Summary of Delivery Optimized Discovery in Behavioral User Segmentation Under Budget Constraint, by Harshita Chopra et al.
Delivery Optimized Discovery in Behavioral User Segmentation under Budget Constraint
by Harshita Chopra, Atanu R. Sinha, Sunav Choudhary, Ryan A. Rossi, Paavan Kumar Indela, Veda Pranav Parwatala, Srinjayee Paul, Aurghya Maiti
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an algorithm that addresses the challenges of delivering targeted messages to users based on their online behavioral footprints. The existing algorithms for discovering user segments are sophisticated but neglect the delivery component. The authors introduce a stochastic optimization-based algorithm that jointly optimizes discovery and delivery, taking into account budget constraints. The algorithm combines optimization techniques with a learning-based approach for discovery and leverages public and proprietary datasets to demonstrate its effectiveness in improving delivery metrics, reducing budget spend, and achieving strong predictive performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using online behavior to group people together based on what they do online. Companies want to send messages to these groups of people, but it’s hard because not everyone who should see a message actually does. The authors are trying to solve this problem by creating an algorithm that finds the right groups and sends messages to them in the most effective way possible, while also being mindful of the company’s budget. |
Keywords
* Artificial intelligence * Optimization
