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A machine learning approach to improve precision for navigational
queries in a Web information retrieval system
Reiner Kraft
Motivation
• Ranking of search results:– Require high precision vs. recall– Navigational queries (homepage finding task)
should have desired result on top– Users are impatient and don’t examine low
ranked results– Want to incorporate users relevance judgment
to improve overall ranking
Project Goal
• Use on-line learning algorithm, that given query q, find homepage hq– Rank r(q,hq) is within top k ranked search
results, where k<20
– More ambitious: Let r(q,hq) =1
– Improve precision of top k search results
• Algorithm design has to be space and time efficient to be of practical use
Overall setup
• On-line learning algorithm based on weighted majority algorithm
• Predict with weighted median for query q• User is teacher and provides reinforcements:
– Negative Vote: document ranked too high (-)– Positive Vote: document ranked too low (+)
• Algorithm incorporate feedback and update ranking for q
LearnRank 1• Use good quality ranking of search engine for query q as
initialization of expert’s weights• Uses matrix of experts per query q• Each expert predicts fixed rank (linear distribution)• Rows of experts are managed by k master algoritms (MA)
and combine predictions• MA predict with weighted median• Master rank algorithm (MRA) then combines predictions of
MA’s by sorting• Need to resolve ties using heuristics based on votes• MA’s are using fixed multiplicative update to punish poorly
performing experts
The expert weight matrix Mq
DocMapping E1 E2 E3
d20.5001 0.166 0.166 0.166
d30.2505 0.2505 0.249 0.249
d10.1667 0.1667 0.1667 0.4999
MA1 predicts: 1
MA2 predicts: 2
MA3 predicts: 3
MRA predicts then: (d2,1),(d3,2),(d1,3)
Example:
LearnRank 2
• Uses absolute loss based on distance to voted rank
• Uses shared update– Takes some of the weight of misleading
experts and distributes it among the other experts
– Better adaptability
Average precision of one query over time
Average Votes Distribution
Average Precision compared to initial search engine ranking
Conclusion
• LearnRank 1 and LearnRank 2 outperform initial search engine ranking in terms of average precision over time
• LearnRank 2 performs better because of shared update (more adaptive)
• Algorithms are time and space efficient and can be easily implement in search engines