A Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain Recommendation

Core Concepts

A unified framework that adaptively enhances user representations and learns disentangled user preferences to improve cross-domain recommendation performance.

Abstract

The paper proposes a Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain Recommendation (AREIL). The key highlights are:

Disentanglement-based Embedding Layer: The user representations are divided into domain-shared and domain-specific components to capture diverse user preferences across domains.

Adaptive Representation Enhancement Module (AREM):

Intra-domain AREM utilizes LightGCN to capture high-order collaborative information within each domain.
Inter-domain AREM employs self-attention to explore cross-domain relevance and adaptively transfer important and general factors.

Inversed Representation Learning Module (IRLM):

Domain classifiers and gradient reversal layers are introduced to learn disentangled user representations in a unified framework.
The inversed constraint objective ensures domain-shared and domain-specific representations encode complementary information.

Multi-task Learning: The entire framework is optimized through a joint loss function that combines recommendation performance and disentanglement constraints.

Extensive experiments on multiple datasets demonstrate the substantial improvement in recommendation performance achieved by AREIL compared to state-of-the-art baselines. Ablation studies and representation visualizations further validate the effectiveness of adaptive enhancement and inversed learning in cross-domain recommendation.

Stats

The paper uses three real-world recommendation datasets from Amazon:

Elec&Phone: 3,325 users, 17,709 items in Elec, 38,706 items in Phone
Sport&Phone: 4,998 users, 20,845 items in Sport, 13,655 items in Phone
Elec&Cloth: 15,761 users, 51,447 items in Elec, 48,781 items in Cloth

Quotes

None

Key Insights Distilled From

A Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain Recommendation

by Luankang Zha... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00268.pdf

Deeper Inquiries

How can the proposed framework be extended to incorporate additional user/item attributes to further enhance the adaptive representation learning

To extend the proposed framework to incorporate additional user/item attributes for enhanced adaptive representation learning, we can introduce a multi-modal approach. By integrating various types of user/item attributes such as text reviews, image features, demographic information, or social network interactions, the model can capture a more comprehensive understanding of user preferences. This extension would involve modifying the input data pipeline to include these diverse attributes and adjusting the feature extraction and fusion mechanisms within the framework. For example, incorporating text reviews could involve utilizing natural language processing techniques to extract sentiment or topic information, which can then be integrated into the user/item embeddings. By incorporating these additional attributes, the model can adaptively enhance representations based on a richer set of user/item features, leading to more personalized and accurate recommendations.

What are the potential limitations of the inversed representation learning approach, and how can it be improved to ensure more robust disentanglement of user preferences

While inversed representation learning offers a promising approach for disentangling user preferences, there are potential limitations that need to be addressed for more robust results. One limitation is the risk of overfitting to the domain-specific information, which may lead to a loss of generalizability across domains. To mitigate this, regularization techniques such as dropout or weight decay can be applied to prevent the model from memorizing domain-specific noise. Additionally, incorporating stronger domain alignment constraints during training can help ensure that domain-shared and domain-specific components are properly disentangled. Another limitation is the potential imbalance in the importance of domain-shared and domain-specific features, which can be addressed by dynamically adjusting the weighting parameters in the inversed learning module based on the relative importance of each component. By carefully balancing the constraints and regularization techniques, the inversed representation learning approach can achieve more robust disentanglement of user preferences.

Can the adaptive enhancement and inversed learning techniques be applied to other recommendation scenarios beyond cross-domain, such as multi-task or multi-modal recommendation

The adaptive enhancement and inversed learning techniques proposed in the framework can indeed be applied to other recommendation scenarios beyond cross-domain, such as multi-task or multi-modal recommendation. In a multi-task recommendation scenario, where the model needs to simultaneously optimize for different recommendation objectives (e.g., personalized recommendations, diversity, novelty), the adaptive enhancement module can be tailored to adaptively enhance user representations based on the specific task requirements. Similarly, in a multi-modal recommendation setting, where recommendations are based on diverse types of user/item interactions (e.g., text, images, audio), the inversed learning approach can be utilized to disentangle user preferences across different modalities. By incorporating these techniques into various recommendation scenarios, the model can effectively capture the complex and diverse user preferences present in real-world recommendation systems.