DDTCDR: Deep Dual Transfer Cross Domain Recommendation

By P. Li, Alexander Tuzhilin. Web Search and Data Mining, 2019

327 citations recorded by the source corpus.

Cross domain recommender systems have been increasingly valuable for helping consumers identify the most satisfying items from different categories. However, previously proposed cross-domain models did not take into account bidirectional latent relations between users and items. In addition, they do not explicitly model information of user and item features, while utilizing only user ratings information for recommendations. To address these concerns, in this paper we propose a novel approach to cross-domain recommendations based on the mechanism of dual learning that transfers information between two related domains in an iterative manner until the learning process stabilizes. We develop a novel latent orthogonal mapping to extract user preferences over multiple domains while preserving relations between users across different latent spaces. Combining with autoencoder approach to extract the latent essence of feature information, we propose Deep Dual Transfer Cross Domain Recommendation (DDTCDR) model to provide recommendations in respective domains. We test the proposed method on a large dataset containing three domains of movies, book and music items and demonstrate that it consistently and significantly outperforms several state-of-the-art baselines and also classical transfer learning approaches.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/a35b975e96266b34d62ce0862cca2aee2d337f2b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jun Zhou
2. Chaochao Chen
3. Feng Zhu
4. Longfei Li
5. Yan Wang
6. Guanfeng Liu

In Information Systems

1. Chaochao Chen
2. Feng Zhu
3. Jun Zhou
4. Longfei Li
5. Yan Wang
6. Guanfeng Liu

Citations in the corpus

• Cross-Domain Recommendation: Challenges, Progress, and Prospects (2021, methodology): “To this end, dual-target CDR [ Zhu et al. , 2019; Li and Tuzhilin, 2020; Zhu et al. , 2020; Liu et al. , 2020b] and multi-target CDR [Cui et al. , 2020] have been proposed recently to improve the recommendation performance in dual/multiple domains.”