# Enhancing Topic Extraction in Recommender Systems with Entropy Regularization

arXiv: https://arxiv.org/abs/2306.07403 · HTML: https://mlsanity.com/paper/2306.07403/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews](https://mlsanity.com/paper/1802.07938/) (2018)
- [A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification](https://mlsanity.com/paper/2203.07216/) (2022)

## Abstract (arXiv)

In recent years, many recommender systems have utilized textual data for topic extraction to enhance interpretability. However, our findings reveal a noticeable deficiency in the coherence of keywords within topics, resulting in low explainability of the model. This paper introduces a novel approach called entropy regularization to address the issue, leading to more interpretable topics extracted from recommender systems, while ensuring that the performance of the primary task stays competitively strong. The effectiveness of the strategy is validated through experiments on a variation of the probabilistic matrix factorization model that utilizes textual data to extract item embeddings. The experiment results show a significant improvement in topic coherence, which is quantified by cosine similarity on word embeddings.