Summary of Algorithmic Content Selection and the Impact Of User Disengagement, by Emilio Calvano et al.
Algorithmic Content Selection and the Impact of User Disengagement
by Emilio Calvano, Nika Haghtalab, Ellen Vitercik, Eric Zhao
First submitted to arxiv on: 17 Oct 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 A novel approach is proposed in this paper to address the content selection dilemma faced by digital services. The authors develop a multi-armed bandit model that takes into account user engagement dynamics, recognizing that users can disengage when satisfied. This framework balances immediate revenue gains from high-reward content with long-term benefits of maintaining user engagement. Specifically, the model incorporates a forgetting mechanism to capture how users’ preferences and behaviors evolve over time. The authors evaluate their approach using real-world data and demonstrate significant improvements in content selection compared to traditional methods. This research has important implications for digital services seeking to optimize content recommendations and maintain user loyalty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Digital services need to decide which content to show users, but they face a big problem. They want to make money now by showing popular content, but if they don’t keep users engaged, those users will leave and stop generating revenue in the long run. A team of researchers has come up with a new way to solve this problem. They created a special kind of computer model that can handle how people’s preferences change over time. This model is better than the old ones because it takes into account when people get bored or unhappy with the content they’re seeing, and adjusts what’s shown accordingly. |
