Supporting personalized ranking over categorical attributes

1. Supporting personalized ranking over categorical attributes Presenter : Lin, Shu-Han Authors : Gae-won You, Seung-won Hwang, Hwanjo Yu Information Sciences 178(2008)

2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments

3. Motivation • Categorical attributes’ problem of information retrieval's personal ranking • Categorical attributes do not have an inherent ordering. • How to rank the relevant data by categorical attribute. • For example, how can we… • Find old female with the preference of soda drink. 3

4. Objectives • Enableauniformrankedretrievaloveracombinationofcategoricalattributesandnumericalattributes. • Supportrankingofbinary representation of categorical attribute • Binary encoding • Sparsity Single-valued attribute Multi-valued attribute with bounded cardinality (item set, bc=2) 4

5. Overview （3） （2） （1） 5

6. Rank formulation 6 F= 0.5*age + 3*female+…

7. Rank processing (TA) A Simple example query: Find old female with the preference of soda drink. Transform into F= age + female • Candidate identification • Sorted Access age and female • Find top-k sa(age) and sa(female), e.g., k=1, sa(age)={o1}; sa(female)={o2} • Candidate reduction • O1=30+0 • O2=25+1 • O1 with the highest F score • Termination • O1 !> F(30,1)=31 // upper bound score • Another round of sorted access to consider more candidates, e.g., sa(age)={O4}; sa(female)={O3} 7

8. Bitmap – binary encoding F=v1+v2+v3+v4, k=2 • K={}, C={1111}（Initailization） • OID=excute(C) • OID={o4},|OID|>0,K={[o4,4]} • C={0111/1011/1101/1110} （Expansion） • K.count<k,Back to 2) • … 8

9. Bitmap– sparsity Single-valued attribute F=w1v1+w2v2+…+w6v6 rankedweightw1≧w2≧w3;w4≧w5≧w6forsimple,allw=1,k=2 • K={}, C={100.100.100} （Initailization） • OID=excute(C) • OID={o4},|OID|>0,K=OID={[o4,2]} • C={010.100.100/100.010.100/100.100.010} （Expansion） • K.count<k,Back to 2) • … 9

10. Bitmap– sparsity Multi-valued attributewithboundedcardinality 10

11. Experiments • UCI’ssparsityofindicatingvariable • 22%ofdatasetconsistonlythecategoricalattributes. • 56%ofcombinationofnumerical&categoricalattributes. 11

12. Experiments – syntheticdata 12

13. Experiments – real-lifedata 13

14. Conclusions • Thispaperstudies • Howtosupportrankformulation • Processingoverdatawithcategoricalattributes • Insteadofadoptingexistingnumericalalgorithms,developabitmap-basedapproachto • Binaryencoding • Sparsity • Single-valued • Multi-valuedwithboundedcardinality

15. Comments • Advantage • … • Drawback • … • Application • …