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Learning to Rank (part 1)

Learning to Rank (part 1). NESCAI 2008 Tutorial Yisong Yue Cornell University. Booming Search Industry. Goals for this Tutorial. Basics of information retrieval What machine learning contributes New challenges to address New insights on developing ML algorithms. (Soft) Prerequisites.

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1.6k views • 66 slides

Introduction to Information Retrieval

Introduction to Information Retrieval. Introduction to. Information Retrieval. Evaluation. Introduction to Information Retrieval. Binary relevance Precision@K (P@K) Mean Average Precision (MAP) Mean Reciprocal Rank (MRR) Multiple levels of relevance

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456 views • 20 slides

Evaluation

Evaluation. Rank-Based Measures. Binary relevance Precision@K (P@K) Mean Average Precision (MAP) Mean Reciprocal Rank (MRR) Multiple levels of relevance Normalized Discounted Cumulative Gain (NDCG). Precision@K. Set a rank threshold K Compute % relevant in top K

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421 views • 20 slides

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Kr compute precision k - PowerPoint PPT Presentation

Learning to Rank (part 1)

Introduction to Information Retrieval

Evaluation