Aspect Based Neural Recommender Using Adaptive Prediction

Abstract

Recommendation systems are information processing systems that analyze user behavior and make suggestions relevant to the users' interests. These systems recommend movies, products etc., based on many different factors. These recommended items are the items which are most likely in the interest of the users. This work focuses on aspects of users' textual comments to make recommendations that are relevant to users. We propose a model that recommends the items based on extracted aspects, taking the reviews and information from the items. Based on the item's aspect level importance, a user may rate them high or low, and we have modelled our recommendation system considering the same. This calculation is based on the neural co-attention mechanism. The proposed model points to various shortcomings of the model that were introduced before. We have used the datasets from amazon, which are extracted using web scraping. The existing recommendation system predicts ratings alone, while the proposed model gives rankings and ratings. The model's efficiency is checked using precision, recall and F1 score. © 2023 IEEE.