Context-Sensitive Query Auto-Completion AUTHORS:NAAMA KRAUS AND ZIV BAR-YOSSEF DATE OF...
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Transcript of Context-Sensitive Query Auto-Completion AUTHORS:NAAMA KRAUS AND ZIV BAR-YOSSEF DATE OF...
![Page 1: Context-Sensitive Query Auto-Completion AUTHORS:NAAMA KRAUS AND ZIV BAR-YOSSEF DATE OF PUBLICATION:NOVEMBER 2010 SPEAKER:RISHU GUPTA 1.](https://reader036.fdocuments.us/reader036/viewer/2022062417/5516b1a3550346f0208b52c8/html5/thumbnails/1.jpg)
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Context-Sensitive QueryAuto-CompletionAUTHORS:NAAMA KRAUS AND ZIV BAR-YOSSEF
DATE OF PUBLICATION:NOVEMBER 2010
SPEAKER:RISHU GUPTA
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digital camera reviewsdigital camera buying guidedigital camera with wifidigital camera dealsdigital camera worlddigital picture framedigital copy
Motivating Example
I want to buy a good Digital
Camera
Current Result Desired Result
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Most Challenging Auto-Completion Scenario
Challenge :Query Auto-Completion predicts the correct user’s query with only 12.8%
probability.
Goal :To predict the user’s intended query reliably when user has entered only
one character.
Advantages:◦ Makes search experience faster◦ Reduces load on servers in Instant Search
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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
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
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Context-Sensitive Auto-Completion
How to Compensate for the lack of information ??
Observation:
• User searches within some context.• User context reflects user’s 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
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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 ???
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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 ??
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EvaluationEVALUATION 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 to
“prediction 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
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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.
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Most Popular VS Nearest Completion
Relevant Context:MRR of NearestCompletion (with depth-3 traversal) is higher in 48% than that of MostPopular-Completion.
NearestCompletion becomesdestructive, so its MRR is 19% lower than that of MostPopularCompletion.
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How Hybrid Completion Works??
Produce Lists
• Produce top k completions of NearestCompletion• Produce top k completions of MostPopularCompletion
Standardi
ze• Two lists differ in units and scale
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
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MostPopular, Nearest, and Hybrid (2)
HybridCompletion is shown to be at least as good as NearestCompletion when the context is relevant and almost as good as MostPopularCompletion when thecontext is irrelevant.
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Examples
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Conclusion 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
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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.