Effective Phrase Effective Phrase PredictionPrediction

VLDB 2007

Arnab NandiDept. of EECSUniversity of Michigan, Ann Arborarnab@umich.edu

H. V. JagadishDept. of EECSUniversity of Michigan, Ann Arborjag@umich.edu

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