Effective Phrase Prediction

1. Effective Phrase Prediction VLDB 2007 Arnab Nandi Dept. of EECS University of Michigan, Ann Arbor arnab@umich.edu H. V. Jagadish Dept. of EECS University of Michigan, Ann Arbor jag@umich.edu

2. Outline • INTRODUCTION • Motivation • Effective suggestions for autocompletion • Simple FussyTree Construction algorithm& Significance FussyTree • EVALUATION METRICS& Total Profit Metric(TPM) • EXPERIMENTS

3. INTRODUCTION • Autocompletion is a widely deployed facility in systems that require user input.

4. Motivation • Ex: Hello.f • Hello.foo • Hello.freeze • Hello.frozen? - Decrease the number of keystrokes typed by up to 20% for email

5. Effective suggestions for autocompletion • τ = 2 z = 2 y = 3

6. 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

7. 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)

8. Simple FussyTree Construction algorithm • Occurs with a Threshold frequency τ=2

9. Significance FussyTree • the branch point C is considered for flag promotion

10. EVALUATION METRICS& Total Profit Metric(TPM) • n: number of accepted completions

11. 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(rightor wrong) to be a blocking factor that costs us one keystroke

12. EXPERIMENTS

13. EXPERIMENTS • Training size 8 • Prefix length