Affective Impression: Sentiment-Awareness POI Suggestion via Embedding in Heterogeneous LBSNs

作     者：Xiong, Xi Qiao, Shaojie Han, Nan Li, Yuanyuan Xiong, Fei He, Ling 

作者机构：Chengdu Univ Informat Technol Chengdu 610225 Peoples R China Chengdu Univ Informat Technol Sch Software Engn Chengdu 610225 Peoples R China Chengdu Univ Informat Technol Software Automat Generat & Intelligent Serv Key L Chengdu 610225 Sichuan Peoples R China Chengdu Univ Informat Technol Sch Management Chengdu 610103 Sichuan Peoples R China Sichuan Univ Mental Hlth Ctr West China Sch Med Chengdu 610041 Sichuan Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Sichuan Univ Coll Elect Engn & Informat Technol Chengdu 610065 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON AFFECTIVE COMPUTING》 (IEEE Trans. Affective Comput.)

年 卷 期：2022年第13卷第1期

页      面：272-284页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [61772091, 61802035, 71701026] Sichuan Science and Technology Program [2018JY0448, 2019YFG0106, 2019YFS0067, 2018GZ0253, 2018GZ0307] Natural Science Foundation of Guangxi [2018GXNSFDA138005] Youth Foundation for Humanities and Social Sciences of Ministry of Education of China [17YJCZH202] Innovative Research Team Construction Plan in Universities of Sichuan Province [18TD0027] China Postdoctoral Science Foundation [2019M653400] Scientific Research Foundation for Young Academic Leaders of Chengdu University of Information Technology [J201701] Scientific Research Foundation for Advanced Talents of Chengdu University of Information Technology [KYTZ201715, KYTZ201750] Guangdong Key Laboratory Project [2017B030314073] 

主　　题：Network embedding affective computing location-based social networks POI suggestion heterogeneous networks probabilistic graphical model 

摘      要：Location-based social networks (LBSNs) add geographical information into traditional social networks and link people s virtual and physical lives. As an important application of LBSNs, point-of-interest (POI) suggestion has become an important method to help users explore interesting and attractive locations in LBSNs. The main problems of POI suggestion include data sparsity and cold start, which have been paid much attention by existing techniques. There are two major challenges which can greatly influence the performance of suggestion accuracy. One is the fuzzy boundary between sentiments, i.e., the fine distinction between sentiments makes it difficult to classify words and texts after word-sentiment mapping operation. The other challenge is the unreliability of data quality represented by similarity metrics, which relies on data integrity and path reachability of a heterogeneous network. To cope with the above two challenges, we present a novel framework called Community-based Sentiment Extraction and Network Embedding for POI Recommendation (CENTER) for suggesting impressive POIs to a specific user in an effective fashion. The CENTER framework contains two essential techniques: (1) a latent probabilistic generative model called Community-based Sentiment Extraction (CSE), which can accurately capture the sentiments from review content in LBSNs by taking into consideration the characteristics of social communities. The parameters of the CSE model can be inferred effectively by the Gibbs sampling method. The primary sentiments are obtained based on the distribution of sentiments;(2) a network embedding model called Sentiment-aware Nework Embedding for POI Recommendation (SNER) is employed to learn the representation of the factors including POIs, users and textual sentiments in a low-dimensional embedding space. The joint training is utilized to alternatively sample all sets of edges in a heterogeneous information network. Extensive experiments were conducted on t