Project page zero, Smart Search, Learning to Personalize suggestions
•Download as PPTX, PDF•
2 likes•1,671 views
Project PageZero aims to transform search by looking at the world through entities rather than words. Search has become personalized through features like short and long search history, age, gender, location, and more. Experimental results found personalization improved auto-completion effectiveness, with the most gains from combining all features: 6.45% on AOL and 9.42% on Bing testbeds.
More Related Content
Similar to Project page zero, Smart Search, Learning to Personalize suggestions
Similar to Project page zero, Smart Search, Learning to Personalize suggestions (20)
Recently uploaded
Recently uploaded (20)
Project page zero, Smart Search, Learning to Personalize suggestions
- 2. Search transformed the way we look at the world
- 4. Search box: Looking at the world through a window
- 9. Satori: Entering the World of Entities
- 14. Sites
- 17. Bing uses the world of entities as soon as you type. Not only for refining search results
- 18. Smart Search Windows 8.1 World Wide
- 32. Learning to Personalize Query AutoCompletion Milad Shokouhi Microsoft
- 33. 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.
- 35. 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
- 36. Personalized Ranking Features • Demographics • Age (5 groups) • Gender (2 groups) • Zip-code (10 groups) • Search history • Short (session) • Long (all past queries)
- 37. Personalization by Age The effectiveness of auto-completion personalization according to the user’s age in terms of MRR. All differences are statistically significant (P < 0.01) Testbed Baseline Personalized MRR (Gain/Loss) Bing (age) - - +3.80% Frequently promoted suggestions for different age groups Below 20 21-30 31-40 41-50 Above 50
- 38. Results Summary Features Short history Long history Age Gender Location All AOL +1.95% +4.45% +6.45% Bing 0.91% 5.57% 3.80% +3.59% +4.58% +9.42%