printf: Preference Modeling Based on User Reviews with Item Images and Textual Information via Graph Learning

TL;DR

This paper introduces printf, a graph learning-based model that effectively combines user reviews, item images, and textual information to improve recommendation accuracy, outperforming existing methods significantly.

Contribution

The paper presents a novel graph learning approach with a dimension-based attention mechanism to integrate reviews and images for better user preference modeling.

Findings

Achieves up to 48.65% improvement in NDCG@5 on Amazon datasets.

Effectively captures high-order relationships between users and items.

Demonstrates the importance of review dimensions in modeling topics and aspects.

Abstract

Nowadays, modern recommender systems usually leverage textual and visual contents as auxiliary information to predict user preference. For textual information, review texts are one of the most popular contents to model user behaviors. Nevertheless, reviews usually lose their shine when it comes to top-N recommender systems because those that solely utilize textual reviews as features struggle to adequately capture the interaction relationships between users and items. For visual one, it is usually modeled with naive convolutional networks and also hard to capture high-order relationships between users and items. Moreover, previous works did not collaboratively use both texts and images in a proper way. In this paper, we propose printf, preference modeling based on user reviews with item images and textual information via graph learning, to address the above challenges. Specifically, the dimension-based attention mechanism directs relations between user reviews and interacted items, allowing each dimension to contribute different importance weights to derive user representations. Extensive experiments are conducted on three publicly available datasets. The experimental results demonstrate that our proposed printf consistently outperforms baseline methods with the relative improvements for NDCG@5 of 26.80%, 48.65%, and 25.74% on Amazon-Grocery, Amazon-Tools, and Amazon-Electronics datasets, respectively. The in-depth analysis also indicates the dimensions of review representations definitely have different topics and aspects, assisting the validity of our model design.