# NRPA: Neural Recommendation with Personalized Attention

## TL;DR

This paper introduces NRPA, a neural recommendation model that employs personalized attention mechanisms to generate user- and item-specific representations from reviews, enhancing recommendation accuracy.

## Contribution

The paper presents a novel personalized attention framework for review-based recommendation, enabling tailored representation learning for diverse users and items.

## Key findings

- Improved recommendation performance on five datasets.
- Effective personalization of review and user/item representations.
- Superior to existing review-based recommendation methods.

## Abstract

Existing review-based recommendation methods usually use the same model to learn the representations of all users/items from reviews posted by users towards items. However, different users have different preference and different items have different characteristics. Thus, the same word or similar reviews may have different informativeness for different users and items. In this paper we propose a neural recommendation approach with personalized attention to learn personalized representations of users and items from reviews. We use a review encoder to learn representations of reviews from words, and a user/item encoder to learn representations of users or items from reviews. We propose a personalized attention model, and apply it to both review and user/item encoders to select different important words and reviews for different users/items. Experiments on five datasets validate our approach can effectively improve the performance of neural recommendation.