Matrix Factorization Method for Decentralized Recommender Systems

2016-04-28 · Zheng Wenjie

Decentralized recommender system does not rely on the central service provider, and the users can keep the ownership of their ratings. This article brings the theoretically well-studied matrix factorization method into the decentralized recommender system, where the formerly prevalent algorithms are heuristic and hence lack of theoretical guarantee. Our preliminary simulation results show that this method is promising.

📄 PDF Abstract BibTeX arXiv:1604.08420

Code (0)

등록된 구현이 없습니다.

Tasks

Recommendation Systems

Decentralized Matrix Factorization with Heterogeneous Differential Privacy

2022-12-01 · Wentao Hu, Hui Fang

Conventional matrix factorization relies on centralized collection of users' data for recommendation, which might introduce an increased risk of privacy leakage especially when the recommender is untrusted. Existing diff…

RankMat : Matrix Factorization with Calibrated Distributed Embedding and Fairness Enhancement

2022-04-27 · Hao Wang

Matrix Factorization is a widely adopted technique in the field of recommender system. Matrix Factorization techniques range from SVD, LDA, pLSA, SVD++, MatRec, Zipf Matrix Factorization and Item2Vec. In recent years, di…

Fairness Recommendation Systems Word Embeddings

TEE-based decentralized recommender systems: The raw data sharing redemption

2022-02-23 · Akash Dhasade, Nevena Dresevic, Anne-Marie Kermarrec, Rafael Pires

Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but…

Collaborative Filtering Federated Learning Recommendation Systems

Privacy Threats Against Federated Matrix Factorization

2020-07-03 · Dashan Gao, Ben Tan, Ce Ju, Vincent W. Zheng 외

Matrix Factorization has been very successful in practical recommendation applications and e-commerce. Due to data shortage and stringent regulations, it can be hard to collect sufficient data to build performant recomme…

Collaborative Filtering Federated Learning Privacy Preserving Recommendation Systems

CF Recommender System Based on Ontology and Nonnegative Matrix Factorization (NMF)

2024-05-31 · Sajida Mhammedi, Hakim El Massari, Noreddine Gherabi, Amnai Mohamed

Recommender systems are a kind of data filtering that guides the user to interesting and valuable resources within an extensive dataset. by providing suggestions of products that are expected to match their preferences. …

Collaborative Filtering Recommendation Systems