Subject: recommender systems, link prediction, graph embeddings, word embeddings
Year: 2020
Type: Article
Title: Boosting Recommender Systems with Advanced Embedding Models
Author: Cenikj, Gjorgjina
Author: Gievska, Sonja
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.
Publisher: ACM
Relation: Companion Proceedings of the Web Conference 2020
Identifier: oai:repository.ukim.mk:20.500.12188/16568
Identifier: http://hdl.handle.net/20.500.12188/16568Identifier: 10.1145/3366424.3383300
