Deep Task-specific Bottom Representation Network for Multi-Task Recommendation

• URL: http://arxiv.org/abs/2308.05996v2
• Date: Fri, 18 Aug 2023 01:37:14 GMT
• Title: Deep Task-specific Bottom Representation Network for Multi-Task Recommendation
• Authors: Qi Liu, Zhilong Zhou, Gangwei Jiang, Tiezheng Ge, Defu Lian
• Abstract summary: We propose the Deep Task-specific Bottom Representation Network (DTRN) to alleviate the negative transfer problem. The two proposed modules can achieve the purpose of getting task-specific bottom representation to relieve tasks' mutual interference.
• Score: 36.128708266100645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural-based multi-task learning (MTL) has gained significant improvement, and it has been successfully applied to recommendation system (RS). Recent deep MTL methods for RS (e.g. MMoE, PLE) focus on designing soft gating-based parameter-sharing networks that implicitly learn a generalized representation for each task. However, MTL methods may suffer from performance degeneration when dealing with conflicting tasks, as negative transfer effects can occur on the task-shared bottom representation. This can result in a reduced capacity for MTL methods to capture task-specific characteristics, ultimately impeding their effectiveness and hindering the ability to generalize well on all tasks. In this paper, we focus on the bottom representation learning of MTL in RS and propose the Deep Task-specific Bottom Representation Network (DTRN) to alleviate the negative transfer problem. DTRN obtains task-specific bottom representation explicitly by making each task have its own representation learning network in the bottom representation modeling stage. Specifically, it extracts the user's interests from multiple types of behavior sequences for each task through the parameter-efficient hypernetwork. To further obtain the dedicated representation for each task, DTRN refines the representation of each feature by employing a SENet-like network for each task. The two proposed modules can achieve the purpose of getting task-specific bottom representation to relieve tasks' mutual interference. Moreover, the proposed DTRN is flexible to combine with existing MTL methods. Experiments on one public dataset and one industrial dataset demonstrate the effectiveness of the proposed DTRN.

Related papers

• Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning [85.66907881270785]
  We propose a data curriculum method, namely Data-CUBE, that arranges the orders of all the multi-task data for training. In the task level, we aim to find the optimal task order to minimize the total cross-task interference risk. In the instance level, we measure the difficulty of all instances per task, then divide them into the easy-to-difficult mini-batches for training.
  arXiv  Detail & Related papers  (2024-01-07T18:12:20Z)
• Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond [62.406687088097605]
  Multi-Task Learning (MTL) is a framework, where multiple related tasks are learned jointly and benefit from a shared representation space. We show that MTL can be successful with classification tasks with little, or non-overlapping annotations. We propose a novel approach, where knowledge exchange is enabled between the tasks via distribution matching.
  arXiv  Detail & Related papers  (2024-01-02T14:18:11Z)
• Mitigating Task Interference in Multi-Task Learning via Explicit Task Routing with Non-Learnable Primitives [19.90788777476128]
  Multi-task learning (MTL) seeks to learn a single model to accomplish multiple tasks by leveraging shared information among the tasks. Existing MTL models have been known to suffer from negative interference among tasks. We propose ETR-NLP to mitigate task interference through a synergistic combination of non-learnable primitives and explicit task routing.
  arXiv  Detail & Related papers  (2023-08-03T22:34:16Z)
• Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks [69.38572074372392]
  We present the first results proving that feature learning occurs during training with a nonlinear model on multiple tasks. Our key insight is that multi-task pretraining induces a pseudo-contrastive loss that favors representations that align points that typically have the same label across tasks.
  arXiv  Detail & Related papers  (2023-07-13T16:39:08Z)
• Sample-Level Weighting for Multi-Task Learning with Auxiliary Tasks [0.0]
  Multi-task learning (MTL) can improve the generalization performance of neural networks by sharing representations with related tasks. MTL can also degrade performance through harmful interference between tasks. We propose SLGrad, a sample-level weighting algorithm for multi-task learning with auxiliary tasks.
  arXiv  Detail & Related papers  (2023-06-07T15:29:46Z)
• Task Aware Feature Extraction Framework for Sequential Dependence Multi-Task Learning [1.0765359420035392]
  We analyze sequential dependence MTL from rigorous mathematical perspective. We propose a Task Aware Feature Extraction (TAFE) framework for sequential dependence MTL.
  arXiv  Detail & Related papers  (2023-01-06T13:12:59Z)
• AdaTask: A Task-aware Adaptive Learning Rate Approach to Multi-task Learning [19.201899503691266]
  We measure the task dominance degree of a parameter by the total updates of each task on this parameter. We propose a Task-wise Adaptive learning rate approach, AdaTask, to separate the emphaccumulative gradients and hence the learning rate of each task. Experiments on computer vision and recommender system MTL datasets demonstrate that AdaTask significantly improves the performance of dominated tasks.
  arXiv  Detail & Related papers  (2022-11-28T04:24:38Z)
• Multi-Task Learning as a Bargaining Game [63.49888996291245]
  In Multi-task learning (MTL), a joint model is trained to simultaneously make predictions for several tasks. Since the gradients of these different tasks may conflict, training a joint model for MTL often yields lower performance than its corresponding single-task counterparts. We propose viewing the gradients combination step as a bargaining game, where tasks negotiate to reach an agreement on a joint direction of parameter update.
  arXiv  Detail & Related papers  (2022-02-02T13:21:53Z)
• Semi-supervised Multi-task Learning for Semantics and Depth [88.77716991603252]
  Multi-Task Learning (MTL) aims to enhance the model generalization by sharing representations between related tasks for better performance. We propose the Semi-supervised Multi-Task Learning (MTL) method to leverage the available supervisory signals from different datasets. We present a domain-aware discriminator structure with various alignment formulations to mitigate the domain discrepancy issue among datasets.
  arXiv  Detail & Related papers  (2021-10-14T07:43:39Z)
• Task Uncertainty Loss Reduce Negative Transfer in Asymmetric Multi-task Feature Learning [0.0]
  Multi-task learning (MTL) can improve task performance overall relative to single-task learning (STL), but can hide negative transfer (NT) Asymmetric multitask feature learning (AMTFL) is an approach that tries to address this by allowing tasks with higher loss values to have smaller influence on feature representations for learning other tasks. We present examples of NT in two datasets (image recognition and pharmacogenomics) and tackle this challenge by using aleatoric homoscedastic uncertainty to capture the relative confidence between tasks, and set weights for task loss.
  arXiv  Detail & Related papers  (2020-12-17T13:30:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.