Project PageZeroSmart Search

Antonio Gulli

Search transformed the way we look at the world

Search box:Looking at the world through a window

Satori: Entering the World of Entities

Words are ambiguous

Sites

Bing uses the world of entities as soon as you type. Not only for refining search results

Smart Search

Windows 8.1World Wide

SkyDrive Xbox Web

Personalized Results Email Skype

Local Files

Web

Learning to Personalize Query Auto-Completion

Milad ShokouhiMicrosoft

Relevance Labelling for Contextual Search

•For learning we need labels.•Relevance labelling for contextual (personalized) search

(auto-completion) is not trivial.•Previous work on personalized search [Fox et al., 2005]• Samples search impressions from the logs•Documents with SAT clicks are annotated with relevant labels.• The goal is to learn a re-ranking model that improves the

ranking of those relevant documents given the context.

Analogy: Auto-Completion Labels

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Experimental Settings

• Ranker: Lambda-Mart [Burges et al., 2011] • AOL testbed• 657K users (Mar-May 2006)• 128,620 queries in the prefix-tree• Userid, query, timestamp

• Bing testbed• 196K logged in users with Microsoft LiveID (Jan-2013)• 699,862 queries in the prefix-tree• Userid, query, timestamp, age, gender, zip code

• Training & testing on different sets of users

Personalized Ranking Features

• Demographics• Age (5 groups)• Gender (2 groups)• Zip-code (10 groups)

• Search history• Short (session)• Long (all past queries)

Personalization by Age

Testbed Baseline Personalized MRR (Gain/Loss)

Bing (age) - - +3.80%

The effectiveness of auto-completion personalization according to the user’s age in terms of MRR. All differences are statistically significant (P < 0.01)

Below 20 21-30 31-40 41-50 Above 50

Frequently promoted suggestions for different age groups

Results Summary

Features AOL BingShort history +1.95% 0.91%Long history +4.45% 5.57%Age - 3.80%Gender - +3.59%Location - +4.58%All +6.45% +9.42%

London

Twitter: @gulliantonio