Cross-Attribute Matrix Factorization Model with Shared User Embedding

TL;DR

This paper introduces a cross-attribute matrix factorization model with shared user embeddings that enhances recommender systems by incorporating user and item attributes, improving robustness especially in sparse data scenarios.

Contribution

It proposes a novel neural matrix factorization approach that integrates attribute information and shared user embeddings to better handle cold-start and long-tail issues.

Findings

Outperforms existing models on Movielens and Pinterest datasets.

Shows significant improvements in sparse data scenarios.

Effectively addresses cold-start problems.

Abstract

Over the past few years, deep learning has firmly established its prowess across various domains, including computer vision, speech recognition, and natural language processing. Motivated by its outstanding success, researchers have been directing their efforts towards applying deep learning techniques to recommender systems. Neural collaborative filtering (NCF) and Neural Matrix Factorization (NeuMF) refreshes the traditional inner product in matrix factorization with a neural architecture capable of learning complex and data-driven functions. While these models effectively capture user-item interactions, they overlook the specific attributes of both users and items. This can lead to robustness issues, especially for items and users that belong to the "long tail". Such challenges are commonly recognized in recommender systems as a part of the cold-start problem. A direct and intuitive approach to address this issue is by leveraging the features and attributes of the items and users themselves. In this paper, we introduce a refined NeuMF model that considers not only the interaction between users and items, but also acrossing associated attributes. Moreover, our proposed architecture features a shared user embedding, seamlessly integrating with user embeddings to imporve the robustness and effectively address the cold-start problem. Rigorous experiments on both the Movielens and Pinterest datasets demonstrate the superiority of our Cross-Attribute Matrix Factorization model, particularly in scenarios characterized by higher dataset sparsity.