Summary of Improving the Prediction Of Individual Engagement in Recommendations Using Cognitive Models, by Roderick Seow et al.
Improving the Prediction of Individual Engagement in Recommendations Using Cognitive Models
by Roderick Seow, Yunfan Zhao, Duncan Wood, Milind Tambe, Cleotilde Gonzalez
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA)
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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 cognitive model based on Instance-Based Learning (IBL) Theory is used to predict how behaviors change over time and in response to interventions. The IBL model, which reflects human decision-making processes, outperforms general time-series forecasters like LSTMs in predicting the dynamics of individuals’ states. Additionally, the IBL model provides estimates of the volatility in individuals’ states and their sensitivity to interventions, improving the efficiency of training other time series models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers used real-world data from a maternal health program to test the IBL model’s ability to predict how behaviors change over time. The IBL model was found to be better at predicting individual behavior than traditional time-series forecasters like LSTMs. This research could help public health programs make more informed decisions about when and where to allocate interventions. |
Keywords
» Artificial intelligence » Time series
