Article
Computer Science, Information Systems
Shengwen Li, Chenpeng Sun, Renyao Chen, Xinchuan Li, Qingzhong Liang, Junfang Gong, Hong Yao
Summary: This paper proposes a location-aware neural graph collaborative filtering model (LA-NGCF) that incorporates location information of items to improve personalized recommendation performance.
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
(2022)
Article
Computer Science, Interdisciplinary Applications
Mei Bai, Senan Jiang, Xin Zhang, Xite Wang
Summary: This paper proposes a distributed skyline query algorithm (DSQ) which achieves efficient skyline queries in a distributed environment through pruning and rotation scheduling strategy.
JOURNAL OF COMPUTATIONAL SCIENCE
(2022)
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, Artificial Intelligence
Lei Zhu, Yang Xu, Jingjing Li, Weili Guan, Zhiyong Cheng
Summary: This paper presents a new end-to-end discrete recommendation framework based on multi-task learning to simultaneously achieve explainable and efficient recommendation. The proposed method generates binary hash codes by exploiting the correlations between preference prediction and explanation generation tasks, and uses Hamming distances to make efficient top-K recommendations and generate natural language explanations.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Jaewan Moon, Yoonki Jeong, Dong-Kyu Chae, Jaeho Choi, Hyunjung Shim, Jongwuk Lee
Summary: Collaborative filtering (CF) is a popular solution for dealing with information overload in recommender systems, but it often faces issues of data sparsity and popularity bias. Existing studies use additional data or expensive computational methods to address these problems. Inspired by Mixup in classification problems, we propose a simple yet effective data augmentation method called Collaborative Mixup (CoMix) for CF. CoMix generates virtual users/items by logically combining random users/items and complements weak collaborative signals by distinguishing the intersection and non-overlapping parts between two users/items. Extensive experimental results show that CF models equipped with CoMix consistently improve base models on four benchmark datasets.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Zhongchuan Sun, Bin Wu, Youwei Wang, Yangdong Ye
Summary: This paper introduces a sequential graph attention network (SGAT) that utilizes a multiplex directed heterogeneous graph and a vectorization algorithm to address the information understanding problem in next-item recommendation. Experimental results demonstrate the superior performance of SGAT across various datasets.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Vasileios Perifanis, Pavlos S. Efraimidis
Summary: In this study, we propose a federated item recommendation system called FedNCF, which enables learning without disclosing users' raw data through the use of a privacy-preserving aggregation method. Experimental results show that FedNCF achieves comparable recommendation quality and faster convergence compared to the original NCF system, demonstrating the effectiveness of the federated system in preserving data privacy.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Fahrettin Horasan, Ahmet Hasim Yurttakal, Selcuk Gunduz
Summary: Collaborative filtering is a technique that considers the common characteristics of users and items in recommendation systems. Matrix decompositions, such as SVD and NMF, are widely used in collaborative filtering. In this study, a technique called T-ULVD was used to improve the accuracy and quality of recommendations. Experimental results showed that T-ULVD achieved better results compared to NMF and performed as well as or even better than SVD. This study may provide guidance for future research on solving the cold-start problem and reducing sparsity in collaborative filtering based recommender systems.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Mathematics, Interdisciplinary Applications
Lei Fu, XiaoMing Ma
Summary: With the popularization of the Internet and the increasing complexity of e-commerce systems, the application of network marketing recommendation systems has greatly improved these issues, although challenges such as data sparsity and user interest drift still exist.
Article
Computer Science, Artificial Intelligence
Guannan Liu, Liang Zhang, Junjie Wu
Summary: This paper proposes a model called REDA (Relation Embedding with Dual Attentions) to address the challenge of similarity measurement in Collaborative Filtering. A new paradigm called Relation-based Collaborative Filtering is designed based on REDA. Extensive experiments show that REDA outperforms ten state-of-the-art methods, exhibiting robustness against data and relation sparsity, the ability to learn explainable item aspects, and the potential for large-scale recommendation.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Xiaofeng Zhang, Jingbin Zhong, Kai Liu
Summary: This paper is the first attempt to adapt Wasserstein autoencoders to collaborative filtering, proposing a new loss function and designing appropriate distance metrics. Experimental results demonstrate that the proposed approach outperforms existing methods in evaluation criteria Recall@R and NDCG@R.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Geography, Physical
Shengwen Li, Renyao Chen, Chenpeng Sun, Hong Yao, Xuyang Cheng, Zhuoru Li, Tailong Li, Xiaojun Kang
Summary: This study proposes a region-aware neural graph collaborative filtering (RA-NGCF) model that improves the accuracy of personalized recommendations by introducing geographical regions. The experiment results show that introducing region entities can enhance the effectiveness of personalized recommendations.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2022)
Article
Computer Science, Artificial Intelligence
Xinke Zhao, Wei Zeng, Yixin He
Summary: This paper introduces a factorized neural network model (FNN) to solve the issue of excessive parameters in deep learning models leading to heavy computational and storage burden. By utilizing traditional matrix factorization and regularization methods, most parameters in the network can be reduced, with less than 1% decrease in accuracy.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Wei Zeng, Ge Fan, Shan Sun, Biao Geng, Weiyi Wang, Jiacheng Li, Weibo Liu
Summary: The deep neural network has been successfully applied to the collaborative filtering problem, capturing side information of users and items and modeling interactions between them. Research trends towards utilizing neural networks with mixed structures to learn better representations, achieving high accuracy with minimal additional computation.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Joojo Walker, Fan Zhou, Edward Y. Baagyere, Emmanuel Ahene, Fengli Zhang
Summary: This paper proposes an Implicit Optimal Variational autoencoder model (IOVA-CF) to address the challenge of inefficient modelling of non-linear user-item interactions in collaborative filtering. IOVA-CF utilizes a novel implicit optimal prior to generate excellent latent representations. Empirical evaluations show that IOVA-CF outperforms several competitive baseline models on multiple real-world datasets.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Piero Montanari, Ilaria Bartolini, Paolo Ciaccia, Marco Patella, Stefano Ceri, Marco Masseroli
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2016)
Article
Computer Science, Information Systems
Ilaria Bartolini, Marco Patella
MULTIMEDIA SYSTEMS
(2018)
Article
Computer Science, Information Systems
Ilaria Bartolini, Marco Patella
MULTIMEDIA SYSTEMS
(2018)
Article
Engineering, Electrical & Electronic
Oreste Andrisano, Ilaria Bartolini, Paolo Bellavista, Andrea Boeri, Luciano Bononi, Alberto Borghetti, Armando Brath, Giovanni Emanuele Corazza, Antonio Corradi, Stefano de Miranda, Fabio Fava, Luca Foschini, Giovanni Leoni, Danila Longo, Michela Milano, Fabio Napolitano, Carlo Alberto Nucci, Gianni Pasolini, Marco Patella, Tullio Salmon Cinotti, Daniele Tarchi, Francesco Ubertini, Daniele Vigo
PROCEEDINGS OF THE IEEE
(2018)
Article
Computer Science, Information Systems
Ilaria Bartolini, Paolo Ciaccia, Marco Patella
ACM TRANSACTIONS ON DATABASE SYSTEMS
(2014)
Article
Computer Science, Artificial Intelligence
George Trimponias, Ilaria Bartolini, Dimitris Papadias, Yin Yang
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2013)
Article
Computer Science, Artificial Intelligence
Ilaria Bartolini, Paolo Ciaccia, Marco Patella
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2013)
Article
Computer Science, Information Systems
Ilaria Bartolini, Vincenzo Moscato, Ruggero G. Pensa, Antonio Penta, Antonio Picariello, Carlo Sansone, Maria Luisa Sapino
MULTIMEDIA TOOLS AND APPLICATIONS
(2016)
Article
Clinical Neurology
Ilaria Bartolini, Fabio Pizza, Andrea Di Luzio, Giulia Neccia, Elena Antelmi, Stefano Vandi, Giuseppe Plazzi
Article
Computer Science, Information Systems
Weixiong Rao, Lei Chen, Ilaria Bartolini
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2015)
Article
Chemistry, Multidisciplinary
Ilaria Bartolini, Marco Patella
Summary: RAM(3)S serves as a middleware software layer between multimedia stream analysis techniques and Big Data streaming platforms, facilitating the implementation of non-parallel techniques on streaming platforms. The updated RAM(3)S version incorporates novel stream processing platforms and communication with different message brokers, enabling pipeline of multiple processing tasks and demonstrating its generality through experiments on various multimedia applications.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Interdisciplinary Applications
Ilaria Bartolini, Andrea Di Luzio
Summary: The CAT-CAD tool is introduced for automatic detection of cataplexy symptoms, aiming to support neurologists in diagnosing/monitoring the disease and allowing patients to conduct video recordings at home for a more convenient experience. Performance evaluation of the tool demonstrates the effectiveness of the proposed solutions in analyzing patients' videos for discovering disease symptoms.
Article
Computer Science, Information Systems
Ilaria Bartolini, Marco Patella
Proceedings Paper
Computer Science, Cybernetics
Ilaria Bartolini, Andrea Di Luzio
MOMM 2017: THE 15TH INTERNATIONAL CONFERENCE ON ADVANCES IN MOBILE COMPUTING & MULTIMEDIA
(2017)
Proceedings Paper
Business
Ilaria Bartolini
2015 12TH INTERNATIONAL JOINT CONFERENCE ON E-BUSINESS AND TELECOMMUNICATIONS (ICETE), VOL 5
(2015)