Contrastive Cross-Domain Sequential Recommendation (2304.03891v1)

Abstract: Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challenge of CDSR is how to mine precise cross-domain user preference based on the intra-sequence and inter-sequence item interactions. Existing works first learn single-domain user preference only with intra-sequence item interactions, and then build a transferring module to obtain cross-domain user preference. However, such a pipeline and implicit solution can be severely limited by the bottleneck of the designed transferring module, and ignores to consider inter-sequence item relationships. In this paper, we propose C^2DSR to tackle the above problems to capture precise user preferences. The main idea is to simultaneously leverage the intra- and inter- sequence item relationships, and jointly learn the single- and cross- domain user preferences. Specifically, we first utilize a graph neural network to mine inter-sequence item collaborative relationship, and then exploit sequential attentive encoder to capture intra-sequence item sequential relationship. Based on them, we devise two different sequential training objectives to obtain user single-domain and cross-domain representations. Furthermore, we present a novel contrastive cross-domain infomax objective to enhance the correlation between single- and cross- domain user representations by maximizing their mutual information. To validate the effectiveness of C^2DSR, we first re-split four e-comerce datasets, and then conduct extensive experiments to demonstrate the effectiveness of our approach C^2DSR.

• The paper proposes a unified model, C²DSR, that concurrently leverages intra-sequence and inter-sequence item relationships.
• It integrates a graph neural network with a sequential attentive encoder to capture both single-domain and cross-domain user preferences.
• Results on e-commerce datasets demonstrate significant improvements in MRR, NDCG, and Hit Ratio over traditional methods.

Contrastive Cross-Domain Sequential Recommendation

This paper introduces a model known as C$^2$DSR, designed to address the complex task of Cross-Domain Sequential Recommendation (CDSR). The focus is on predicting users' future interactions based on historical sequential data across multiple domains. The challenge in CDSR lies in accurately capturing user preferences when dealing with both intra-domain and inter-domain interactions.

Methodology

The authors critique existing approaches that compartmentalize learning of single-domain preferences followed by a transfer module for cross-domain interactions. They argue that this method is constrained by the bottleneck associated with the transfer process and often neglects the potential inter-sequence item relationships. Instead, C$^2$DSR proposes a unified strategy to capture user preferences by concurrently leveraging both intra-sequence and inter-sequence relationships.

The model architecture employs a graph neural network to explore inter-sequence item relationships, capturing collaborative signals between items in different sequences. Subsequently, a sequential attentive encoder is employed to extract intra-sequence item relationships, focusing on the sequential order. These components generate sequential outputs that represent user preferences in both single and cross-domain contexts.

A crucial innovation in C$^2$DSR is the contrastive cross-domain infomax objective. This objective aims to maximize mutual information between single-domain and cross-domain representations, thereby enhancing the alignment between independent domain-user preferences and holistic cross-domain preferences.

Results and Implications

The paper evaluates C$^2$DSR using four e-commerce datasets, demonstrating significant improvements over existing methods. Metrics such as Mean Reciprocal Rank (MRR), NDCG, and Hit Ratio indicate superior performance. The results suggest that capturing comprehensive user preferences through the joint modeling approach enhances recommendation precision and mitigates biases inherent in single-domain models.

The paper's findings have notable implications for recommender systems deployed in environments where users interact across diverse categories (e.g., e-commerce platforms like Amazon). By leveraging cross-domain interactions, these systems can offer significantly more tailored recommendations, potentially leading to improved user satisfaction and engagement.

Theoretical Contributions and Future Directions

From a theoretical standpoint, the paper contributes to the field by introducing a model that effectively integrates mutual information maximization into sequential recommendations, showcasing how contrastive learning methods can enhance the CDSR framework.

Looking forward, the research suggests several extensions. Exploring the application of C$^2$DSR in multi-domain settings or adapting the model to operate in continuous time spaces could offer further insights. These avenues could involve integrating additional user context or temporal dynamics, which may reveal nuanced patterns of user behavior across domains.

In conclusion, the C$^2$DSR model presents a robust solution to CDSR challenges by unifying intra-sequence and inter-sequence learning with an innovative contrastive learning approach. This research paves the way for more sophisticated cross-domain recommendation systems in complex multi-domain environments.