M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework

Abstract: Multi-domain recommendation and multi-task recommendation have demonstrated their effectiveness in leveraging common information from different domains and objectives for comprehensive user modeling. Nonetheless, the practical recommendation usually faces multiple domains and tasks simultaneously, which cannot be well-addressed by current methods. To this end, we introduce M3oE, an adaptive Multi-domain Multi-task Mixture-of-Experts recommendation framework. M3oE integrates multi-domain information, maps knowledge across domains and tasks, and optimizes multiple objectives. We leverage three mixture-of-experts modules to learn common, domain-aspect, and task-aspect user preferences respectively to address the complex dependencies among multiple domains and tasks in a disentangled manner. Additionally, we design a two-level fusion mechanism for precise control over feature extraction and fusion across diverse domains and tasks. The framework's adaptability is further enhanced by applying AutoML technique, which allows dynamic structure optimization. To the best of the authors' knowledge, our M3oE is the first effort to solve multi-domain multi-task recommendation self-adaptively. Extensive experiments on two benchmark datasets against diverse baselines demonstrate M3oE's superior performance. The implementation code is available to ensure reproducibility.

• The paper introduces a framework that leverages shared, domain, and task experts to overcome negative transfer and boost recommendation accuracy.
• It employs multi-objective AutoML optimization to balance performance and computational efficiency in diverse recommendation tasks.
• Experimental results show enhanced user engagement predictions and robustness compared to prior models like MMoE and STAR.

Overview of M3oE Framework

The paper "M3oE: Multi-Domain Multi-Task Mixture-of-Experts Recommendation Framework" introduces a comprehensive recommendation system designed to tackle complex tasks involving multiple domains and tasks. This framework aims to address the limitations of existing models that struggle with negative transfer, computational inefficiency, and the inability to effectively model inter-domain and cross-task relationships. The proposed framework utilizes a mixture-of-experts model to enhance performance by leveraging shared knowledge and optimizing through structural learning strategies.

Introduction

As digital environments generate vast amounts of user data, robust recommendation systems have become essential in understanding user preferences across various contexts. Traditional recommendation systems often fall short in multi-scenario and multi-task contexts due to their inability to capture and leverage shared information across diverse domains and tasks. The "M3oE" framework seeks to bridge these gaps by integrating domain-specific and task-specific insights into a cohesive model, thereby enhancing user personalization and recommendation accuracy.

Methodology

The methodology behind the M3oE framework is structured around a few key components:

1. Domain Representation Learning: This layer involves capturing domain-specific features that are crucial for accurate user preference modeling. It serves as a foundational stage that feeds into more complex expert layers.
2. Multi-View Expert Learning Layer: This consists of three types of expert mechanisms:
   • Shared Expert: Focuses on extracting generalized patterns across all domains, leveraging shared information fusion techniques.
   • Domain Expert: Deals with domain-specific intricacies, employing cross-domain fusion strategies and multi-domain reweighting mechanisms to maintain a balance between shared and domain-specific knowledge.
   • Task Expert: Concentrates on task-specific features, utilizing cross-task fusion and multi-task reweighting to optimize task-specific predictions.
3. Multi-Objective Optimization: The framework implements a bi-level optimization strategy using AutoML, which adapts the model structure to maximize the performance of the recommendation system across various objectives.

Experimentation and Results

The experimental evaluation of M3oE demonstrates its ability to outperform existing frameworks such as MMoE and STAR in multi-task and multi-domain contexts. The framework showed marked improvements in user engagement predictions and effectively tackled the domain and task seesaw phenomenon that plagues other models. The ablation study further corroborated the significance of each component of the framework, particularly highlighting the effectiveness of the optimization techniques in improving model capacity and predictive accuracy.

Comparative Analysis

The paper contrasts the M3oE framework against current multi-task and multi-domain models, underscoring its superior adaptability and efficiency. A notable finding is the model's robustness in maintaining performance across diverse tasks and domains without succumbing to negative transfer issues. The AutoML-driven optimization process ensures that the computational cost is managed effectively, making it feasible for large-scale deployments.

Related Work and Implications for Future Research

The M3oE framework builds upon existing research in multi-task and multi-domain recommendation systems while introducing novel integration strategies and optimization techniques. Its success suggests several avenues for future exploration, including the refinement of expert systems and the extension of the framework to accommodate even more complex interaction patterns. Furthermore, the adaptability of the framework to various content types and recommendation settings hints at broader applications within AI-driven personalization systems.

Conclusion

The "M3oE: Multi-Domain Multi-Task Mixture-of-Experts Recommendation Framework" presents a well-structured approach to improving recommendation accuracy across diverse domains and tasks. Through innovative use of mixture-of-experts modeling and automated optimization, the framework addresses critical challenges in current recommendation systems, demonstrating both theoretical and practical advancements. The insights gained from this research have the potential to inform future developments in personalized recommendation technologies.