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STCAPLRS: A Spatial-Temporal Context-Aware Personalized Location Recommendation System

文献类型：期刊论文

作者Fang, Quan1; Xu, Changsheng1; Hossain, M. Shamim2; Muhammad, G.3
刊名ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
出版日期2016-07-01
卷号7期号:4页码:30
关键词Algorithms Experimentation Performance Location Recommendation Topic Model Matrix Factorization
DOI10.1145/2842631
文献子类Article
英文摘要Newly emerging location-based social media network services (LBSMNS) provide valuable resources to understand users' behaviors based on their location histories. The location-based behaviors of a user are generally influenced by both user intrinsic interest and the location preference, and moreover are spatial-temporal context dependent. In this article, we propose a spatial-temporal context-aware personalized location recommendation system (STCAPLRS), which offers a particular user a set of location items such as points of interest or venues (e.g., restaurants and shopping malls) within a geospatial range by considering personal interest, local preference, and spatial-temporal context influence. STCAPLRS can make accurate recommendation and facilitate people's local visiting and new location exploration by exploiting the context information of user behavior, associations between users and location items, and the location and content information of location items. Specifically, STCAPLRS consists of two components: offline modeling and online recommendation. The core module of the offline modeling part is a context-aware regression mixture model that is designed to model the location-based user behaviors in LBSMNS to learn the interest of each individual user, the local preference of each individual location, and the context-aware influence factors. The online recommendation part takes a querying user along with the corresponding querying spatial-temporal context as input and automatically combines the learned interest of the querying user, the local preference of the querying location, and the context-aware influence factor to produce the top-k recommendations. We evaluate the performance of STCAPLRS on two real-world datasets: Dianping and Foursquare. The results demonstrate the superiority of STCAPLRS in recommending location items for users in terms of both effectiveness and efficiency. Moreover, the experimental analysis results also illustrate the excellent interpretability of STCAPLRS.
WOS关键词INFORMATION ; NETWORKS
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000380322200016
资助机构National Basic Research Program of China(2012CB316304) ; National Natural Science Foundation of China(61225009 ; Beijing Natural Science Foundation(4131004) ; Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia(RGP-1436-023) ; 61432019 ; 61332016 ; 61303176)
源URL[http://ir.ia.ac.cn/handle/173211/12169]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.King Saud Univ, Coll Comp & Informat Sci, Dept Software Engn, Riyadh 11543, Saudi Arabia
3.King Saud Univ, Dept Comp Engn, Coll Comp & Informat Sci, Riyadh 11543, Saudi Arabia
推荐引用方式
GB/T 7714		 Fang, Quan,Xu, Changsheng,Hossain, M. Shamim,et al. STCAPLRS: A Spatial-Temporal Context-Aware Personalized Location Recommendation System[J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,2016,7(4):30.
APA Fang, Quan,Xu, Changsheng,Hossain, M. Shamim,&Muhammad, G..(2016).STCAPLRS: A Spatial-Temporal Context-Aware Personalized Location Recommendation System.ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,7(4),30.
MLA Fang, Quan,et al."STCAPLRS: A Spatial-Temporal Context-Aware Personalized Location Recommendation System".ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 7.4(2016):30.

入库方式： OAI收割

来源：自动化研究所

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