1 Context-Aware Clustering Junsong Yuan and Ying Wu EECS Dept., Northwestern University.
Effective Phrase Prediction VLDB 2007 Arnab Nandi Dept. of EECS University of Michigan, Ann Arbor...
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Effective Phrase Effective Phrase Prediction Prediction VLDB 2007 Arnab Nandi Dept. of EECS University of Michigan, Ann Arbor [email protected] H. V. Jagadish Dept. of EECS University of Michigan, Ann Arbor [email protected]
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Transcript of Effective Phrase Prediction VLDB 2007 Arnab Nandi Dept. of EECS University of Michigan, Ann Arbor...
Effective Phrase Effective Phrase PredictionPrediction
VLDB 2007
Arnab NandiDept. of EECSUniversity of Michigan, Ann [email protected]
H. V. JagadishDept. of EECSUniversity of Michigan, Ann [email protected]
Outline
INTRODUCTION MotivationMotivation Effective suggestions for autocompletion Simple FussyTree Construction algorithm&
Significance FussyTree EVALUATION METRICS& Total Profit
Metric(TPM) EXPERIMENTS
INTRODUCTION
Autocompletion is a widely deployed facility in systems that require user input.
MotivationMotivation
Ex: Hello.f
1. Hello.foo
2. Hello.freeze
3. Hello.frozen?
- Decrease the number of keystrokes typed by up to 20% for email
Effective suggestions for autocompletion
τ = 2 z = 2 y = 3
Effective suggestions for autocompletion
“please call” meets all three conditions of co-occurrence, comparability
“please call me” fails
to meet the uniqueness requirement, since “please call me asap”
has the same frequency.
τ = 2 z = 2 y = 3
Simple FussyTree Construction algorithm our tree using a
sliding window of 4
The first phrase to be added is
(please, call, me, asap)
(please, call, me),
(please, call)
Simple FussyTree Construction algorithm Occurs with a
Threshold frequency τ=2
Significance FussyTree
the branch point C is considered for flag promotion
EVALUATION METRICS& Total Profit Metric(TPM)
n: number of accepted completions
EVALUATION METRICS& Total Profit Metric(TPM) d : distraction parameter
TPM metric measures the effectiveness of our suggestion mechanism while the precision and recall metrics refer to the quality of the suggestions themselves
TPM(0): the fraction of keystrokes saved as a result of
the autocompletion TPM(1):is an extreme case where we consider
every suggestion(right or wrong) to be a blocking factor that costs us one keystroke
EXPERIMENTS
EXPERIMENTS
Training size 8 Prefix length
