A general graph-based framework for top-N recommendation using content, temporal and trust information

TL;DR

This paper introduces GraFC2T2, a versatile graph-based framework that combines content, temporal, and trust data to enhance top-N recommendations, outperforming traditional matrix factorization and deep learning methods.

Contribution

The paper presents a novel, flexible graph-based framework that integrates multiple side information types for improved top-N recommendation performance.

Findings

Combining content, temporal, and trust information improves recommendation accuracy.

GraFC2T2 outperforms matrix factorization and deep learning baselines.

The framework is effective on Epinions and Ciao datasets.

Abstract

Recommending appropriate items to users is crucial in many e-commerce platforms that contain implicit data as users' browsing, purchasing and streaming history. One common approach consists in selecting the N most relevant items to each user, for a given N, which is called top-N recommendation. To do so, recommender systems rely on various kinds of information, like item and user features, past interest of users for items, browsing history and trust between users. However, they often use only one or two such pieces of information, which limits their performance. In this paper, we design and implement GraFC2T2, a general graph-based framework to easily combine and compare various kinds of side information for top-N recommendation. It encodes content-based features, temporal and trust information into a complex graph, and uses personalized PageRank on this graph to perform recommendation. We conduct experiments on Epinions and Ciao datasets, and compare obtained performances using F1-score, Hit ratio and MAP evaluation metrics, to systems based on matrix factorization and deep learning. This shows that our framework is convenient for such explorations, and that combining different kinds of information indeed improves recommendation in general.