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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

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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

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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
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