TLDR: HorizonRec is a novel framework for cross-domain sequential recommendation that addresses data sparsity and interest drift by harmonizing user preferences across source, target, and mixed domains. It utilizes a Mixed-conditioned Distribution Retrieval module to inject behavior-aligned noise and a Dual-oriented Preference Diffusion module for guided, symmetric denoising. This ‘align-for-fusion’ approach leads to more accurate, robust, and computationally efficient recommendations compared to existing methods.
Personalized recommendations are everywhere, from what movies to watch to what products to buy. These systems aim to predict what you’ll like next based on your past behavior. However, they often face challenges like data sparsity (not enough information about a user) and interest drift (when a user’s preferences change over time).
To tackle these issues, researchers have turned to cross-domain sequential recommendation (CDSR), which leverages your behaviors from one domain (like music) to improve recommendations in another (like books). Traditional CDSR methods often follow an “align-then-fusion” approach, where they first align user representations across different domains and then combine them. The problem with this is that it can overlook the subtle, fine-grained connections between domains and might even introduce noise, leading to less accurate recommendations.
A new research paper introduces a novel framework called “Align-for-Fusion” for CDSR, specifically implemented as HorizonRec. This innovative approach aims to harmonize user preferences across three distinct areas: the source domain, the target domain, and a “mixed” domain that combines behaviors from both. It achieves this by leveraging the power of Dual-oriented Diffusion Models (DMs), which are known for their ability to match complex data distributions.
How HorizonRec Works
HorizonRec addresses the limitations of previous methods by focusing on a more integrated fusion process. It consists of two key modules:
First, the Mixed-conditioned Distribution Retrieval (MDR) module. Existing diffusion-based recommenders often inject random noise, which can disrupt cross-domain relationships. MDR solves this by creating “behavior-aligned” and “user-specific” noise. It does this by looking at a user’s historical interactions in the mixed domain and retrieving similar patterns. This retrieved information acts as a structured guide, ensuring that the noise injected into the system is meaningful and helps maintain the consistency of user preferences across all three domains.
Second, the Dual-oriented Preference Diffusion (DPD) module. This module uses the enhanced noise from MDR to simultaneously refine user representations in both the source and target domains. The mixed-domain representation acts as a semantic bridge, guiding this denoising process. This dual-oriented approach ensures that the model extracts domain-specific knowledge that is closely aligned with the user’s overall global preferences and their specific interests in the target domain. Unlike previous methods that might only focus on the target domain, HorizonRec recognizes the importance of refining both source and target representations symmetrically.
The paper highlights that HorizonRec’s “align-for-fusion” strategy leads to better alignment between user representations and target items, resulting in more accurate and relevant recommendations. The final user representation generated by HorizonRec is shown to be more aligned with the distribution of target items, demonstrating its effectiveness.
Also Read:
- A New Approach to Personalized Recommendations: Combining Diverse User Actions and Item Details
- The Next Frontier: How Generative AI is Reshaping Recommendation Systems
Experimental Validation
The researchers conducted extensive experiments on four real-world CDSR datasets from platforms like Amazon and Douban. HorizonRec consistently outperformed state-of-the-art sequential, cross-domain, and cross-domain sequential recommendation baselines across various performance metrics. This validates the framework’s ability to effectively integrate denoising diffusion into cross-domain sequential recommendation.
Ablation studies, where parts of the model were removed, confirmed that each component of HorizonRec—MDR, DPD, and the overall diffusion mechanism—plays a crucial role in its superior performance. The model also demonstrated robustness to key hyperparameters and showed significant computational efficiency, making it practical for real-world applications.
In essence, HorizonRec offers a powerful and efficient solution for personalized recommendations in complex, multi-domain environments. By harmonizing diverse domain signals through its unique diffusion-based refinement, it sets a new standard for cross-domain sequential recommendation. You can find more details about this research in the full paper available here.
