1. Learning to Personalize Query Auto-Completion
Milad Shokouhi
PAPER
PRESENTATION
Sandesh Sanjay Gade

2. Query auto-completion (QAC)
QAC is one of the most prominent features of modern search engines.
List of query candidates generated according to the prefix entered by the user in the search box and is updated on each new stroke.
Two step process:
Filtering
Facilitated by using data structures such as prefix-trees (tries) that allow efficient lookups by prefix matching.
Ranking
Results of the filtering process that match the prefix are then ordered according to their expected likelihood.

3. Traditional Pipeline filtered relevant list ranked relevant list
prefix
RANK
FILTER
PARAMETERS
PARAMETERS

4. i filtered relevant ranked list Ikea RANK Imdb prefix FILTER Instagram
Ipad
Ipod …
Imdb
Ikea
Instagram
ipad i

6. Query Auto-completion
Query Suggestion
Query Auto-completion
Personalized Search

7. Personalized Search Query Suggestion Query Auto-completion
Maximum Likelihood Estimation

8. Personalized Auto-Completion
Learning to personalize auto-completion ranking of query suggestions for prefixes, is analogous to learning to personalize search results.
Aim: Using a training dataset that consists of a set of labeled query-document pairs, the goal is to learn a ranking model by optimizing a cost function that is expected to be correlated with user satisfaction.

9. Experiment Setup Winner of the Yahoo! 2010 Learning to Rank Challenge.
TRAINING Lambda-MART Rank algorithm [Burge at al., 2006] as the ranking algorithm.
Winner of the Yahoo! 2010 Learning to Rank Challenge.
BASELINE MostPopularCompletion(MPC) method [Bar-Yossef and Kraus, 2011] used as a baseline.
EVALUATION METRIC Mean-Reciprocal-Rank.
The top-10 candidates returned by the MPC model as input to the ranker.
Work’s personalized auto-completion ranker is built on top of the MPC algorithm.

10. Datasets AOL testbed Bing testbed March 1, 2006 – May 31, 2006
QUERY SAMPLE
DURATION
March 1, 2006 – May 31, 2006
January 1, 2013 – January 9, 2013
UNIQUE QUERIES
128,620
699,862
UNIQUE USERS
657,426
196,190

11. List of features used

12. d Short History Features filtered relevant list ranked relevant list
prefix
dictionary
Driving directions
Deal or no deal
dell
RANK
dell
dictionary
Driving directions
Deal or no deal
FILTER d
The personalization model has picked up on the high lexical similarity between dell (candidate) and dell computer (2nd last query in the session) for re-ranking.

13. n Long History Features filtered Relevant list ranked Relevant list
nascar
netflix
nicks.com
nascar.com
nextel
northwest airlines
netflix
nascar
northwest airlines
nicks.com
nascar.com
nextel
prefix
FILTER
RANK n
Compared to the previous experiment based on short-term features, the MRR gains are higher.
Personalization model realizes that Netflix has appeared twice in user’s search history before and boosts it to position one.

14. Age*
Gender
Region
* Biggest mover in personalized auto-completion rankings

15. Evaluation Summary Long history feature (Bing and AOL)
BEST PERFORMING FEATURE
Long history feature (Bing and AOL)
SECOND BEST
Location features
BEST COMBINATION (BING TESTBED)
BEST COMBINATION (AOL TESTBED)
All user-specific and demographic features
Short and long-history features

16. Conclusions