Learning-to-Rank: Features, Models, and Position Bias
~45 min read
The three learning-to-rank paradigms, feature engineering for search, why interaction features dominate, and how to correct position bias when training on click log data.
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Key points
- •LambdaRank (pairwise LTR with NDCG-weighted gradients) is the standard production choice: more principled than pointwise, more tractable than listwise
- •Query-document interaction features (BM25 score, semantic similarity, historical CTR for this query-document pair) consistently dominate document quality features — always build these first
- •Position bias is systematic: clicks at position 1 are 5-10× more frequent than position 5 for the same document. IPS correction re-weights training examples to de-bias the model