? 本地达人会去何处?一种基于用户位置-话题相似度的Top-N地点推荐方法
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Journal of Computer Science and Technology 2018, Vol. 33 Issue (1) :190-206    DOI: 10.1007/s11390-017-1766-3
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本地达人会去何处?一种基于用户位置-话题相似度的Top-N地点推荐方法
Rong Wang, Tian-Lei Hu*, Gang Chen, Member, CCF, ACM, IEEE
Database Laboratory, College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
Where Do Local Experts Go? Evaluating User Geo-Topical Similarity for Top-N Place Recommendation
Rong Wang, Tian-Lei Hu*, Gang Chen, Member, CCF, ACM, IEEE
Database Laboratory, College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China

摘要
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摘要 最近几年,基于位置的社交网络得到了迅速增长.Top-N地点推荐,目的在于为每个用户推荐N个他最喜欢的新地点,它在近年来逐渐变得重要.然而传统的解决方法通常简单选取附近最流行的N个地点,或者利用协同过滤方法把每个地点当做一个离散项,忽视了地点之间的地理与语义关系.我们提出了GoTo方法,这是一种基于协同过滤的Top-N地点推荐方法.与传统的方法比起来,GoTo的主要思想是利用本地达人的访问历史实现高精度推荐.对于任何一个目标用户,他的本地达人是与他活动范围相近,且兴趣类似的用户.为了有效找出本地达人,我们设计了一种新型的用户相似度量,它结合了基于高斯混合模型的地理相似度量,以及基于话题模型的话题相似度量.我们的实验表明,通过引入本地达人的概念,GoTo能够大大提高传统协同过滤地点推荐方法的精确度.
关键词基于位置的社交网络   本地达人   地点推荐     
Abstract: Recent years have witnessed the ever-growing popularity of location-based social network (LBSN) services. Top-N place recommendation, which aims at retrieving N most appealing places for a target user, has thus gained increasing importance. Yet existing solutions to this problem either provide non-personalized recommendations by selecting nearby popular places, or resort to collaborative filtering (CF) by treating each place as an independent item, overlooking the geographical and semantic correlations among places. In this paper, we propose GoTo, a collaborative recommender that provides top-N personalized place recommendation in LBSNs. Compared with existing methods, GoTo achieves its effectiveness by exploiting the wisdom of the so-called local experts, namely those who are geographically close and have similar preferences with regard to a certain user. At the core of GoTo lies a novel user similarity measure called geo-topical similarity, which combines geographical and semantic correlations among places for discovering local experts. In specific, the geo-topical similarity uses Gaussian mixtures to model users' real-life geographical patterns, and extracts users' topical preferences from the attached tags of historically visited places. Extensive experiments on real LBSN datasets show that compared with baseline methods, GoTo can improve the performance of top-N place recommendation by up to 50% in terms of accuracy.
Keywordslocation-based social network (LBSN)   local expert   geo-topical similarity   top-N place recommendation     
Received 2016-09-19;
本文基金:

This work is supported by the National Natural Science Foundation of China under Grant No. M1552002 and the National High Technology Research and Development Program of China under Grant No. 2014AA015205.

通讯作者: Tian-Lei Hu     Email: htl@zju.edu.cn
About author: Rong Wang is a Ph.D. candidate in the Database Laboratory of College of Computer Science and Technology, Zhejiang University, Hangzhou. His research interests include data mining and information retrieval.
引用本文:   
Rong Wang, Tian-Lei Hu, Gang Chen.本地达人会去何处?一种基于用户位置-话题相似度的Top-N地点推荐方法[J]  Journal of Computer Science and Technology , 2018,V33(1): 190-206
Rong Wang, Tian-Lei Hu, Gang Chen.Where Do Local Experts Go? Evaluating User Geo-Topical Similarity for Top-N Place Recommendation[J]  Journal of Computer Science and Technology, 2018,V33(1): 190-206
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