1 / 11

Reiner Kraft rekraft@cse.ucsc

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

salma
Download Presentation

Reiner Kraft rekraft@cse.ucsc

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A machine learning approach to improve precision for navigational queries in a Web information retrieval system Reiner Kraft rekraft@cse.ucsc.edu

  2. 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

  3. 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

  4. 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

  5. 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

  6. The expert weight matrix Mq Example: MA1 predicts: 1 MA2 predicts: 2 MA3 predicts: 3 MRA predicts then: (d2,1),(d3,2),(d1,3)

  7. 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

  8. Average precision of one query over time

  9. Average Votes Distribution

  10. Average Precision compared to initial search engine ranking

  11. 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

More Related