NRPA: Neural Recommendation with Personalized Attention
This work addresses the need for more personalized recommendation systems by focusing on user and item-specific informativeness in reviews, though it is incremental as it builds on existing neural methods.
The paper tackles the problem of review-based recommendation by proposing a neural approach with personalized attention to learn distinct representations for users and items, which effectively improves recommendation performance across five datasets.
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.