Boosting Recommender Systems with Advanced Embedding Models

Abstract

Recommender systems are paramount in providing personalized content and intelligent content filtering on any social media platform, web portal, and online application. In line with the current trends in the field directed towards mapping problem and data encoding representations from other fields, this research investigates the feasibility of augmenting a graph-based recommender system for Amazon products with two state-of-the-art representation models. In particular, the potential benefits of using the language representation model BERT and GraphSage based representations of nodes and edges for improving the quality of the recommendations were investigated. Link prediction and link attribute inference were used to identify the products that the users will buy and predict the rating they will give to a product, respectively. The initial results of our exploratory study are encouraging and point to potential directions for future research.