A machine learning approach to improve precision for navigational

queries in a Web information retrieval system

Reiner Kraft

rekraft@cse.ucsc.edu

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