# Enriching Article Recommendation with Phrase Awareness

## TL;DR

This paper introduces PhrecSys, a phrase-aware recommendation model that enhances content-based article recommendations by incorporating phrase-level features, improving prediction accuracy and interpretability.

## Contribution

It proposes a novel phrase-based approach for article recommendation that improves feature informativeness and model interpretability over existing methods.

## Key findings

- Phrase features improve click and view prediction accuracy.
- Attention mechanisms highlight meaningful text spans.
- Model provides interpretable article recommendations.

## Abstract

Recent deep learning methods for recommendation systems are highly sophisticated. For article recommendation task, a neural network encoder which generates a latent representation of the article content would prove useful. However, using raw text with embedding for models could degrade sentence meanings and deteriorate performance. In this paper, we propose PhrecSys (Phrase-based Recommendation System), which injects phrase-level features into content-based recommendation systems to enhance feature informativeness and model interpretability. Experiments conducted on six months of real-world data demonstrate that phrase features boost content-based models in predicting both user click and view behavior. Furthermore, the attention mechanism illustrates that phrase awareness benefits the learning of textual focus by putting the model's attention on meaningful text spans, which leads to interpretable article recommendation.