# Gated Attentive-Autoencoder for Content-Aware Recommendation

## TL;DR

This paper introduces GATED, a neural autoencoder with attention mechanisms that effectively combines item content and user feedback to improve personalized recommendations, especially in sparse data scenarios.

## Contribution

The paper presents a novel gated attentive-autoencoder model that fuses content and rating data using attention modules, enhancing recommendation accuracy and interpretability.

## Key findings

- Outperforms state-of-the-art methods on multiple datasets
- Effectively handles sparse implicit feedback
- Provides interpretable attention-based insights

## Abstract

The rapid growth of Internet services and mobile devices provides an excellent opportunity to satisfy the strong demand for the personalized item or product recommendation. However, with the tremendous increase of users and items, personalized recommender systems still face several challenging problems: (1) the hardness of exploiting sparse implicit feedback; (2) the difficulty of combining heterogeneous data. To cope with these challenges, we propose a gated attentive-autoencoder (GATE) model, which is capable of learning fused hidden representations of items' contents and binary ratings, through a neural gating structure. Based on the fused representations, our model exploits neighboring relations between items to help infer users' preferences. In particular, a word-level and a neighbor-level attention module are integrated with the autoencoder. The word-level attention learns the item hidden representations from items' word sequences, while favoring informative words by assigning larger attention weights. The neighbor-level attention learns the hidden representation of an item's neighborhood by considering its neighbors in a weighted manner. We extensively evaluate our model with several state-of-the-art methods and different validation metrics on four real-world datasets. The experimental results not only demonstrate the effectiveness of our model on top-N recommendation but also provide interpretable results attributed to the attention modules.