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INFORMATION RETRIEVAL TECHNIQUES BY DR . ADNAN ABID

INFORMATION RETRIEVAL TECHNIQUES BY DR . ADNAN ABID. Lecture # 27 Mean Average Precision Non Binary Relevance DCG NDCG. ACKNOWLEDGEMENTS. The presentation of this lecture has been taken from the underline sources

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INFORMATION RETRIEVAL TECHNIQUES BY DR . ADNAN ABID

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  1. INFORMATION RETRIEVAL TECHNIQUESBYDR. ADNAN ABID Lecture # 27 Mean Average Precision Non Binary Relevance DCG NDCG

  2. ACKNOWLEDGEMENTS The presentation of this lecture has been taken from the underline sources • “Introduction to information retrieval” by PrabhakarRaghavan, Christopher D. Manning, and Hinrich Schütze • “Managing gigabytes” by Ian H. Witten, ‎Alistair Moffat, ‎Timothy C. Bell • “Modern information retrieval” by Baeza-Yates Ricardo, ‎ • “Web Information Retrieval” by Stefano Ceri, ‎Alessandro Bozzon, ‎Marco Brambilla

  3. Outline • Mean Average Precision • Mean Reciprocal Rank • Cumulative Gain • Discounted Cumulative Gain • Normalized Discounted Cumulative Gain

  4. Mean Average Precision(MAP) • Average Precision: Average of the precision values at the points at which each relevant document is retrieved. • Ex1: (1 + 1 + 0.75 + 0.667 + 0.38 + 0)/6 = 0.633 • Ex2: (1 + 0.667 + 0.6 + 0.5 + 0.556 + 0.429)/6 = 0.625 • Mean Average Precision: Average of the average precision value for a set of queries.

  5. Mean average precision • If a relevant document never gets retrieved, we assume the precision corresponding to that relevant doc to be zero • MAP is macro-averaging: each query counts equally • Now perhaps most commonly used measure in research papers • Good for web search? • MAP assumes user is interested in finding many relevant documents for each query • MAP requires many relevance judgments in text collection

  6. Mean Reciprocal Rank • Consider rank position, K, of first relevant doc • Could be – only clicked doc • Reciprocal Rank score = • MRR is the mean RR across multiple queries

  7. Non-Binary Relevance • Documents are rarely entirely relevant or non-relevant to a query • Many sources of graded relevance judgments • Relevance judgments on a 5-point scale • Multiple judges • Click distribution and deviation from expected levels (but click-through != relevance judgments)

  8. Cumulative Gain • Withgraded relevance judgments, we can compute the gain at each rank. • Cumulative Gain at rank n: (Where reli is the graded relevance of the document at position i)

  9. Discounted Cumulative Gain • Uses graded relevance as a measure of usefulness, or gain, from examining a document • Gain is accumulated starting at the top of the ranking and may be reduced, or discounted, at lower ranks • Typical discount is 1/log (rank) • With base 2, the discount at rank 4 is 1/2, and at rank 8 it is 1/3

  10. Discounting Based on Position • Users care more about high-ranked documents, so we discount results by 1/log2(rank) • Discounted Cumulative Gain:

  11. Normalized Discounted Cumulative Gain (NDCG) • To compare DCGs, normalize values so that a ideal ranking would have a Normalized DCGof 1.0 • Ideal ranking:

  12. Normalized Discounted Cumulative Gain (NDCG) • Normalize by DCG of the ideal ranking: • NDCG ≤ 1 at all ranks • NDCG is comparable across different queries

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