# Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation

arXiv: https://arxiv.org/abs/1708.03797 · HTML: https://mlsanity.com/paper/1708.03797/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2017.

## Built on by (most cited first, 2 of 2)

- [Automatic Product Ontology Extraction from Textual Reviews](https://mlsanity.com/paper/2105.10966/) (2021)
- [Graph-Based Recommendation System Enhanced with Community Detection](https://mlsanity.com/paper/2201.03622/) (2022)

## Abstract (arXiv)

Matrix factorization has now become a dominant solution for personalized recommendation on the Social Web. To alleviate the cold start problem, previous approaches have incorporated various additional sources of information into traditional matrix factorization models. These upgraded models, however, achieve only "marginal" enhancements on the performance of personalized recommendation. Therefore, inspired by the recent development of deep-semantic modeling, we propose a hybrid deep-semantic matrix factorization (HDMF) model to further improve the performance of tag-aware personalized recommendation by integrating the techniques of deep-semantic modeling, hybrid learning, and matrix factorization. Experimental results show that HDMF significantly outperforms the state-of-the-art baselines in tag-aware personalized recommendation, in terms of all evaluation metrics, e.g., its mean reciprocal rank (resp., mean average precision) is 1.52 (resp., 1.66) times as high as that of the best baseline.