计算机工程与科学2018，Vol.40Issue(4)：616-625,10.DOI:10.3969/j.issn.1007-130X.2018.04.007
基于上下文感知和个性化度量嵌入的下一个兴趣点推荐
Context-aware personalized metric embedding for next POI recommendation
摘要
Abstract
With the rapid development of Location-Based Social Networks (LBSN) recommender system,Point-of-Interest (POI) recommendation has become a hot topic.The research of POI recommendation aims to recommend POIs for users and to provide services such as advertising and potential customer discovery.Due to the high data sparseness of users' check-ins,POI recommendation faces a great challenge.Many researches combine geographical influence,time awareness,social relevance and other factors to improve the performance of POI recommendation.However,in most POI recommendation researches,the periodicity of mobility and the user preference varying with the change of contextual scenario have not been excavated in depth.Moreover,there exists high data sparseness in Next POI recommendation.Based on the above considerations,this paper proposes a Context-aware Personalized Metric Embedding (CPME) algorithm,which is based on the user's periodic behavior pattern.It takes into account the contextual information of users' check-ins,which can enrich the valid data,alleviate the data sparseness,improve the recommendation accuracy,and further optimize the algorithm to reduce the time complexity.The experimental analysis on two real LBSN datasets show that the proposed algorithm has better recommendation performance.关键词
基于位置的社交网络/下一个兴趣点推荐/推荐系统/上下文感知/度量嵌入Key words
location-based social networks/next POI recommendation/recommender system/context-aware/metric embedding分类
信息技术与安全科学引用本文复制引用
鲜学丰,陈晓杰,赵朋朋,杨元峰,Victor S.Sheng..基于上下文感知和个性化度量嵌入的下一个兴趣点推荐[J].计算机工程与科学,2018,40(4):616-625,10.基金项目
国家自然科学基金(61728205,61472268,61672372,61472211) （61728205,61472268,61672372,61472211）
江苏省高校自然科学面上项目(17KJD520009) （17KJD520009）
苏州市产业技术创新专项(SYG201710) （SYG201710）
