1. A DDING S TRUCTURE TO T OP -K: F ORM I TEMS TO E XPANSIONS Date : 2012.5.21 Source : CIKM’ 11 Speaker : I-Chih Chiu Advisor : Dr. Jia-Ling Koh 1

2. I NDEX Introduction Problem Definition Basic Algorithm Semantic Optimization Experiments Conclusion 2

3. I NTRODUCTION Keyword based search interfaces are extremely popular. 3

4. I NTRODUCTION Google search Query → What’s the weather today? Results include ‘what’, ’weather’, ’today’. Lack of semantic. Del.icio.us Search results → Using a faceted interface. Expansions → A fixed set of tags. 4

5. I NTRODUCTION Motivated by these drawbacks of current search result interfaces, considering a search scenario in which each item is annotated with a set of keywords. Don’t need to assume the existence of pre-defined categorical hierarchy Want to automatically group query result items into different expansions of the query corresponding to subsets of keywords. 5

6. I NDEX Introduction Problem Definition Basic Algorithm Semantic Optimization Experiments Conclusion 6

7. P ROBLEM D EFINITION 7 t i.a j : normalized to [0,1] Author(0.3)Click(0.6) t10.60.8 t20.70.2 t30.40.3 t40.90.4 u(t i ) 0.6*0.3+0.6*0.8=0.64 0.3*0.7+0.6*0.2=0.33 0.3*0.4+0.6*0.3=0.30 0.3*0.9+0.6*0.4=0.51

8. P ROBLEM D EFINITION Group items into different expansions of Q and return high quality expansions. A subset of keywords e ⊆ K − Q. (K : all keywords) Subset-of relationship for K-Q={k 1,k 2,k 3,k 4 } 8

9. D ETERMINING I MPORTANCE OF A N E XPANSION 9 S k1 S k1,k2 S k2,k3 t 1 (k 1 )0.4XX t 2 (k 1,k 2 )0.60.5X t 3 (k 3 )XX0.6 g(S e )1.00.50.6

10. I NDEX Introduction Problem Definition Basic Algorithm Semantic Optimization Experiments Conclusion 10

11. N AÏVE A LGORITHM TopExp-Naïve algorithm 11 Access items in the non- increasing order of their attribute value For each matching item accessed, enumerate all possible expansions and update their lower bound and upper bound utility value; Round-robin

12. I MPROVED A LGORITHM 12 LKLK L

13. I MPROVED A LGORITHM 13

14. I MPROVED A LGORITHM TopExp-Lazy algorithm 14 Access items in the non- increasing order of their attribute value

15. I MPROVED A LGORITHM To count how many expansions correspond to the same set of items. Use the classical inclusion-exclusion principle. 2 |e| − count − 1 count += 2 |e’| -1 E.g. e = {k 1,k 2,k 3 } → 8 (2 |e| ) e’ = {k 1,k 2 },{k 3 } → 4 (count) 8 – 4 – 1 = 3  ({k 1, k 2, k 3 }, {k 1, k 3 } and {k 2, k 3 }). 15

16. I NDEX Introduction Problem Definition Basic Algorithm Semantic Optimization Experiments Conclusion 16

17. W EIGHTING E XPANSIONS 17

18. P ATH E XCLUSION BASED A LGORITHM 18

19. P ATH E XCLUSION BASED A LGORITHM 19 Assume weights are equal 1. H1H1 H2H2 G

20. P ATH E XCLUSION BASED A LGORITHM Top-PEkExp algorithm 20 Generate necessary expansions using TopExp-Lazy R G ←GreedyMWIS( L ); Etopk ←k expansions in L which have the largest upper bound utilities;

21. I NDEX Introduction Problem Definition Basic Algorithm Semantic Optimization Experiments Conclusion 21

22. E XPERIMENTS Synthetic datasets Generated 5 synthetic datasets with size from 8000 to 12000. Efficiency Scalability Memory saving Real datasets The ACM Digital Library. Demonstrate the quality of the expansions returned. 22

23. E XPERIMENTS Fixed N=10 and k=10 23

24. E XPERIMENTS Fixed number of items=10000, N = 10 24

25. E XPERIMENTS Fixed number of items=10000, k = 10 25

26. E XPERIMENTS Queries : “xml” “histogram” “privacy” Attributes : The average author publication number The citation count. Keywords : The title Keywords list Abstract 26

27. 27

28. C ONCLUSION They studied the problem of how to better present search/query results to users. Proposed various efficient algorithms which can calculate top-k expansions. Not only demonstrated the performance of the proposed algorithms, also validated the quality of the expansions returned by doing a study on a real data set. 28