计算机科学 ›› 2016, Vol. 43 ›› Issue (12): 163-167, 178.doi: 10.11896/j.issn.1002-137X.2016.12.029

• 数据挖掘 • 上一篇    下一篇

基于用户签到和地理属性的个性化位置推荐算法研究

蔡海尼,陈程,文俊浩,王喜宾,曾骏   

  1. 重庆大学软件学院 重庆401331,重庆大学软件学院 重庆401331,重庆大学软件学院 重庆401331,重庆邮电大学软件工程学院 重庆400065,重庆大学软件学院 重庆401331
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金(61379158,61502062),科技支撑计划(2014BAH25F01),重庆市科技计划项目(cstc2014jcyjA40054)资助

Personalized Location Recommendation Algorithm Research Based on User Check-ins and Geographical Properties

CAI Hai-ni, CHEN Cheng, WEN Jun-hao, WANG Xi-bin and ZENG Jun   

  • Online:2018-12-01 Published:2018-12-01

摘要: 针对基于LBSNs (Location-based Social Networks)的位置推荐算法考虑因素单一且不能有效解决用户位于不同城市的位置推荐的问题,综合考虑潜在的社交影响、内容匹配影响和地理属性影响等因素,提出了基于用户签到和地理属性的个性化位置推荐算法SCL (Social-Content-Location)。该算法在协同过滤的基础上,引入了用户兴趣特征比较,改进了用户的相似度计算;同时,在分析位置的内容信息时,融入用户评论,缓解了位置标签的短文本特性对LDA (Latent Dirichlet Allocation)主题提取的影响,提高了用户兴趣和城市偏好主题提取的准确率。实验结果表明,SCL算法在本地城市召回率上较协同过滤算法U提高近65%,较LCA-LDA算法提高近 30%;在异地城市召回率上,高于LCA-LDA算法近26%。这表明SCL算法在不同城市下的位置推荐具有一定的可行性。

关键词: 潜在社交影响,内容匹配影响,地理属性影响,协同过滤,LDA主题提取

Abstract: Since the consideration of location recommendation algorithms based on LBSNs (Location-Based Social Networks) is too single,and it couldn’t effectively solve the problem of location recommendation for user in different ci-ties,synthesizing the factors of potential social influence,content match influence and geographical property influence,the personalized location recommendation algorithm SCL (Social-Content-Location) based on user check-ins and geographical properties was proposed. SCL algorithm introduces the comparison of users’ interest features based on the collaborative filtering,and it improves the similarity of users.At the same time,when the content information of location is analyzed, user’s comments on location is integrated,and it alleviates the influence of the short text feature of location labels to LDA (Latent Dirichlet Allocation) topic extraction and improves the accuracy of user’s interest and city pre-ference topic in extraction.The experimental results show that,for the recall rate of residence city,algorithm SCL outperforms collaborative filtering algorithm U near 65%,and outperforms algorithm LCA-LDA near 30%.For the recall rate of new city,algorithm SCL outperforms algorithm LCA-LDA near 26%,which shows that algorithm SCL has certain feasibility for location recommendation under different cities.

Key words: Potential social influence,Content match influence,Geographical property influence,Collaborative filtering,LDA topic extraction

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