Article
Computer Science, Artificial Intelligence
F. Ortega, J. Mayor, D. Lopez-Fernandez, R. Lara-Cabrera
Summary: CF4J 2.0 is a framework designed for research experiments based on collaborative filtering, with features like implemented algorithms, quality measures, parallel execution, and abstract classes for developers to customize. The new version focuses on simple deployment, reproducible science, hyper-parameter optimization, data analysis, and community openness as an open-source project.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Lianghao Xia, Chao Huang, Yong Xu, Huance Xu, Xiang Li, Weiguo Zhang
Summary: As deep learning techniques are applied to real-world recommendation tasks, many deep neural network based Collaborative Filtering (CF) models have been developed. However, most existing models are not well equipped to handle missing data. In this paper, we propose a Collaborative Reflection-Augmented Autoencoder Network (CRANet) that can leverage both observed and unobserved user-item interactions to improve recommendation performance. We also introduce a robust joint training algorithm using regularization-based tied-weights. Experimental results show that debiasing negative signals improves the performance compared to state-of-the-art techniques.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Soheila Molaei, Amirhossein Havvaei, Hadi Zare, Mahdi Jalili
Summary: The paper proposes a new recommendation approach - Collaborative Deep Forest Learning (CDFL), which aims to improve the performance of recommender systems by learning latent social features and outperforms state-of-the-art CF recommendation methods based on experiments with real-world datasets from different domains.
Article
Computer Science, Information Systems
Le Nguyen Hoai Nam
Summary: This paper focuses on the rating prediction phase in memory-based collaborative filtering and improves the prediction accuracy by optimizing an objective function. Experimental results demonstrate that the proposed method outperforms others, especially when the number of selected neighbors is small to medium.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Jesus Bobadilla, Fernando Ortega, Abraham Gutierrez, Angel Gonzalez-Prieto
Summary: The research introduces a method to incorporate stochasticity into deep learning models using variational autoencoders, aiming to improve the performance of recommender systems. By introducing variational techniques in the latent space, this approach can be applied as a plugin to current and future models, demonstrating superior performance in experiments.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Cybernetics
Kang Liu, Feng Xue, Shuaiyang Li, Sheng Sang, Richang Hong
Summary: Multimedia-based recommendation is a challenging task that aims to explore multimodal user preference cues and provide personalized recommendations. However, current solutions are limited by multimodal noise contamination. To address this issue, researchers propose a hierarchical framework to separately learn collaborative signals and multimodal preference cues, and take measures to alleviate noise contamination.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jesus Bobadilla, Abraham Gutierrez, Raciel Yera, Luis Martinez
Summary: This paper proposes a method based on Generative Adversarial Networks (GANs) to generate collaborative filtering datasets with specific features. The method uses dense, short, and continuous embeddings for faster and more accurate learning compared to traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Pham Minh Thu Do, Thi Thanh Sang Nguyen
Summary: This paper proposes a novel semantic-enhanced Neural Collaborative Filtering (NCF) model for movie rating prediction and recommendation tasks. By building a semantic knowledge base and user behavior analytic model, combined with user preferences and recommendation model, the proposed model shows better recommendation performance in experiments.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Alvaro Gonzalez, Fernando Ortega, Diego Perez-Lopez, Santiago Alonso
Summary: Recommender Systems, an essential tool in streaming and marketplace systems, have been found to exhibit clear bias and unfairness towards minorities and underrepresented groups. This paper analyzes the demographic characteristics of a gold standard dataset and proposes Soft Matrix Factorization (SoftMF) to balance predictions and reduce existing inequality.
Article
Computer Science, Artificial Intelligence
Anchen Li, Bo Yang, Huan Huo, Hongxu Chen, Guandong Xu, Zhen Wang
Summary: Recently, deep learning techniques have achieved great success in recommender systems. However, most deep methods lack explicit extraction of mutual semantic relationships between users and items, which are latent in user-item interactions. Additionally, these methods primarily focus on representation learning in euclidean geometry, ignoring the non-euclidean latent anatomy of the bipartite graph structure. This work presents HNCR, a deep hyperbolic representation learning method that leverages mutual semantic relationships for collaborative filtering tasks, demonstrating superior performance compared to euclidean counterparts and state-of-the-art recommendation baselines.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Sofia Bourhim, Lamia Benhiba, M. A. Janati Idrissi
Summary: This article presents a graph-based recommender system that improves accuracy by considering the behavior of communities and the interaction between sub-groups in the network. Experimental results show significant improvements compared to other deep learning models, and the system has the potential to extract deep relationships for better recommendations.
Article
Chemistry, Multidisciplinary
Abebe Tegene, Qiao Liu, Yanglei Gan, Tingting Dai, Habte Leka, Melak Ayenew
Summary: A collaborative recommender system based on a latent factor model has achieved significant success in the field of personalized recommender systems. However, the latent factor model suffers from sparsity problems and limited ability in extracting non-linear data features, resulting in poor recommendation performance. In this paper, we propose a dual deep learning and embedding-based latent factor model that considers dense user and item feature vectors to overcome these problems and improve rating prediction performance.
APPLIED SCIENCES-BASEL
(2023)
Review
Computer Science, Information Systems
Fethi Fkih
Summary: This paper provides an in-depth review of similarity measures used in collaborative filtering-based recommender systems. Through experimental studies, the performance of different measures is compared, and important conclusions are drawn. Evaluation results show that different similarity measures have different suitability in user-based and item-based recommendations.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Yueting Fang, Hao Wu, Yiji Zhao, Lei Zhang, Shaowei Qin, Xin Wang
Summary: Graph neural network (GNN) is a powerful model for processing non-Euclidean data, such as graphs, in recommendation tasks. However, existing GNN models lack attention to recommendation diversity. This work proposes a novel graph spreading network (GSN) model that addresses the accuracy-diversity dilemma in recommendation by modifying the propagation rule and developing a new sampling strategy. GSN effectively improves diversity while maintaining accuracy through a selective sampler.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Defu Lian, Xing Xie, Enhong Chen, Hui Xiong
Summary: The paper proposes a new method called product Quantized Collaborative Filtering (pQCF) to achieve a better balance between efficiency and accuracy. By decomposing the latent space into subspaces and learning clustered representations, latent factors can be efficiently represented and user preferences for items can be calculated. Experimental results show that pQCF significantly outperforms hashing-based CF methods.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Ming Lei, Heyan Huang, Chong Feng, Yang Gao, Chao Su
NEURAL COMPUTING & APPLICATIONS
(2019)
Article
Computer Science, Artificial Intelligence
Tianfu Zhang, Heyan Huang, Chong Feng, Xiaochi Wei
NEURAL COMPUTING & APPLICATIONS
(2020)
Article
Computer Science, Software Engineering
Chong Feng, Muhammad Adnan, Arshad Ahmad, Ayaz Ullah, Habib Ullah Khan
SCIENTIFIC PROGRAMMING
(2020)
Review
Computer Science, Information Systems
Arshad Ahmad, Chong Feng, Muzammil Khan, Asif Khan, Ayaz Ullah, Shah Nazir, Adnan Tahir
SECURITY AND COMMUNICATION NETWORKS
(2020)
Article
Computer Science, Information Systems
Arshad Ahmad, Ayaz Ullah, Chong Feng, Muzammil Khan, Shahzad Ashraf, Muhammad Adnan, Shah Nazir, Habib Ullah Khan
SECURITY AND COMMUNICATION NETWORKS
(2020)
Article
Computer Science, Artificial Intelligence
Yashen Wang, Heyan Huang, Chong Feng
Summary: This paper focuses on enhancing microblog retrieval effectiveness by using local conceptual word embeddings and incorporating strategies such as query expansion and temporal evidences. The proposed approach outperforms baseline methods in terms of understanding information needs and meeting users' real-time information requirements, as demonstrated by experiments on the official TREC Twitter corpora.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Review
Computer Science, Information Systems
Abdulganiyu Abdu Yusuf, Chong Feng, Xianling Mao, Ramadhani Ally Duma, Mohammed Salah Abood, Abdulrahman Hamman Adama Chukkol
Summary: This paper systematically reviews the use of graph neural networks for image-based VQA. It analyzes and compares various models in terms of techniques, performance, and challenges. The study shows that graph neural networks have significant advantages in handling graph-structured data for VQA tasks.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Bo Wang, Yi-Fan Lu, Xiaochi Wei, Xiao Liu, Ge Shi, Changsen Yuan, Heyan Huang, Chong Feng, Xianling Mao
Summary: This paper presents the system developed by the BIT-WOW team for the NLPCC2022 shared task in Task5 Track1. The system utilizes the Label-aware Graph Convolutional Network (LaGCN) to address the multi-label classification task for academic paper abstracts in the scientific domain. The experiments demonstrate the effectiveness of LaGCN in modeling category information and dealing with a large number of categories. Furthermore, the application of curriculum learning contributes to the adaptability of the system. The ensemble model achieved the best performance according to the official results.
NATURAL LANGUAGE PROCESSING AND CHINESE COMPUTING, NLPCC 2022, PT II
(2022)
Proceedings Paper
Computer Science, Information Systems
He Zhao, Zhunchen Luo, Chong Feng, Yuming Ye
PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19)
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Changsen Yuan, Heyan Huang, Chong Feng, Xiao Liu, Xiaochi Wei
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)
Article
Computer Science, Information Systems
Arshad Ahmad, Chong Feng, Kan Li, Syed Mohammad Asim, Tingting Sun
Proceedings Paper
Computer Science, Artificial Intelligence
Yang Wang, Chong Feng, Qian Liu
NATURAL LANGUAGE PROCESSING AND CHINESE COMPUTING, NLPCC 2018, PT II
(2018)
Article
Computer Science, Artificial Intelligence
Jun Ma, Chong Feng, Ge Shi, Xuewen Shi, Heyang Huang
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
(2018)
Review
Computer Science, Information Systems
Arshad Ahmad, Chong Feng, Shi Ge, Abdallah Yousif
DATA TECHNOLOGIES AND APPLICATIONS
(2018)