Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization

TL;DR

This paper introduces CDIMF, a scalable cross-domain recommendation model that extends implicit matrix factorization with ALS and shared latent factors, effectively addressing data sparsity and cold-start issues.

Contribution

The paper proposes a novel cross-domain matrix factorization method using ALS and ADMM to learn shared latent factors for overlapped users, improving scalability and performance.

Findings

Outperforms recent cross-domain models in experiments

Effective for cold-start and warm-start scenarios

Demonstrates competitive performance on industrial datasets

Abstract

Data sparsity has been one of the long-standing problems for recommender systems. One of the solutions to mitigate this issue is to exploit knowledge available in other source domains. However, many cross-domain recommender systems introduce a complex architecture that makes them less scalable in practice. On the other hand, matrix factorization methods are still considered to be strong baselines for single-domain recommendations. In this paper, we introduce the CDIMF, a model that extends the standard implicit matrix factorization with ALS to cross-domain scenarios. We apply the Alternating Direction Method of Multipliers to learn shared latent factors for overlapped users while factorizing the interaction matrix. In a dual-domain setting, experiments on industrial datasets demonstrate a competing performance of CDIMF for both cold-start and warm-start. The proposed model can outperform most other recent cross-domain and single-domain models. We also provide the code to reproduce experiments on GitHub.