The paper introduces Progressive Layered Extraction (PLE), a novel Multi-Task Learning (MTL) model, aimed at overcoming the challenges in recommender systems, particularly the seesaw phenomenon and negative transfer. Traditional MTL models often struggle with performance degradation due to complex task correlations within real-world recommender systems.
In the Customized Gate Control (CGC) model, designed for Multi-Task Learning (MTL), the architecture incorporates a sophisticated mechanism to efficiently manage the contributions of multiple expert modules to various tasks. Here's a detailed look at the gating networks and the data flow within the model, especially focusing on the scenario with m experts and n tasks.
m): The model includes m expert modules, each designed to capture different aspects or features of the input data.d for simplicity, each expert's output shape is [Batch Size, d].n): Corresponding to n tasks, the model employs n gating networks. Each gating network is dedicated to a specific task, determining the relevance and contribution of each expert's output to that task.m experts and generates a weight vector. These weights dictate how much each expert's output should influence the task-specific output.m experts, resulting in a shape of [Batch Size, m * d] for the input to each gating network.[Batch Size, m], representing the contribution weights of the m experts towards its specific task.[Batch Size, Input Features], where "Input Features" is the dimensionality of the input data.m experts, resulting in m output vectors, each with shape [Batch Size, d].[Batch Size, m * d].n gating networks processes the concatenated expert outputs, producing n weight vectors, each with shape [Batch Size, m].n task-specific representations, each potentially with shape [Batch Size, d], assuming the weights are applied element-wise to each expert's output and then summed.The CGC model's utilization of n gating networks allows for a dynamic and flexible integration of knowledge from m expert modules into n task-specific outputs. This architecture ensures that each task can benefit from the most relevant features extracted by the experts, tailored to its specific requirements, thereby enhancing the model's overall performance and effectiveness in MTL scenarios.
PLE is designed with a unique sharing structure to efficiently manage shared and task-specific components, addressing the seesaw phenomenon and negative transfer. It uses a progressive routing mechanism to gradually extract deeper semantic knowledge, enhancing joint representation learning and information routing across tasks.
Progressive Layered Extraction builds upon the concept of gating networks to establish a powerful multi-task learning solution for personalized recommendation systems. Let's understand how these components fit together:
Gating networks provide the fundamental mechanism that lets PLE dynamically modulate the flow of information within the model. Their core strength is selective, customizable "filtering" for what information passes from one part of a network to another.
PLE's innovation lies in how it leverages gating networks. For each expert layer within PLE, there's a separate, dedicated gating network, forming the heart of the Customized Gate Control Model. - Customization is Key: Gating isn't generic — decisions are dependent on the user, the task, and features relevant to that task.
PLE brings everything together into a multi-tiered structure: - Shared Representation: At the base, PLE learns a unified representation of user and item characteristics. - Expert Layers: Multiple expert networks specialize in various recommendation tasks (e.g., click-through rate prediction, rating prediction). - Custom Gate Control: Gates, coupled with each expert layer, use the shared representation to create custom input mixes for each expert.
Key takeaway: Gating networks are the building blocks. The specific, tailored way PLE assigns a dynamic gate to each expert network is what distinguishes the Customized Gate Control Model. These components then form the heart of the larger Progressive Layered Extraction architecture.
Extensive experiments demonstrate PLE's superiority over state-of-the-art MTL models in handling both complicatedly and normally correlated tasks. Applied to Tencent's real-world video recommender system, PLE showed significant improvements in view-count and watch time, indicating its practical effectiveness and adaptability to various scenarios beyond recommendations.
PLE represents a significant advancement in MTL for recommender systems, effectively addressing common challenges and setting a new benchmark for future research and applications in this domain.
Created 2024-02-14T19:11:32-08:00, updated 2024-02-22T04:55:37-08:00 · History · Edit