Augmenting the User-Item Graph with Textual Similarity Models

TL;DR

This paper presents a data augmentation method for recommender systems that leverages textual similarity models to add semantic relations to user-item graphs, improving performance especially in cold-start scenarios.

Contribution

It introduces a simple, effective technique to enhance user-item graphs with semantic relations derived from textual data, boosting recommendation accuracy.

Findings

Significant performance improvements across various recommendation algorithms.

Most notable gains observed in knowledge graph-based recommenders.

Enhanced cold-start recommendation performance leading to state-of-the-art results.

Abstract

This paper introduces a simple and effective form of data augmentation for recommender systems. A paraphrase similarity model is applied to widely available textual data, such as reviews and product descriptions, yielding new semantic relations that are added to the user-item graph. This increases the density of the graph without needing further labeled data. The data augmentation is evaluated on a variety of recommendation algorithms, using Euclidean, hyperbolic, and complex spaces, and over three categories of Amazon product reviews with differing characteristics. Results show that the data augmentation technique provides significant improvements to all types of models, with the most pronounced gains for knowledge graph-based recommenders, particularly in cold-start settings, leading to state-of-the-art performance.