Learning to Rank for Information Retrieval¶
Liu, T. (2009). Learning to Rank for Information Retrieval. Foundations and Trends® in Information Retrieval, 3(3), 225-331.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Salience-as-Significance
- The selection process is the ranking function: under a hard capacity limit — ten blue links above the fold — it scores candidate documents by predicted click-through and dwell time and orders them.
This sourceSurveys learning-to-rank methods that construct a ranking function from click-derived signals (click-through, dwell time) to order candidate documents under a presented result slate.
Supported in partVerified against the publisher's abstract
Liu's abstract supplies only the ranking-function frame — a model built from training data that sorts documents by relevance — not click-through/dwell-time scoring or a ten-link capacity limit.
“Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking model using training data, such that the model can sort new objects according to their degrees of relevance, preference, or importance.”
- The selection process is the ranking function: under a hard capacity limit — ten blue links above the fold — it scores candidate documents by predicted click-through and dwell time and orders them.
Verification¶
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Registry ID ref:184f22251969 · see in the full table