Learning to Personalize Query Auto-Completion

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Presentation transcript:

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

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.

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

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

Query Auto-completion Query Suggestion Query Auto-completion Personalized Search

Personalized Search Query Suggestion Query Auto-completion Maximum Likelihood Estimation

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.

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.

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

List of features used

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.

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.

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

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

Conclusions