CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network (2005.10549v2)

Abstract: In a large recommender system, the products (or items) could be in many different categories or domains. Given two relevant domains (e.g., Book and Movie), users may have interactions with items in one domain but not in the other domain. To the latter, these users are considered as cold-start users. How to effectively transfer users' preferences based on their interactions from one domain to the other relevant domain, is the key issue in cross-domain recommendation. Inspired by the advances made in review-based recommendation, we propose to model user preference transfer at aspect-level derived from reviews. To this end, we propose a cross-domain recommendation framework via aspect transfer network for cold-start users (named CATN). CATN is devised to extract multiple aspects for each user and each item from their review documents, and learn aspect correlations across domains with an attention mechanism. In addition, we further exploit auxiliary reviews from like-minded users to enhance a user's aspect representations. Then, an end-to-end optimization framework is utilized to strengthen the robustness of our model. On real-world datasets, the proposed CATN outperforms SOTA models significantly in terms of rating prediction accuracy. Further analysis shows that our model is able to reveal user aspect connections across domains at a fine level of granularity, making the recommendation explainable.

• The paper proposes the CATN framework, which transfers aspect-level user preferences from review data to address cold-start challenges.
• It employs an attention-based mechanism to extract and correlate semantic aspects between source and target domains.
• Empirical evaluations on Amazon datasets demonstrate improved rating prediction and robustness even under sparse data conditions.

Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network (CATN)

The paper "CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network" addresses the persistent challenge in recommender systems of improving the efficacy of recommendations for cold-start users through cross-domain learning. Cold-start users, who have no prior interactions within a target domain, present unique difficulties, especially in systems where user preferences are initially determined based on historical interactions. The proposed solution foregrounds the application of aspect-level user preference modeling to achieve effective transfer learning across domains.

Summary of the CATN Framework

Problem Addressed:

In traditional collaborative filtering methods, cold-start users represent a particular challenge due to the absence of past feedback. This research explores the transfer of user preferences between two relevant domains (e.g., books and movies) where sufficient user interaction data may be available in one domain but not in another.

Proposed Solution:

The authors propose the Aspect Transfer Network (CATN), a framework that leverages aspect-based information derived from user reviews to perform cross-domain recommendations. It focuses on specific semantic aspects of user interactions as detailed within reviews, identifying correlations between them across both the source and target domains.

1. Aspect Extraction and Transfer: CATN begins by extracting various aspects from the review text attributed to users and items in both source and target domains. Utilizing advanced text convolution techniques with an aspect-specific gate mechanism, CATN attends to multi-faceted user preferences and establishes correlations with item features across domains.
2. Cross-Domain Learning: Utilizing an attention mechanism, CATN prioritizes aspect pairs that exhibit stronger cross-domain correlations, thereby capturing fine-grained dependencies and relationships that can be leveraged in crafting recommendations in the target domain.
3. Data Augmentation with Auxiliary Reviews: To cushion the effect of review scarcity and sparsity of overlapping users, CATN adopts auxiliary reviews from like-minded users, enriching the representation of user preferences.
4. End-to-End Optimization: The framework encapsulates an end-to-end optimization process, differing fundamentally from traditional disjointed mapping approaches. This holistic optimization strategy refines correlations and representations simultaneously, enhancing robustness and interpretability.

Evaluation and Results

The empirical evaluation of the CATN model against state-of-the-art baselines, including CMF, EMCDR, and RC-DFM, demonstrated its superiority across three real-world datasets derived from the Amazon product reviews. CATN consistently achieved lower mean squared error (MSE) values, exemplifying a significant improvement in rating prediction accuracy for cold-start users in cross-domain settings. Notably, CATN's performance was less sensitive to the proportion of overlapping users, underscoring its robustness under varying conditions of data sparsity.

Theoretical and Practical Implications

1. Theoretical Implications: The research extends the scope of aspect-based recommendation models, emphasizing semantic aspect correlations for preference transfer. It supports a shift from unitary feature mappings to more nuanced, semantically driven transfer learning strategies, which are especially pertinent in multi-domain settings.
2. Practical Implications: By improving recommendations for cold-start users, CATN enhances the capabilities of cross-domain recommender systems, potentially leading to richer user experiences and increased user engagement in diverse platforms such as e-commerce, media streaming, and more.

Future Directions

While CATN marks a substantial advance in cross-domain recommendation, future research could explore the extension of this framework into more complex multi-domain environments, potential integration with deep learning architectures such as graph neural networks for richer representation learning, and further empirical studies under various domain relevance settings to understand the full potential and limitations of aspect-level transfer.

In conclusion, CATN offers a compelling approach to addressing cold-start challenges in cross-domain recommendations, focusing on leveraging the inherent richness of review data. This paper contributes significantly to the discourse on how best to harness aspect-based insights for improving recommendation quality and interpretability in complex ecosystem landscapes.