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Evidence from Behavior
INST 734 Doug Oard Module 7 1
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Agenda Explicit feedback Implicit Feedback Link analysis Clickstreams
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Recommender Systems Source: Jon Herlocker, SIGIR 1999
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Relevance Feedback x x x x o x x x x x x x o x o x x o x o o x x x x
Initial query x x x x o x x x x x x x o x o x x o x o o x x x x Revised query qm = modified query vector q0 = original query vector α,β,γ: weights (hand-chosen or set empirically) Dr = set of known relevant doc vectors Dnr = set of known irrelevant doc vectors x non-relevant documents o relevant documents
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Rocchio Example (+) (-) 1 1 1 1 2 16 8 8 2 1 8 4 4 1 8 16 4 16 12 2 4
Typically, < Computer Apollo Program Theater NASA Navy Original query 1 1 1 1 (+) Positive Feedback 2 16 8 8 2 1 8 4 4 1 (-) Negative feedback 8 16 4 16 12 2 4 1 4 3 New query -1 3 3 1 -2 16
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Relevance Feedback Assumptions
The initial query was reasonable The positive examples are representative The user will give feedback
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A1: Good Initial Query? Requires finding something on the first try!
Problems: Knowledge gaps (“Anomalous state of knowledge”) Vocabulary gaps (active vs. passive vocabulary) Misspelling (e.g., Brittany Speers)
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A2: Representative Examples?
Some relevant documents may be dissimilar Examples: Medical documents written for doctors or patients Policy documents from different organizations Documents written in different dialects
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A3: Will People Use It? Efficiency Understandability Risk
Longer queries require more processing time Understandability Harder to see why subsequent documents retrieved Risk Users are reluctant to provide negative feedback
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“Blind” Relevance Feedback
Goal: include terms user could have chosen By choosing terms related to those they did choose Three-stage process Perform an initial search Select new terms from the top results e.g., highest “offer weight” terms in ≥ 2 top results Expand (and reweight) the query Improves MAP for about 2/3 of the queries But increases the variance in AP
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Agenda Explicit feedback Implicit Feedback Link analysis Clickstreams
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