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Computer Science, Information Systems
Michal Balchanowski, Urszula Boryczka
Summary: Recommendation systems are essential tools in many websites, but the recommendations generated using different methods may vary in effectiveness. To improve the overall quality of recommendations, aggregation techniques such as the use of the Differential Evolution algorithm are suggested.
Article
Computer Science, Information Systems
Zhang Li, Chen XiaoBo
Summary: The paper proposes a recommendation algorithm based on social networks, which optimizes the recommendation results by considering the trust relationships and social influence between users. The experiments show that the algorithm has significant advantages in recommendation accuracy.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Xiaodong Feng, Zhen Liu, Wenbing Wu, Wenbo Zuo
Summary: The rapid development of social recommendation in recent years has greatly improved the performance of recommender systems, especially for the cold start problem. However, existing techniques based on matrix factorization do not effectively capture the complex nonlinear relationships between users and items, as well as between users themselves. To address this, deep learning is employed to model the social network-enhanced collaborative filtering problem. By simultaneously modeling the social and item domain interactions, the proposed SoNeuMF framework shows significant improvements in recommendation accuracy compared to state-of-the-art methods, as demonstrated by comprehensive experiments on real-world datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Quan Do, Wei Liu, Jin Fan, Dacheng Tao
Summary: This research proposes a method to discover explicit and implicit similarities across domains through matrix tri-factorization, improving the accuracy of cross-domain recommendations by preserving both shared and domain-specific factors. By utilizing explicit and implicit similarities, the approach outperforms existing algorithms by more than two times in recommendation accuracy.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Chemistry, Medicinal
Iori Azuma, Tadahaya Mizuno, Hiroyuki Kusuhara
Summary: This study proposed a new method called neighborhood regularized bidirectional matrix factorization (NRBdMF) to predict drug effects by incorporating bidirectionality. The NRBdMF model achieved high accuracy and interpretability in predicting both side effects and therapeutic effects.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Mathematics
Yongheng Mu, Yun Wu
Summary: Recommendation systems are widely used to provide personalized content and services to users efficiently. In this paper, a personalized multimodal movie recommendation system based on deep learning and multimodal data analysis was proposed. Real-world MovieLens datasets were used to test the effectiveness of the algorithm, which achieved improved accuracy in predicting movie scores compared to traditional collaborative filtering approaches. The combination of deep learning and multimodal data analysis can help alleviate the sparse data problem and enhance the performance of recommendation systems.
Article
Engineering, Multidisciplinary
Xujian Fang, Jiayi Wang, Dewen Seng, Binquan Li, Chenxuan Lai, Xiyuan Chen
Summary: This paper proposes the Local-Global Awareness Attention Model (LGAA) to model comment information, which calculates the importance of comments through local and global attention mechanisms, ultimately improving the performance of recommendation systems.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Wang Zhou, Amin Ul Haq, Laixiang Qiu, Jehan Akbar
Summary: This article proposes a novel multi-view social recommendation scenario named MsRec, which improves recommendation performance and user experience by leveraging intricate inner relationships within social networks and various information sources. Experimental results demonstrate the significant advantages of MsRec over other benchmark recommender algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Zhaoliang Chen, Shiping Wang
Summary: This paper provides an overview of recommender systems based on matrix completion, including the introduction of related algorithms, performance evaluation, and discussion of future research directions.
KNOWLEDGE AND INFORMATION SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Xiaoxin Sun, Lisa Zhang, Yuling Wang, Mengying Yu, Minghao Yin, Bangzuo Zhang
Summary: This paper explores the effective fusion of user-item ratings and item attributes to improve recommendations by proposing an attribute-aware deep attentive recommendation model. Extensive experiments demonstrate that this method outperforms existing methods in both rating prediction and Top-N Recommendation tasks.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Engineering, Multidisciplinary
Liangtian Wan, Feng Xia, Xiangjie Kong, Ching-Hsien Hsu, Runhe Huang, Jianhua Ma
Summary: Recent years have seen a surge in information overload on online social networks, leading to increased interest in social network based recommender systems. This study introduces a novel trust-aware approach based on deep learning to improve recommendation performance, incorporating deep matrix factorization techniques, deep marginalized denoising autoencoder, and community regularization, which outperformed existing baselines, particularly for cold-start users.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2021)
Article
Chemistry, Multidisciplinary
Xu Yang, Yuchuan Luo, Shaojing Fu, Ming Xu, Yingwen Chen
Summary: Collaborative filtering faces serious privacy and resource problems, with research focusing on using decentralized probabilistic matrix factorization to address these issues. The proposed probabilistic matrix co-factorization model integrates explicit and implicit feedback, while the decentralized learning method allows users to keep their private data on end devices.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Hanfei Wang, Yuan Zuo, Hong Li, Junjie Wu
Summary: This study proposes a novel framework for building cross-domain personality-based recommender systems, which effectively recognizes users' personality traits and improves recommendation performance through transfer learning.
KNOWLEDGE-BASED SYSTEMS
(2021)
Review
Computer Science, Information Systems
Rui Chen, Kangning Pang, Min Huang, Hui Liang, Shizheng Zhang, Lei Zhang, Pu Li, Zhengwei Xia, Jianwei Zhang, Xiangjie Kong
Summary: With the development of online social networks, more and more users are participating and forming rich social relationships. These relationships provide a data source and research basis for recommender systems, driving the development of recommender systems based on social networks.
Article
Computer Science, Information Systems
Dongjin Yu, Wenbo Wanyan, Dongjing Wang
Summary: The proposed context- and preference- aware model (CPAM) incorporates contextual influence and user preferences into POI recommendation, outperforming existing baselines according to experimental results.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Chien Chin Chen, Meng-Chieh Chung
KNOWLEDGE-BASED SYSTEMS
(2015)
Article
Computer Science, Artificial Intelligence
Ze-Han Fang, Chien Chin Chen
DECISION SUPPORT SYSTEMS
(2016)
Article
Computer Science, Artificial Intelligence
Yung-Chun Chang, Chien Chin Chen, Wen-Lian Hsu
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2016)
Article
Computer Science, Information Systems
Chien Chin Chen, Yu-Chun Sun
INFORMATION PROCESSING LETTERS
(2016)
Article
Computer Science, Artificial Intelligence
Zhong-Yong Chen, Chien Chin Chen
KNOWLEDGE-BASED SYSTEMS
(2016)
Article
Mathematical & Computational Biology
Yung-Chun Chang, Chun-Han Chu, Yu-Chen Su, Chien Chin Chen, Wen-Lian Hsu
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
(2016)
Article
Mathematical & Computational Biology
Sun Kim, Rezarta Islamaj Dogan, Andrew Chatr-Aryamontri, Christie S. Chang, Rose Oughtred, Jennifer Rust, Riza Batista-Navarro, Jacob Carter, Sophia Ananiadou, Sergio Matos, Andre Santos, David Campos, Jose Luis Oliveira, Onkar Singh, Jitendra Jonnagaddala, Hong-Jie Dai, Emily Chia-Yu Su, Yung-Chun Chang, Yu-Chen Su, Chun-Han Chu, Chien Chin Chen, Wen-Lian Hsu, Yifan Peng, Cecilia Arighi, Cathy H. Wu, K. Vijay-Shanker, Ferhat Aydin, Zehra Melce Husunbeyi, Arzucan Ozgur, Soo-Yong Shin, Dongseop Kwon, Kara Dolinski, Mike Tyers, W. John Wilbur, Donald C. Comeau
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
(2016)
Article
Computer Science, Artificial Intelligence
Chien Chin Chen, Zhong-Yong Chen, Chen-Yuan Wu
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2012)
Article
Computer Science, Information Systems
Chien Chin Chen, Yu-Hao Wan, Meng-Chieh Chung, Yu-Chun Sun
INFORMATION SCIENCES
(2013)
Article
Computer Science, Information Systems
Ze-Han Fang, Chien Chin Chen
Summary: This paper proposes a novel collaborative trend prediction method using crowdsourced intelligence from web search engines to estimate the status of trending topics. The experimental results show that the proposed method outperforms traditional methods and successfully predicts the statuses of trending topics.
DATA TECHNOLOGIES AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Yung-Chun Chang, Pi-Hua Chuang, Chien Chin Chen, Wen-Lian Hsu
COMPUTATIONAL INTELLIGENCE
(2017)
Proceedings Paper
Computer Science, Information Systems
Yung-Chun Chang, Chien Chin Chen, Wen-Lian Hsu
2017 IEEE 33RD INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2017)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Yung-Chun Chang, Cen-Chieh Chen, Yu-Lun Hsieh, Chien Chin Chen, Wen-Lian Hsu
PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL) AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (IJCNLP), VOL 2
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Computer Science, Information Systems
Zhong-Yong Chen, Chien Chin Chen
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING
(2015)