Summary of Model-free Approximate Bayesian Learning For Large-scale Conversion Funnel Optimization, by Garud Iyengar and Raghav Singal
Model-Free Approximate Bayesian Learning for Large-Scale Conversion Funnel Optimization
by Garud Iyengar, Raghav Singal
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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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 presents a novel approach to personalized marketing campaigns by modeling consumer behavior as a conversion funnel. The authors propose an attribution-based decision-making algorithm, called model-free approximate Bayesian learning, which is designed to maximize adoption probability for new products. This algorithm updates its belief over the value of each state-specific intervention as it interacts with consumers. While the algorithm inherits the scalability and interpretability of Thompson sampling, it achieves asymptotic optimality and outperforms traditional approaches in extensive simulations. The proposed method is particularly useful in modern-day marketing campaigns where flexibility in choosing ad actions based on consumer states is crucial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to convince people to buy a new product. You want to send them personalized ads that will really make them want to buy it. But, how do you know what kind of ad to send? The answer lies in understanding how people behave when they see your ads. The researchers in this paper created a model that looks at how people react to different types of ads and then uses that information to decide which ad to show each person next. They tested their method on real data and found it worked really well! This could be super helpful for companies trying to market new products. |
Keywords
* Artificial intelligence * Probability
