Cross-Attribute Matrix Factorization Model with Shared User Embedding
Wen Liang Zeng Fan Youzhi Liang Jianguo Jia
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.