Summary of Multislot Reranker: a Generic Model-based Re-ranking Framework in Recommendation Systems, by Qiang Charles Xiao et al.
MultiSlot ReRanker: A Generic Model-based Re-Ranking Framework in Recommendation Systems
by Qiang Charles Xiao, Ajith Muralidharan, Birjodh Tiwana, Johnson Jia, Fedor Borisyuk, Aman Gupta, Dawn Woodard
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 proposed MultiSlot ReRanker framework optimizes relevance, diversity, and freshness in large-scale production recommendation engines. The Sequential Greedy Algorithm (SGA) achieves a lift of 6-10% offline Area Under the receiver operating characteristic Curve (AUC), outperforming previous models by explicitly modeling mutual influences among items and leveraging second-pass ranking scores. The framework also generalizes offline replay theory to multi-slot re-ranking scenarios, offering trade-offs among multiple objectives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to recommend things on lists, like movies or products. It’s called the MultiSlot ReRanker, and it makes sure that what you see is relevant, diverse, and not too old. The method is really fast and works well even with very large lists. It also helps solve problems by considering how different things on a list relate to each other. |
Keywords
» Artificial intelligence » Auc
