Summary of Adaptively Learning to Select-rank in Online Platforms, by Jingyuan Wang et al.
Adaptively Learning to Select-Rank in Online Platforms
by Jingyuan Wang, Perry Dong, Ying Jin, Ruohan Zhan, Zhengyuan Zhou
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 a novel approach to adaptively ranking items for heterogeneous users, addressing the challenge of personalizing user experience across various online platforms. The researchers develop a user response model that considers diverse user preferences and varying item positions to optimize overall user satisfaction with the ranked list. This is framed within a contextual bandits framework, where each ranked list is an action. The algorithm incorporates an upper confidence bound to adjust predicted user satisfaction scores and selects the ranking action that maximizes these adjusted scores, efficiently solved via maximum weight imperfect matching. The proposed approach achieves a cumulative regret bound of O(d√NKT) for ranking K out of N items in a d-dimensional context space over T rounds, under the assumption that user responses follow a generalized linear model. This is demonstrated to alleviate dependence on the ambient action space, which grows exponentially with N and K, making direct application of existing adaptive learning algorithms infeasible. The algorithm is experimentally shown to outperform baseline approaches on both simulated and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps online platforms like e-commerce sites or content streaming services give users a personalized experience by ranking items that they are most likely to like. It does this by developing a special model that takes into account different user preferences and the way people react to items in different positions on the list. The researchers use a framework called contextual bandits, where each ranked list is like an action that can be taken. They adjust their predictions based on how well they think users will respond to certain rankings and choose the one that maximizes satisfaction. This approach has been shown to perform better than other methods in experiments using real-world data. |
