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32 papers in the last four weeks, up 28% on the four weeks before. 0.2% of all new papers.

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Restricted Bernoulli Matrix Factorization: Balancing the trade-off between prediction accuracy and coverage in classification based collaborative filtering
Oct 5, 2022Ángel González-Prieto, Abraham Gutiérrez, Fernando Ortega +1Recommender Systems Collaborative Filtering
Departamento de Algebra, Geometria y Topologia, Universidad Complutense de Madrid · Instituto de Ciencias Matematicas (CSIC-UAM-UCM-UC3M) · KNODIS Research Group, Universidad Politecnica de Madrid +1
Beyond Interestingness: Semantic and Context-Aware Natural Language Query Recommendations for Visual Data Analysis
Jan 13, 2022Xingbo Wang, Siyuan Li, Furui Cheng +5Visual Analytics Recommender Systems
Bosch Research North America & Bosch Center for Artificial Intelligence (BCAI), Sunnyvale, CA, USA · Department of Computer Science and Engineering, The Hong Kong University of Science and Technology (HKUST), Hong Kong, Hong Kong, China · Dalian University of Technology, Dalian, Liaoning, China +3
Membership Inference Attacks on Recommender System: A Survey
Date pendingJiajie He, Xintong Chen, Xinyang Fang +4Membership Inference Attacks Privacy-Preserving ML
Mcmaster University, Canada · Nanyang Technological University, SG
LLMAR: A Tuning-Free Recommendation Framework for Sparse and Text-Rich Industrial Domains
Date pendingRyogo Hishikawa, Ichiro Kataoka, Shinya YudaLLM-Assisted Annotation Recommender Systems
Hitachi, Ltd. Research and Development Group Hitachi, Ibaraki, Japan · Hitachi Power Solutions Co., Ltd. Hitachi, Ibaraki, Japan

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Restricted Bernoulli Matrix Factorization: Balancing the trade-off between prediction accuracy and coverage in classification based collaborative filtering

Ángel González-Prieto, Abraham Gutiérrez, Fernando Ortega, Raúl Lara-Cabrera

Departamento de Algebra, Geometria y Topologia, Universidad Complutense de Madrid · Instituto de Ciencias Matematicas (CSIC-UAM-UCM-UC3M) · KNODIS Research Group, Universidad Politecnica de Madrid+1

Recommender Systems Collaborative Filtering Matrix Factorization

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Reliability measures associated with the prediction of the machine learning models are critical to strengthening user confidence in artificial intelligence. Therefore, those models that provide not only predictions, but also reliability, enjoy greater popularity. In the field of recommender systems, reliability is crucial, since users tend to prefer those recommendations that are sure to interest them, that is, high predictions with high reliabilities. In this paper, we propose Restricted Bernoulli Matrix Factorization (ResBeMF), a new algorithm aimed at enhancing the performance of classification-based collaborative filtering. This model is based on a collection of restricted matrix factorizations that jointly generate, for each user-item pair, a full probability distribution over the possible rating scores. To prove its effectiveness, the proposed model has been compared to other existing solutions in the literature in terms of prediction quality (Mean Absolute Error and accuracy scores), prediction quantity (coverage score) and recommendation quality (Mean Average Precision score). The experimental results demonstrate that the proposed model provides a good balance in terms of the quality measures used compared to other recommendation models.

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Latest papers 304

Restricted Bernoulli Matrix Factorization: Balancing the trade-off between prediction accuracy and coverage in classification based collaborative filtering

Beyond Interestingness: Semantic and Context-Aware Natural Language Query Recommendations for Visual Data Analysis

Membership Inference Attacks on Recommender System: A Survey

LLMAR: A Tuning-Free Recommendation Framework for Sparse and Text-Rich Industrial Domains