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System Overview Document Retrieval Scoring and Ranking . Personalization and Re-ranking
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Deep Learning for Search and Recommender Systems in Practice
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- 1 System Overview Document Retrieval Scoring and Ranking . Personalization and Re-ranking
- 2 Document Retrieval • Simple regex based retrieval . Traditional inverted index based retrieval Embedding based retrieval
- 3 Metrics for Evaluation • Multiple level of relevance NDCG (Normalized Discounted Cumulative Gain) . Binary relevance DMAP (Mean Average Precision) MRR Meon Reciprocal Ronk
- 4 Normalized Discounted Cumulative Gain Discounted Cumulative Goin
- 5 Mean Average Precision Precision: Relevant documents up to rank K/K
- 6 Mean Reciprocal Rank Reciprocal Rank
- 7 Learning to Rank
- 8 Pointwise Ranking Loss function is based on a single (query, document) pair
- 9 Regression based pointwise ranking Input (4.x) feature vector responding to the query and a document, Label: y relevance of the document
- 10 Classification based pointwise ranking
- 11 Ordinal regression based pointwise ranking
- 12 Summary of pointwise ranking Pros • Simple, considering one document at a time. • Available algorithms are rich. Most regression/classification algorithms can be used.
- 13 Pairwise Ranking Loss function is based on query and a pair of documents.
- 14 Listwise Ranking Loss function is based on the query and a list of documents
- 15 AdaRank Motivation: commonly used evaluation metrics are not differentiable. So it is not easy to optimize directly. AdaRank minimizes the exponential loss. El below can be NDCG.
- 16 List Net / ListMLE Map list of scores to a probability distribution by Plockett-Luce model. • Permutation probability, where 5() is the scoring function.
- 17 Summary of listwise ranking Pros
- 18 DeText: a Deep Learning Ranking Framework
