Summary of Effective and Secure Federated Online Learning to Rank, by Shuyi Wang
Effective and secure federated online learning to rank
by Shuyi Wang
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); 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 Online Learning to Rank (OLTR) method optimizes ranking models using implicit user feedback, such as clicks. Unlike traditional Learning to Rank (LTR) methods that rely on a static set of training data with relevance judgments, OLTR updates the model continually as new data arrives. This addresses drawbacks like high annotation costs, potential misalignment between user preferences and human judgments, and rapid changes in query intents. However, OLTR methods require searchable data, user queries, and clicks, posing privacy concerns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Online Learning to Rank is a way to improve search results by using clues from how people interact with them, like which ones they click on. This approach is different because it learns as new information comes in, instead of relying on what experts have decided is important. This helps fix problems like needing too many expensive labels and worrying about what users really want versus what humans think they should want. |
Keywords
» Artificial intelligence » Online learning
