Hidden Representation Clustering with Multi-Task Representation Learning towards Robust Online Budget Allocation

• URL: http://arxiv.org/abs/2506.00959v1
• Date: Sun, 01 Jun 2025 11:09:07 GMT
• Title: Hidden Representation Clustering with Multi-Task Representation Learning towards Robust Online Budget Allocation
• Authors: Xiaohan Wang, Yu Zhang, Guibin Jiang, Bing Cheng, Wei Lin,
• Abstract summary: Marketing optimization, commonly formulated as an online budget allocation problem, has emerged as a pivotal factor in driving user growth.<n>This paper proposes a novel approach that solves the problem from the cluster perspective.
• Score: 25.524699372749957
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Marketing optimization, commonly formulated as an online budget allocation problem, has emerged as a pivotal factor in driving user growth. Most existing research addresses this problem by following the principle of 'first predict then optimize' for each individual, which presents challenges related to large-scale counterfactual prediction and solving complexity trade-offs. Note that the practical data quality is uncontrollable, and the solving scale tends to be tens of millions. Therefore, the existing approaches make the robust budget allocation non-trivial, especially in industrial scenarios with considerable data noise. To this end, this paper proposes a novel approach that solves the problem from the cluster perspective. Specifically, we propose a multi-task representation network to learn the inherent attributes of individuals and project the original features into high-dimension hidden representations through the first two layers of the trained network. Then, we divide these hidden representations into $K$ groups through partitioning-based clustering, thus reformulating the problem as an integer stochastic programming problem under different total budgets. Finally, we distill the representation module and clustering model into a multi-category model to facilitate online deployment. Offline experiments validate the effectiveness and superiority of our approach compared to six state-of-the-art marketing optimization algorithms. Online A/B tests on the Meituan platform indicate that the approach outperforms the online algorithm by 0.53% and 0.65%, considering order volume (OV) and gross merchandise volume (GMV), respectively.

Related papers

• Accelerating Spectral Clustering under Fairness Constraints [56.865810822418744]
  We present a new efficient method for fair spectral clustering (Fair SC) by casting the Fair SC problem within the difference of convex functions (DC) framework.<n>We show that each associated subproblem can be solved efficiently, resulting in higher computational efficiency compared to prior work.
  arXiv  Detail & Related papers  (2025-06-09T18:46:27Z)
• Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks [71.30914500714262]
  Cross-layer resource allocation over mobile edge computing (MEC)-aided cell-free networks can sufficiently exploit the transmitting and computing resources to promote the data rate.<n>Joint subcarrier allocation and beamforming optimization are investigated for the MEC-aided cell-free network from the perspective of deep learning.
  arXiv  Detail & Related papers  (2024-12-21T10:18:55Z)
• Multi-Task Combinatorial Bandits for Budget Allocation [7.52750519688457]
  Today's top advertisers typically manage hundreds of campaigns simultaneously and consistently launch new ones throughout the year. A crucial challenge for marketing managers is determining the optimal allocation of limited budgets across various ad lines in each campaign to maximize cumulative returns. We propose to formulate budget allocation as a multi-task bandit problem and introduce a novel online budget allocation system.
  arXiv  Detail & Related papers  (2024-08-31T23:19:49Z)
• LoRA-Ensemble: Efficient Uncertainty Modelling for Self-Attention Networks [52.46420522934253]
  We introduce LoRA-Ensemble, a parameter-efficient ensembling method for self-attention networks.<n>The method not only outperforms state-of-the-art implicit techniques like BatchEnsemble, but even matches or exceeds the accuracy of an Explicit Ensemble.
  arXiv  Detail & Related papers  (2024-05-23T11:10:32Z)
• On the Necessity of Collaboration for Online Model Selection with Decentralized Data [53.244188985271606]
  We consider online model selection with decentralized data over $M$ clients, and study the necessity of collaboration among clients. Our results show (i) collaboration is unnecessary in the absence of computational constraints on clients; (ii) collaboration is necessary if the computational cost on each client is limited to $o(K)$, where $K$ is the number of candidate hypothesis spaces.
  arXiv  Detail & Related papers  (2024-04-15T06:32:28Z)
• CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network [53.72046586512026]
  We propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net) It captures the high-level features and local structure of each view by incorporating the view-specific deep encoders and graph embedding strategy into a framework. Based on the human cognition, i.e., learning from easy to hard, it introduces a self-paced strategy to select the most confident samples for model training.
  arXiv  Detail & Related papers  (2024-03-28T15:45:03Z)
• One-step Multi-view Clustering with Diverse Representation [47.41455937479201]
  We propose a one-step multi-view clustering with diverse representation method, which incorporates multi-view learning and $k$-means into a unified framework. We develop an efficient optimization algorithm with proven convergence to solve the resultant problem.
  arXiv  Detail & Related papers  (2023-06-08T02:52:24Z)
• End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location [10.130342722193204]
  Facility location problems (FLPs) are a typical class of NP-hard optimization problems, which are widely seen in the supply chain and logistics. In this paper, we consider the multi-objective facility location problem (MO-FLP) that simultaneously minimizes the overall cost and maximizes the system reliability. Two graph neural networks are constructed to learn the implicit graph representation on nodes and edges.
  arXiv  Detail & Related papers  (2022-10-27T07:15:55Z)
• Learning from Images: Proactive Caching with Parallel Convolutional Neural Networks [94.85780721466816]
  A novel framework for proactive caching is proposed in this paper. It combines model-based optimization with data-driven techniques by transforming an optimization problem into a grayscale image. Numerical results show that the proposed scheme can reduce 71.6% computation time with only 0.8% additional performance cost.
  arXiv  Detail & Related papers  (2021-08-15T21:32:47Z)
• The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems [7.433931244705934]
  We consider a data-driven setting in which the reward and resource consumption of each request are generated using an input model unknown to the decision maker. We design general class of algorithms that attain good performance in various input models without knowing which type of input they are facing. Our algorithms operate in the Lagrangian dual space: they maintain a dual multiplier for each resource that is updated using online mirror descent.
  arXiv  Detail & Related papers  (2020-11-18T18:39:17Z)
• A Fast Graph Neural Network-Based Method for Winner Determination in Multi-Unit Combinatorial Auctions [44.14410999484577]
  Auction (CA) is an efficient mechanism for resource allocation in different fields, including cloud computing. The problem of allocating items among the bidders to maximize the auctioneers" revenue is NP-complete to solve and inapproximable. We propose leveraging machine learning (ML) techniques to develop a novel low-complexity algorithm for solving this problem with negligible revenue loss.
  arXiv  Detail & Related papers  (2020-09-29T00:22:37Z)
• Decentralized MCTS via Learned Teammate Models [89.24858306636816]
  We present a trainable online decentralized planning algorithm based on decentralized Monte Carlo Tree Search. We show that deep learning and convolutional neural networks can be employed to produce accurate policy approximators.
  arXiv  Detail & Related papers  (2020-03-19T13:10:20Z)

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.