杰,田云飞 . 基于灰色理论的路基沉降研究[J]. 山东大学学报(工学版), 2007, 37(3): 86 -88 .
[6] 庞志俭 张长桥. 甲基丙烯酸十二酯基二元共聚制备缔合减阻剂的合成与性能研究[J]. 山东大学学报(工学版), 2009, 39(5): 128 -132 .
[7] 陈朋 胡文容 裴海燕. 一株反硝化细菌LZ-14的筛选及其脱氮特性[J]. 山东大学学报(工学版), 2009, 39(5): 133 -138 .
[8] 孟健, 李贻斌, 李彬. 四足机器人跳跃步态控制方法[J]. 山东大学学报(工学版), 2015, 45(3): 28 -34 .
[9] 张光庆,孔凡玉,李大兴, . Koblitz曲线上抵抗简单功耗分析的有效算法[J]. 山东大学学报(工学版), 2007, 37(3): 78 -80 .
[10] 许延生,刘兴芳 . 模糊聚类迭代模型在水资源承载能力评价中的应用[J]. 山东大学学报(工学版), 2007, 37(3): 100 -104 .