DDTCDR: Deep Dual Transfer Cross Domain Recommendation (1910.05189v1)

Abstract: 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.

• The paper introduces a dual transfer learning mechanism that iteratively exchanges user preferences between related domains.
• It employs a latent orthogonal mapping and autoencoder-based embedding to capture nuanced user-item interactions.
• Empirical results demonstrate up to 9.54% improvement in MAE, significantly enhancing recommendation accuracy across multiple domains.

Deep Dual Transfer Cross Domain Recommendation: An Expert Analysis

The paper "DDTCDR: Deep Dual Transfer Cross Domain Recommendation" by Pan Li and Alexander Tuzhilin addresses a key challenge in cross-domain recommendation systems by enhancing the transfer of information between related domains. The proposed Deep Dual Transfer Cross Domain Recommendation (DDTCDR) model innovatively integrates dual learning mechanisms and deep learning embeddings to improve recommendation accuracy across multiple domains, namely movies, books, and music.

Core Contributions

The paper contributes significantly to the field of cross-domain recommendation systems through the following:

1. Dual Transfer Learning Mechanism: The DDTCDR model introduces a dual learning mechanism that iteratively transfers information in both directions between two related domains. This approach contrasts with prior models that primarily focused on unidirectional information transfer.
2. Latent Orthogonal Mapping: The authors propose a latent orthogonal mapping to extract user preferences, preserving the relational structure between domains. This mapping plays a crucial role in capturing latent and complex interactions by maintaining the inner product of vectors, thereby preserving user preference similarities across domains.
3. Autoencoder-Based Embedding: By employing an autoencoder framework, the model effectively transforms user and item feature vectors into continuous embeddings. This process helps to capture the latent essence of user-item interactions, facilitating a more nuanced understanding of user preferences.

Empirical Results

The empirical evaluation of the DDTCDR model highlights its effectiveness in outperforming state-of-the-art baselines across various metrics such as RMSE, MAE, Precision, and Recall. The results demonstrate consistent improvements in recommendation accuracy for the three evaluated domain pairs (books, movies, music). Notably, the model achieves relative performance improvements of up to 9.54% in MAE when compared to other methods.

Implications and Future Directions

From a practical perspective, the DDTCDR model is highly relevant for applications requiring recommendations spanning multiple content domains. The ability to harness latent similarities between user preferences across domains suggests significant potential for reducing cold-start and data sparsity challenges in recommendation systems.

Theoretical implications include the potential extension of dual transfer learning mechanisms to accommodate multiple domains beyond the pairwise level explored in this paper. Future research could further investigate the scalability of the model when applied to larger and more diverse datasets with varying domain interactions.

The paper also opens avenues for exploring convergence properties in greater depth. While empirical evidence supports the convergence of the DDTCDR model, theoretical validation under broader conditions would cement its applicability in diverse settings.

Conclusion

"DDTCDR: Deep Dual Transfer Cross Domain Recommendation" makes substantial contributions to cross-domain recommender systems by integrating dual transfer learning with advanced embedding techniques. The model's ability to significantly enhance recommendation accuracy underscores its promise as a robust framework for multi-domain applications. Future work can build on this foundation to explore more complex domain interactions and further optimize the method's scalability and robustness.