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 h q –Rank r(q,h q ) is within top k ranked search results, where k<20 –More ambitious: Let r(q,h q ) =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 M q DocMapping E1E1 E2E2 E3E3 d2d d3d d1d MA 1 predicts: 1 MA 2 predicts: 2 MA 3 predicts: 3 MRA predicts then: (d 2,1),(d 3,2),(d 1,3) 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