作者：
Fan, Xiaoliang;Hu, Yakun;Zheng, Zibin*;Wang, Yujie;Brezillon, Patrick;...
通讯作者：
Zheng, Zibin
作者机构：
Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, Fujian, China
School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China
Laboratoire d'Informatique de Paris 6, University Pierre and Marie Curie, Paris, France
School of Data and Computer Science and National Engineering Research Center of Digital Life, Sun Yat-sen University, Guangzhou, Guangdong, China
通讯机构：
Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 51027, Guangdong, Peoples R China.
Sun Yat Sen Univ, Natl Engn Res Ctr Digital Life, Guangzhou 51027, Guangdong, Peoples R China.
语种：
英文
关键词：
Computational model;Context- awareness;Context-aware recommender systems;Personalized recommendation;Temporal and spatial;Temporal-spatial correlations;Time factors;Web service recommendations
期刊：
IEEE TRANSACTIONS ON SERVICES COMPUTING
ISSN：
1939-1374
年：
2021
卷：
14
期：
1
页码：
58-70
基金类别：
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61300232);Gansu Provincial Science and Technology Support Program (Grant Number: 1504WKCA087);China Postdoc Foundation (Grant Number: 2015M580564);10.13039/501100012226-Fundamental Research Funds for the Central Universities (Grant Number: lzujbky-2015-100 and lzujbky-2016-br04);Program for Guangdong Introducing Innovative Entrepreneurial Teams (Grant Number: 2016ZT06D211);10.13039/501100009334-Pearl River S and T Nova Program of Guangzhou (Grant Number: 201710010046)
摘要：
Recent years have witnessed the growing research interest in the Context-Aware Recommender System (CARS). CARS for Web service provides opportunities for exploring the important role of temporal and spatial contexts, separately. Although many CARS approaches have been investigated in recent years, they do not fully address the potential of temporal-spatial correlations in order to make personalized recommendation. In this paper, the Context-Aware Services Recommendation based on Temporal-Spatial Effectiveness (named CASR-TSE) method is proposed. We first model the effectiveness of spatial correlations between the user's location and the service's location on user preferen...
