Causal Predictive Optimization and Generation for Business AI

• URL: http://arxiv.org/abs/2505.09847v2
• Date: Wed, 21 May 2025 16:12:30 GMT
• Title: Causal Predictive Optimization and Generation for Business AI
• Abstract summary: We introduce a principled approach to sales optimization and business AI, namely the Causal Predictive Optimization and Generation.<n>We detail the implementation and deployment of the system in LinkedIn, showcasing significant wins over legacy systems and sharing learning and insight broadly applicable to this field.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The sales process involves sales functions converting leads or opportunities to customers and selling more products to existing customers. The optimization of the sales process thus is key to success of any B2B business. In this work, we introduce a principled approach to sales optimization and business AI, namely the Causal Predictive Optimization and Generation, which includes three layers: 1) prediction layer with causal ML 2) optimization layer with constraint optimization and contextual bandit 3) serving layer with Generative AI and feedback-loop for system enhancement. We detail the implementation and deployment of the system in LinkedIn, showcasing significant wins over legacy systems and sharing learning and insight broadly applicable to this field.

Related papers

• From Prediction to Incrementality: Causal Optimization for Large-Scale Targeting and Recommendation [1.8192758370546824]
  We present a decision-centric framework that instead optimize causal effects under global constraints.<n>We evaluate it with offline simulations on a public bandit dataset, targeted architectural ablations, and an online A/B test on LinkedIn Feed marketing traffic.<n>The end-to-end treatment policy delivered a statistically significant $+7.20%$ lift in the primary long-term-value metric.
  arXiv  Detail & Related papers  (2026-08-10T19:54:08Z)
• Causal-Aware Foundation-Model for Bilevel Optimization in Discrete Choice Settings [5.508443859567532]
  We propose a constrained triple-head price optimization (C3PO) network to solve a bilevel decision problem.<n>C3PO integrates imitation learning of prices, multi-task learning of revenue responses, and in context learning of price elasticity.<n>We deploy the tuned foundation model for optimal pricing in real-world applications such as healthcare, tender pricing, airline ancillary pricing, and other domains.
  arXiv  Detail & Related papers  (2026-05-07T20:55:47Z)
• Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations [98.44542103979735]
  First-order gradient descent methods, epitomized by gradient descent (SGD) and Adam, serve as the cornerstone of modern training pipelines.<n>Large-scale model training, stringent privacy requirements, and distributed learning paradigms expose critical limitations in these conventional approaches regarding privacy protection and memory efficiency.<n>We retrospectively analyze the evolutionary trajectory of deep learning optimization algorithms and present a comprehensive empirical evaluation of mainstreams across diverse model architectures and training scenarios.<n>We distill key emerging trends and fundamental design trade-offs, pinpointing promising directions for future research.
  arXiv  Detail & Related papers  (2026-04-14T17:01:36Z)
• DeepRule: An Integrated Framework for Automated Business Rule Generation via Deep Predictive Modeling and Hybrid Search Optimization [12.68443002994035]
  DeepRule is an integrated framework for automated business rule generation in retail assortment and pricing optimization.<n>We design a hybrid knowledge fusion engine employing large language models (LLMs) for deep semantic parsing of unstructured text.<n>We validate the framework in real retail environments achieving higher profits versus systematic B2C baselines while ensuring operational feasibility.
  arXiv  Detail & Related papers  (2025-12-03T09:40:33Z)
• A Multi-objective Optimization Approach for Feature Selection in Gentelligent Systems [62.08647860272078]
  This paper uses the term "Gentelligent system" to refer to systems that incorporate inherent component information and automated mechanisms.<n>By implementing reliable fault detection methods, manufacturers can achieve several benefits, including improved product quality, increased yield, and reduced production costs.
  arXiv  Detail & Related papers  (2025-11-20T23:50:55Z)
• OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System [61.12400636463362]
  OnePiece is a unified framework that seamlessly integrates LLM-style context engineering and reasoning into both retrieval and ranking models.<n>OnePiece has been deployed in the main personalized search scenario of Shopee and achieves consistent online gains across different key business metrics.
  arXiv  Detail & Related papers  (2025-09-22T17:59:07Z)
• OneSearch: A Preliminary Exploration of the Unified End-to-End Generative Framework for E-commerce Search [43.94443394870866]
  OneSearch is the first industrial-deployed end-to-end generative framework for e-commerce search.<n>OneSearch reduces operational expenditure by 75.40% and improves Model FLOPs Utilization from 3.26% to 27.32%.<n>The system has been successfully deployed across multiple search scenarios in Kuaishou, serving millions of users.
  arXiv  Detail & Related papers  (2025-09-03T11:50:04Z)
• ORMind: A Cognitive-Inspired End-to-End Reasoning Framework for Operations Research [53.736407871322314]
  We introduce ORMind, a cognitive-inspired framework that enhances optimization through counterfactual reasoning.<n>Our approach emulates human cognition, implementing an end-to-end workflow that transforms requirements into mathematical models and executable code.<n>It is currently being tested internally in Lenovo's AI Assistant, with plans to enhance optimization capabilities for both business and consumer customers.
  arXiv  Detail & Related papers  (2025-06-02T05:11:21Z)
• DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal [55.13854171147104]
  Large Language Models (LLMs) have revolutionized various domains, including natural language processing, data analysis, and software development.<n>We present Dynamic Action Re-Sampling (DARS), a novel inference time compute scaling approach for coding agents.<n>We evaluate our approach on SWE-Bench Lite benchmark, demonstrating that this scaling strategy achieves a pass@k score of 55% with Claude 3.5 Sonnet V2.
  arXiv  Detail & Related papers  (2025-03-18T14:02:59Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization [65.64108848398696]
  We introduce a preference optimization (PO) process to enhance the multimodal reasoning capabilities of MLLMs.<n>Specifically, we design an automated preference data construction pipeline to create MMPR, a high-quality, large-scale multimodal reasoning preference dataset.<n>We explore integrating PO with MLLMs, developing a simple yet effective method, termed Mixed Preference Optimization (MPO), which boosts multimodal CoT performance.
  arXiv  Detail & Related papers  (2024-11-15T18:59:27Z)
• Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System [75.25394449773052]
  Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving.<n>Yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods.<n>We present Optima, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness.
  arXiv  Detail & Related papers  (2024-10-10T17:00:06Z)
• End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling [12.160403526724476]
  We propose a novel End-to-End Cost-Effective Incentive Recommendation (E3IR) model under budget constraints. Specifically, our methods consist of two modules, i.e., the uplift prediction module and the differentiable allocation module. Our E3IR improves allocation performance compared to existing two-stage approaches.
  arXiv  Detail & Related papers  (2024-08-21T13:48:00Z)
• Neural Optimization with Adaptive Heuristics for Intelligent Marketing System [1.3079444139643954]
  We propose a general framework for marketing AI systems, the Neural Optimization with Adaptive Heuristics (Noah) framework. Noah is the first general framework for marketing optimization that considers both to-business (2B) and to-consumer (2C) products, as well as both owned and paid channels. We describe key modules of the Noah framework, including prediction, optimization, and adaptive audiences, providing examples for bidding and content optimization.
  arXiv  Detail & Related papers  (2024-05-17T01:44:30Z)
• LMaaS: Exploring Pricing Strategy of Large Model as a Service for Communication [11.337245234301857]
  We argue that a pay-as-you-go service mode will be suitable in this context, referred to as Large Model as a Service (LM) We propose an Iterative Model Pricing (IMP) algorithm that optimize the prices of large models iteratively by reasoning customers' future rental decisions. In the second step, we optimize customers' selection decisions by designing a robust selecting and renting algorithm.
  arXiv  Detail & Related papers  (2024-01-05T07:19:19Z)
• Benchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization Regime [59.27851754647913]
  Predictive optimization is the precise modeling of many real-world applications, including energy cost-aware scheduling and budget allocation on advertising. We develop a modular framework to benchmark 11 existing PtO/PnO methods on 8 problems, including a new industrial dataset for advertising. Our study shows that PnO approaches are better than PtO on 7 out of 8 benchmarks, but there is no silver bullet found for the specific design choices of PnO.
  arXiv  Detail & Related papers  (2023-11-13T13:19:34Z)
• Model-based Causal Bayesian Optimization [74.78486244786083]
  We introduce the first algorithm for Causal Bayesian Optimization with Multiplicative Weights (CBO-MW) We derive regret bounds for CBO-MW that naturally depend on graph-related quantities. Our experiments include a realistic demonstration of how CBO-MW can be used to learn users' demand patterns in a shared mobility system.
  arXiv  Detail & Related papers  (2023-07-31T13:02:36Z)
• A framework for massive scale personalized promotion [18.12992386307048]
  Technology companies building consumer-facing platforms may have access to massive-scale user population. Promotion with quantifiable incentive has become a popular approach for increasing active users on such platforms. This paper proposes a practical two-stage framework that can optimize the ROI of various massive-scale promotion campaigns.
  arXiv  Detail & Related papers  (2021-08-27T03:03:18Z)
• A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine Learning Approach [0.0]
  Predicting the outcome of business to business (B2B) sales is a core part of successful business management. We propose a thorough data-driven Machine Learning (ML) workflow on a cloud-based computing platform: Microsoft Azure ML. Our results implied that decision-making based on the ML predictions is more accurate and brings a higher monetary value.
  arXiv  Detail & Related papers  (2020-02-04T18:01:24Z)

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.