用隐式矩阵分解实现跨域用户特征共享，解决推荐系统数据稀疏问题。
Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization
- 通过交替方向乘子法学习跨域重叠用户的共享潜在因子
- 在工业级数据集上冷启动与热启动场景均表现优异
- 结构简洁易扩展，适合实际推荐系统部署
数据稀疏是推荐系统长期存在的问题。一种解决方案是利用其他源域的知识缓解此问题。然而，许多跨域推荐系统采用复杂架构，实用性受限。相比之下，矩阵分解方法仍是单域推荐的强基线。本文提出CDIMF模型，将标准隐式矩阵分解与ALS算法扩展至跨域场景。通过交替方向乘子法，在分解交互矩阵的同时学习重叠用户的共享潜在因子。在双域设置下，基于工业数据集的实验表明，CDIMF在冷启动与热启动场景中均表现出色，性能优于多数近期跨域及单域模型。代码已开源于GitHub。
原文摘要 · Abstract (English)
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.
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