TriMat: Context-aware Recommendation by Tri-Matrix Factorization

Fuente: arXiv
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Main Author: Wang, Hao
Format: Preprint
Published: 2025
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author Wang, Hao
author_facet Wang, Hao
contents Search engine is the symbolic technology of Web 2.0, and many people used to believe recommender systems is the new frontier of Web 3.0. In the past 10 years, with the advent of TikTok and similar apps, recommender systems has materialized the vision of the machine learning pioneers. However, many research topics of the field remain unfixed until today. One such topic is CARS (Context-aware Recommender Systems) , which is largely a theoretical topic without much advance in real-world applications. In this paper, we utilize tri-matrix factorization technique to incorporate contextual information into our matrix factorization framework, and prove that our technique is effective in improving both the accuracy and fairness metrics in our experiments.
format Preprint
id arxiv_https___arxiv_org_abs_2510_21730
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle TriMat: Context-aware Recommendation by Tri-Matrix Factorization
Wang, Hao
Information Retrieval
Search engine is the symbolic technology of Web 2.0, and many people used to believe recommender systems is the new frontier of Web 3.0. In the past 10 years, with the advent of TikTok and similar apps, recommender systems has materialized the vision of the machine learning pioneers. However, many research topics of the field remain unfixed until today. One such topic is CARS (Context-aware Recommender Systems) , which is largely a theoretical topic without much advance in real-world applications. In this paper, we utilize tri-matrix factorization technique to incorporate contextual information into our matrix factorization framework, and prove that our technique is effective in improving both the accuracy and fairness metrics in our experiments.
title TriMat: Context-aware Recommendation by Tri-Matrix Factorization
topic Information Retrieval
url https://arxiv.org/abs/2510.21730