Summary of Efficient Feature Interactions with Transformers: Improving User Spending Propensity Predictions in Gaming, by Ved Prakash et al.
Efficient Feature Interactions with Transformers: Improving User Spending Propensity Predictions in Gaming
by Ved Prakash, Kartavya Kothari
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper proposes a solution to predict user spending propensity in a fantasy sports platform, Dream11, which hosts various real-life sports events for its 200M+ user base. The goal is to identify users who are likely to spend more and utilize this information for downstream applications such as upselling or personalizing product listings. The authors discuss the challenges of predicting user spending propensity, highlighting the importance of this problem in a real-money gaming setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In Dream11, users can create their own virtual teams for sports events, paying an entry amount to participate in various contests. Researchers want to predict how much users will spend so they can offer personalized products and promotions. This paper explains why it’s important to figure out who will spend more and what they’ll do with that information. |
