标题: 以网格为基础的连续兴趣点推荐系统
Grid-based Successive Point-of-interest Recommendation
作者: 高洪诣
黄俊龙
Gau, Hung-Yi
Huang, Jiun-Long
资讯科学与工程研究所
关键字: 兴趣点;推荐系统;适地性社群网路;打卡资料;网格;个人化网页排名;Point-of-interest;Recommendation System;Location-based social networks;Check-in data;Grid;Personalized Pagerank
公开日期: 2016
摘要: 随着携带式装置的普及与发展,人们的生活习惯也逐渐在改变,使用者在社群网路上利用打卡来分享到访某个地点的经验以及相关资讯已成为生活中的一部分。随着社群网路在打卡资料的累积,兴趣点 (Point-of-interest, POI) 推荐系统也因应而生。透过分析过去的打卡偏好和行为来帮助使用者探索周遭,能够为使用者提供更好的到访体验。而现今兴趣点推荐已被广泛且深入的研究,但针对使用者目前位置来进行推荐的方法仍有改善的空间,虽然考量兴趣点间距离远近的影响,却忽略了兴趣点的区域性质。因此我们提出一套以网格为基础的连续兴趣点的推荐系统,透过经纬度将这些兴趣点切成各个方块状的网格来近似区域性的概念,进而评估使用者的区域性偏好。除此之外,我们将 Edge-weighted Personalized PageRank (EdgePPR) 应用到兴趣点间的连续打卡转移关系中,由使用者偏好、区域偏好、连续打卡偏好来构成我们的推荐系统。我们的资料来源为 Gowalla 和 Brightkite,为着名的适地性社群网路 (Location-based social networks, LBSNs) 服务。实验结果显示我们的推荐系统比现有的连续兴趣点推荐系统更加准确。
With the rapid development of mobile devices, living habits of people gradually change.
Users are able to share their visiting experience to point-of-intrests (POI) on location-based social networks (LBSNs) by check-ins, which becomes a part of people's life.
With abundance of check-in data, POI recommendation service attracts a lot of interest. By analysing history check-in preferences and behaviors of users, we can not only help users to explore surroundings but also provide better visiting experience.
Nowadays, POI recommendation has been widely researched.
However, the recommendation methods that consider the current location of users, successive POI recommendation, still have room for improvement. Although the recent methods consider the geographical influence by measuring the distance, they ignore the regional influence of POI. Therefore, we propose a grid-based successive POI recommendation system, UGSE-LR.
To evaluate the region preferences of users, UGSE-LR splits geographic area into square-shaped grids for approximating the concept of regions.
Besides, UGSE-LR applies Edge-weighted personalized PageRank (EdgePPR) for modeling the sequential transitions between POIs.
Finally, UGSE-LR fuses user preferences, region preferences and sequential preferences into a unified recommendation framework.
Results on two real-world LBSNs datasets, Gowalla and Brightkite, show that our method are more accurate than the state-of-the-art successive POI recommendation methods.
URI: http://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070356034
http://hdl.handle.net/11536/139899
显示于类别:Thesis