Disentangled Item Representation for Recommender Systems

TL;DR

This paper introduces a novel disentangled item representation framework for recommender systems that leverages attribute-level information to improve recommendation accuracy, especially in cold-start scenarios, and enhances interpretability.

Contribution

The paper proposes a fine-grained disentangled item representation method and a learning strategy, LearnDIR, applicable to existing models like MF and RNN, improving performance and explainability.

Findings

Outperforms state-of-the-art methods on real-world datasets.

Requires fewer parameters while maintaining high accuracy.

Provides interpretable explanations for recommendations.

Abstract

Item representations in recommendation systems are expected to reveal the properties of items. Collaborative recommender methods usually represent an item as one single latent vector. Nowadays the e-commercial platforms provide various kinds of attribute information for items (e.g., category, price and style of clothing). Utilizing these attribute information for better item representations is popular in recent years. Some studies use the given attribute information as side information, which is concatenated with the item latent vector to augment representations. However, the mixed item representations fail to fully exploit the rich attribute information or provide explanation in recommender systems. To this end, we propose a fine-grained Disentangled Item Representation (DIR) for recommender systems in this paper, where the items are represented as several separated attribute vectors instead of a single latent vector. In this way, the items are represented at the attribute level, which can provide fine-grained information of items in recommendation. We introduce a learning strategy, LearnDIR, which can allocate the corresponding attribute vectors to items. We show how DIR can be applied to two typical models, Matrix Factorization (MF) and Recurrent Neural Network (RNN). Experimental results on two real-world datasets show that the models developed under the framework of DIR are effective and efficient. Even using fewer parameters, the proposed model can outperform the state-of-the-art methods, especially in the cold-start situation. In addition, we make visualizations to show that our proposition can provide explanation for users in real-world applications.