Who Are the Best Adopters? User Selection Model for Free Trial Item Promotion

• URL: http://arxiv.org/abs/2202.09508v2
• Date: Tue, 22 Feb 2022 01:37:45 GMT
• Title: Who Are the Best Adopters? User Selection Model for Free Trial Item Promotion
• Abstract summary: We propose a novel free trial user selection model named SMILE. It is based on reinforcement learning (RL) where an agent actively selects specific adopters aiming to maximize the profit after free trials. Specifically, we design a tree structure to reformulate the action space, which allows us to select adopters from massive user space efficiently.
• Score: 41.58621061329645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increasingly fierce market competition, offering a free trial has become a potent stimuli strategy to promote products and attract users. By providing users with opportunities to experience goods without charge, a free trial makes adopters know more about products and thus encourages their willingness to buy. However, as the critical point in the promotion process, finding the proper adopters is rarely explored. Empirically winnowing users by their static demographic attributes is feasible but less effective, neglecting their personalized preferences. To dynamically match the products with the best adopters, in this work, we propose a novel free trial user selection model named SMILE, which is based on reinforcement learning (RL) where an agent actively selects specific adopters aiming to maximize the profit after free trials. Specifically, we design a tree structure to reformulate the action space, which allows us to select adopters from massive user space efficiently. The experimental analysis on three datasets demonstrates the proposed model's superiority and elucidates why reinforcement learning and tree structure can improve performance. Our study demonstrates technical feasibility for constructing a more robust and intelligent user selection model and guides for investigating more marketing promotion strategies.

Related papers

• Optimizing Monetization Strategies for Generative AI Firms: Implications for Search Engagement [0.0]
  We show that alternative advertising-supported monetization models can help GenAI firms recover costs while maintaining search query engagement.<n>We develop a framework wherein the introduction of advertising-supported monetization models influences user upgrading and downgrading decisions.<n>We show that affective and cognitive evaluations mediate preference for advertising-supported models, with temporal intrusiveness, but not visual, moderating these effects.
  arXiv  Detail & Related papers  (2026-07-30T19:11:01Z)
• ChatShopBuddy: Towards Reliable Conversational Shopping Agents via Reinforcement Learning [88.87305027895657]
  This work investigates Reinforcement Learning-based optimization for shopping agents in real-world scenarios.<n>Agents must simultaneously satisfy multiple interdependent objectives spanning objective metrics (product correctness), subjective qualities (persuasiveness), outcome rewards (final response quality), and process rewards (tool efficiency)<n>Experiments demonstrate that our RL-trained agent, ChatShopBuddy, consistently outperforms larger models relying on generic reasoning.
  arXiv  Detail & Related papers  (2026-03-06T09:18:51Z)
• Tree of Preferences for Diversified Recommendation [54.183647833064136]
  We study diversified recommendation from a data-bias perspective.<n>Inspired by the outstanding performance of large language models (LLMs) in zero-shot inference leveraging world knowledge, we propose a novel approach.
  arXiv  Detail & Related papers  (2025-12-24T04:13:17Z)
• UserRL: Training Interactive User-Centric Agent via Reinforcement Learning [104.63494870852894]
  Reinforcement learning (RL) has shown promise in training agentic models that engage in dynamic, multi-turn interactions.<n>We propose UserRL, a unified framework for training and evaluating user-centric abilities through standardized gym environments.
  arXiv  Detail & Related papers  (2025-09-24T03:33:20Z)
• Heterogeneous Causal Learning for Optimizing Aggregated Functions in User Growth [0.7100520098029438]
  We propose a novel treatment effect optimization methodology to enhance user growth marketing.<n>By leveraging deep learning, our algorithm learns from past experiments to optimize user selection and reward allocation.<n>We experimentally demonstrate that our proposed constrained and direct optimization algorithms significantly outperform state-of-the-art methods by over $20%$.
  arXiv  Detail & Related papers  (2025-07-07T22:08:45Z)
• Diversified Batch Selection for Training Acceleration [68.67164304377732]
  A prevalent research line, known as online batch selection, explores selecting informative subsets during the training process. vanilla reference-model-free methods involve independently scoring and selecting data in a sample-wise manner. We propose Diversified Batch Selection (DivBS), which is reference-model-free and can efficiently select diverse and representative samples.
  arXiv  Detail & Related papers  (2024-06-07T12:12:20Z)
• User Welfare Optimization in Recommender Systems with Competing Content Creators [65.25721571688369]
  In this study, we perform system-side user welfare optimization under a competitive game setting among content creators. We propose an algorithmic solution for the platform, which dynamically computes a sequence of weights for each user based on their satisfaction of the recommended content. These weights are then utilized to design mechanisms that adjust the recommendation policy or the post-recommendation rewards, thereby influencing creators' content production strategies.
  arXiv  Detail & Related papers  (2024-04-28T21:09:52Z)
• Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties [49.351577714596544]
  We propose a human-AI collaborative Bayesian framework to incorporate expert preferences about unmeasured abstract properties into surrogate modeling. We provide an efficient strategy that can also handle any incorrect/misleading expert bias in preferential judgments.
  arXiv  Detail & Related papers  (2024-02-27T09:23:13Z)
• Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions [5.50110172922112]
  We propose a framework of multiple treatment promotion decision making by modeling each customer's direct and enduring response. First, we propose a customer direct and enduring effect (CDEE) model which predicts the customer direct and enduring response. With the help of the CDEE, we personalize incentive allocation to optimize the enduring effect while keeping the cost under the budget.
  arXiv  Detail & Related papers  (2022-07-23T07:13:57Z)
• Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction [97.99938802797377]
  Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in recommender systems. Recent deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success. We propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper.
  arXiv  Detail & Related papers  (2022-06-28T03:28:15Z)
• Generalized Strategic Classification and the Case of Aligned Incentives [16.607142366834015]
  We argue for a broader perspective on what accounts for strategic user behavior. Our model subsumes most current models, but includes other novel settings. We show how our results and approach can extend to the most general case.
  arXiv  Detail & Related papers  (2022-02-09T09:36:09Z)
• 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)
• Targeted Active Learning for Bayesian Decision-Making [15.491942513739676]
  We argue that when acquiring samples sequentially, separating learning and decision-making is sub-optimal. We introduce a novel active learning strategy which takes the down-the-line decision problem into account. Specifically, we introduce a novel active learning criterion which maximizes the expected information gain on the posterior distribution of the optimal decision.
  arXiv  Detail & Related papers  (2021-06-08T09:05:43Z)
• Rebuilding Trust in Active Learning with Actionable Metrics [77.99796068970569]
  Active Learning (AL) is an active domain of research, but is seldom used in the industry despite the pressing needs. This is in part due to a misalignment of objectives, while research strives at getting the best results on selected datasets. We present various actionable metrics to help rebuild trust of industrial practitioners in Active Learning.
  arXiv  Detail & Related papers  (2020-12-18T09:34:59Z)
• Heterogeneous Causal Learning for Effectiveness Optimization in User Marketing [2.752817022620644]
  We propose a treatment effect optimization methodology for user marketing. This algorithm learns from past experiments and utilizes novel optimization methods to optimize cost efficiency with respect to user selection. Our proposed constrained and direct optimization algorithms outperform by 24.6% compared with the best performing method in prior art and baseline methods.
  arXiv  Detail & Related papers  (2020-04-21T01:34:34Z)

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.