Article
Computer Science, Information Systems
Gunay Gultekin, Oguz Bayat
Summary: Researchers introduced a Naive Bayes Prediction Model based on Bayesian Theory for Point of Interest (POI) recommendation, and experimented with the Brightkite dataset to compare its performance with other recommendation methods, finding that it outperforms in location-based POI recommendation.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Jun Zhu, Lixin Han, Zhinan Gou, Yi Yang, Xiaofeng Yuan, Jingxian Li, Shu Li
Summary: This paper introduces an ensemble-based personalized location recommendation algorithm to ensure the robustness of recommendation systems in terms of accuracy and stability. Experimental results demonstrate the algorithm's superior performance in prediction accuracy and system stability.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Jingtong Liu, Huawei Yi, Yixuan Gao, Rong Jing
Summary: This paper proposes an improved graph convolutional network (PPR_IGCN) model for personalized POI recommendation, which integrates collaborative influence and social influence. The model addresses the issues of data sparsity and lack of in-depth mining of user social influence, resulting in improved recommendation results.
Article
Computer Science, Artificial Intelligence
Lei-lei Shi, Lu Liu, Liang Jiang, Rongbo Zhu, John Panneerselvam
Summary: The Quality of Service (QoS) directly impacts the satisfaction of users' nonfunctional requirements by service providers. Recent research has focused on sparse data prediction and user personalized recommendations in service recommendation and management. The proposed hybrid mobile service recommendation and management model utilizes semantic recommendation and location-based quality preference analysis to predict QoS requirements and offer the most suitable services to users.
Article
Management
H. Henry Cao, Liye Ma, Z. Eddie Ning, Baohong Sun
Summary: In this paper, the authors use a continuous time bandit model to analyze the effectiveness of recommendation algorithms in a monopoly and duopoly market. They find that in a competitive market, firms focus more on exploitation rather than exploration. Additionally, competition decreases the return from developing a forward-looking algorithm for impatient users. However, the development of a forward-looking algorithm always benefits users in a competitive market. The decision of competing firms to invest in a forward-looking algorithm can create a prisoner's dilemma, highlighting the implications for artificial intelligence adoption and policy makers.
MANAGEMENT SCIENCE
(2023)
Article
Computer Science, Information Systems
Yi-Chung Chen, Hsi-Ho Huang, Sheng-Min Chiu, Chiang Lee
Summary: Joint promotion is a valuable business strategy that can attract more customers at lower operational cost, but finding a suitable partner can be challenging. This article proposes a framework using location-based social networks data to recommend Joint Promotion Partners, considering six factors and developing efficient algorithms for calculations. The algorithms' effectiveness and efficiency were verified using Foursquare data and real-life case studies.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2021)
Article
Automation & Control Systems
Diyawu Mumin, Lei-Lei Shi, Lu Liu, John Panneerselvam
Summary: This article proposes a new method for item recommendation based on social relationships and the Internet of People, which improves recommendation accuracy and diversity. By introducing a resource redistribution process and adjusting parameters, notable performance improvements are achieved on real-world datasets.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Geography, Physical
Lin Wan, Han Wang, Yuming Hong, Ran Li, Wei Chen, Zhou Huang
Summary: The rising popularity of Location-based Social Networks (LBSNs) has led to a large amount of geo-tagged social media data, enabling various location-aware online services. However, previous studies on next point of interest (POI) recommendation often overlook the fine-grained user temporal preferences and fail to fully utilize the sequential patterns of contextual information. To address these challenges, this study proposes a novel framework called iTourSPOT, which extends traditional collaborative filtering methods with a context-aware POI embedding architecture. Experimental results demonstrate the effectiveness of the proposed framework, especially in sparse and cold-start scenarios.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2022)
Article
Computer Science, Artificial Intelligence
Zahra Bahari Sojahrood, Mohammad Taleai
Summary: Group recommendation using location-based social networks has attracted research attention. This paper proposes a method for point of interest (POI) group recommendation that considers user influence modeled fuzzily, historical check-in data, and factors like category, distance, and time. Experimental results show improved accuracy in POI group recommendations, especially when user influence is calculated using the fuzzy approach. The study also reveals differences in user behavior when choosing places to visit alone or in a group.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Gang-Feng Ma, Xu-Hua Yang, Yue Tong, Yanbo Zhou
Summary: Social recommendation aims to improve recommendation systems by integrating social network information. However, applying graph neural networks (GNNs) to social recommendation faces challenges related to varying user preferences and information redundancy. To address these challenges, this study proposes a preference social recommendation method using GNNs. The method includes a friend influence indicator to describe friend preferences, different GNNs to capture social and user-item interaction information, and two losses to improve connection relationships and sample distance. Experimental results demonstrate the effectiveness of the proposed method, particularly for cold-start problems in recommendation tasks.
PEERJ COMPUTER SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Hongzhi Yin, Qinyong Wang, Kai Zheng, Zhixu Li, Xiaofang Zhou
Summary: It is an important task for recommender systems to suggest satisfying activities to a group of users in daily social life. This paper proposes a novel end-to-end group recommender system called CAGR (Centrality-Aware Group Recommender), which utilizes BGEM, self-attention mechanism, and GCNs to learn group and user representations. Experimental results demonstrate the superiority of the proposed CAGR compared to state-of-the-art group recommender models.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Dengcheng Yan, Tianyi Tang, Wenxin Xie, Yiwen Zhang, Qiang He
Summary: With the increase in complexity of modern software, social collaborative coding and reuse of open source software packages have become popular. This paper proposes a Session-based Social and Dependency-aware software Recommendation (SSDRec) model that considers social influence and dependency constraints. Extensive experiments demonstrate the superiority of the model.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Yang Liu, Liang Chen, Xiangnan He, Jiaying Peng, Zibin Zheng, Jie Tang
Summary: This study focuses on utilizing the indirect influence from high-order neighbors in social networks to enhance the performance of item recommendation. Different from traditional social recommenders, we directly factor social relations in the predictive model to improve user embeddings and recommendation outcomes.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Deniz Canturk, Pinar Karagoz, Sang-Wook Kim, Ismail Hakki Toroslu
Summary: With the increasing use of mobile devices with location-related capabilities, this study focuses on the use of Location-Based Social Networks (LBSN) and proposes a trust-aware recommendation technique called TLoRW. By considering users' previous check-ins, the social network, and predicted trust scores, TLoRW generates personalized location recommendations. The proposed algorithm runs on a contextual subgraph, incorporating trust information to improve recommendation accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Jianghao Li, Guo Yang
Summary: The fast development of Internet technology has led to the importance of intelligent recommendation services. Providing accurate recommendations is crucial for user experience, and dealing with large scale networks poses significant challenges.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Hardware & Architecture
Fu-Zhen Zhuang, Ying-Min Zhou, Hao-Chao Ying, Fu-Zheng Zhang, Xiang Ao, Xing Xie, Qing He, Hui Xiong
JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
(2020)
Article
Computer Science, Artificial Intelligence
Le Wu, Lei Chen, Richang Hong, Yanjie Fu, Xing Xie, Meng Wang
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2020)
Article
Computer Science, Artificial Intelligence
Chuhan Wu, Fangzhao Wu, Tao Qi, Junxin Liu, Yongfeng Huang, Xing Xie
ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING
(2020)
Article
Computer Science, Information Systems
Hong Huang, Yu Song, Fanghua Ye, Xing Xie, Xuanhua Shi, Hai Jin
Summary: This article focuses on exploring heterogeneous edges for network representation learning, proposing a multi-stage non-negative matrix factorization (MNMF) model. Experimental results show that the MNMF model outperforms three types of baselines in practical applications.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2021)
Proceedings Paper
Computer Science, Information Systems
Zheng Liu, Jianxun Lian, Junhan Yang, Defu Lian, Xing Xie
PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20)
(2020)
Proceedings Paper
Computer Science, Information Systems
Kangzhi Zhao, Xiting Wang, Yuren Zhang, Li Zhao, Zheng Liu, Chunxiao Xing, Xing Xie
PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20)
(2020)
Proceedings Paper
Computer Science, Information Systems
Defu Lian, Haoyu Wang, Zheng Liu, Jianxun Lian, Enhong Chen, Xing Xie
WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020)
(2020)
Proceedings Paper
Computer Science, Theory & Methods
Chuhan Wu, Fangzhao Wu, Junxin Liu, Yongfeng Huang, Xing Xie
PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19)
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Chuhan Wu, Fangzhao Wu, Tao Qi, Junxin Liu, Yongfeng Huang, Xing Xie
PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19)
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie
PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19)
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Fengli Xu, Jianxun Lian, Zhenyu Han, Yong Li, Yujian Xu, Xing Xie
PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19)
(2019)
Proceedings Paper
Computer Science, Information Systems
Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, Xing Xie
KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING
(2019)
Proceedings Paper
Computer Science, Information Systems
Xianchen Wang, Hongtao Liu, Peiyi Wang, Fangzhao Wu, Hongyan Xu, Wenjun Wang, Xing Xie
DATABASE SYSTEMS FOR ADVANCED APPLICATIONS
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan
THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Jingyue Gao, Xiting Wang, Yasha Wang, Xing Xie
THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
(2019)