Context-Sensitive Query Auto-Completion AUTHORS:NAAMA KRAUS AND ZIV BAR-YOSSEF DATE OF PUBLICATION:NOVEMBER 2010 SPEAKER:RISHU GUPTA 1

digital camera reviews digital camera buying guide digital camera with wifi digital camera deals digital camera world digital picture frame digital copy Motivating Example I want to buy a good Digital Camera Current Result Desired Result 2

Most Challenging Auto-Completion Scenario Challenge :Query Auto-Completion predicts the correct users query with only 12.8% probability. Goal :To predict the users intended query reliably when user has entered only one character. Advantages: Makes search experience faster Reduces load on servers in Instant Search 3

QAC Algorithms User enters the prefix x of Query q Returns a List of K Completions Hit occurs if c=q Need efficient data structure for faster lookup 4 Completion c of Top K Completion List QAC Algorithm should also work if c is semantically equal to q Ordered By Quality Score Hash Table or Trie

Context-Sensitive Auto- Completion How to Compensate for the lack of information ?? Observation: User searches within some context. User context reflects users intent. Context examples Recent queries Recently visited pages Recent Tweets etc….. Our focus – Recent queries Accessible by search engines 49% of searches are preceded by a different query in the same session For simplicity, in this presentation we focus on the most recent query 5

Recent Query Use Approaches Cluster Similar Queries (Use of Techniques like HMMs) Nearest Completion Algorithm (Assumption:Context relevant to the query) Generalize Most Popular Completion Algorithm None of these previous studies took the user input (prefix) into account in the prediction In 37% of the query pairs the former query has not occurred in the log before Problem with this approach ?? How to tackle this problem ??? 6

Nearest Completion:Measure of Similarity Challenge: Choosing similarity measure that is correlated and universally applicable Completions must be semantically related to the context query. Recommendation Based Query Expansion Represent queries and contexts as high- dimensional term-weighted vectors and resort to cosine similarity. Idea :rich representation of a query is constructed not from its search results, but rather from its recommendation tree. Recommendation Based Query Outputs list of recommendations which are reformulations of previous query. Problem occurs when none of the recommendation compatible with user query How to Overcome this challenge ?? 7

Evaluation EVALUATION METRIC MRR-Mean Reciprocal Rank A standard IR measure to evaluate a retrieval of a specific object at a high rank wMRR-Weighted MRR Weight sample pairs according toprediction difficulty (total # of candidate completions) EVALUATION FRAMEWORK Evaluation Set A random sample of (context, query) pairs from the AOL log Prediction Task Given context query and first character of intended query predict intended query at as high rank as possible 8

Analysis NearestCompletion Fails when the context is irrelevant (difficult to predict whether the context is relevant) MostPopularCompletion Fails when the intended query is not highly popular (long tail) Solution: HybridCompletion HybridCompletion: a combination of Most popular Completion and Nearest Completions Its MRR is 31.5% higher than that of MostPopularCompletion. 9

Most Popular VS Nearest Completion 10 Relevant Context:MRR of NearestCompletion (with depth-3 traversal) is higher in 48% than that of MostPopular-Completion. NearestCompletion becomes destructive, so its MRR is 19% lower than that of MostPopularCompletion.

How Hybrid Completion Works?? Produce Lists Produce top k completions of NearestCompletion Produce top k completions of MostPopularCompletion Standardize Hybrid Score is Convex Combination hybscore(q) = α · Zsimscore(q) + (1 α) · Zpopscore(q) 0 α 1 is a tunable parameter Prior probability that context is relevant 11

MostPopular, Nearest, and Hybrid (2) HybridCompletion is shown to be at least as good as NearestCompletio n when the context is relevant and almost as good as MostPopularCom pletion when the context is irrelevant.

Examples 13

Conclusion 14 Query Auto Completion HybridCompletion Algorithm Nearest Completion Algorithm MostPopularCompletion Algorithm Context Sensitive-Query Auto Completion Based on Popular Queries(AOL Query Log) Convex Combination of NearestCompletion and MostPopular Relevent Context:Based on Users Recent Queries Recommendation Based Algorithm: Rich Query Representatin

Future NearestCompletition: More effective session segmentation technique Predicting the first query in a session still remains an open problem Use of Other Context Resources like Recently Visited Web Pages or Search History Measure of Quality Evaluation should be more relaxed Rich query representation may be further fine tuned. 15