1. Rank Aggregation

2. Rank Aggregation: Settings Multiple items – Web-pages, cars, apartments,…. Multiple scores for each item – By different reviewers, users, according to different features… Some aggregation function on the scores – Sum, Average, Max… Goal: compute the top-k items

3. Rank Aggregation Example ModelPriceRank Honda9 Volvo3 Subaru 9 ModelComfortRank Honda7 Volvo10 Subaru5 ModelBeautyRank Honda3 Volvo8 Subaru4 ModelTotalRank(min) Honda3 Volvo3 Subaru4 ModelTotalRank(avg) Honda6.333 Volvo7 Subaru6

4. Naïve Algorithm Compute the aggregated rank for all items Find the best one, then the second best one… the k best one Good for small-scale problems Still not feasible for web scales…

5. Can we do any better? An assumption to help us: each individual list comes sorted – Reasonable for search engines, user rankings… Another assumption: monotonicity of the aggregation function Now can we do any better?

6. Fagin's algorithm (FA) Do sorted access on all lists in parallel For every item do random access to the other lists to fetch all of its values Stop when at least k items were seen (in the sorted access) in all lists Sort the list Why is this enough?

7. Example ItemScore A9 B9 C3 D1 ItemScore B10 C5 A4 D3 BeautyComfort ItemScore A6.5 Average

8. Example ItemScore A9 B9 C3 D1 ItemScore B10 C5 A4 D3 BeautyComfort ItemScore A6.5 B9.5 Average

9. Example ItemScore A9 B9 C3 D1 ItemScore B10 C5 A4 D3 BeautyComfort ItemScore A6.5 B9.5 Average

10. Example ItemScore A9 B9 C3 D1 ItemScore B10 C5 A4 D3 BeautyComfort ItemScore A6.5 B9.5 C4 Average

11. Example ItemScore A9 B9 C3 D1 ItemScore B10 C5 A4 D3 BeautyComfort ItemScore A6.5 B9.5 C4 Average

12. Example (top-3) ItemScore A9 B9 C3 D1 ItemScore B10 C5 A4 D3 BeautyComfort ItemScore A6.5 B9.5 C4 Average How do we know not to look further?

13. Complexity Probabilistic analysis on the order of items can be used to show better bounds (with good probability) Can we do even better?

14. Cost model This is a very simple settings so we can define a finer cost model than worst case complexity In a web context it is important to do so – Since the scale is huge We associate some cost C s with every sorted access, and some cost C r with every random access Denote the cost for algorithm A on input instance I by cost(A,I)

15. Instance-optimality An algorithm A is instance-optimal if for every input instance I, cost(A,I) = O(cost(A',I)) for every algorithm A' A very strong notion But we can realize it here!

16. Threshold Algorithm (TA) Idea: sometimes we can stop before seeing k objects in every list Use a threshold on how good can a score of an unseen object be. Based on aggregating the minimal score seen so far in all lists

17. Example ItemScore A9 B9 C3 D1 ItemScore B10 C5 A4 D3 BeautyComfort ItemScore A6.5 Average

18. Example ItemScore A9 B9 C3 D1 ItemScore B10 C5 A4 D3 BeautyComfort ItemScore A6.5 Average T=9.5

19. Example ItemScore A9 B9 C3 D1 ItemScore B10 C5 A4 D3 BeautyComfort ItemScore A6.5 B9.5 Average T=9.5

20. Example ItemScore A9 B9 C3 D1 ItemScore B10 C5 A4 D3 BeautyComfort ItemScore A6.5 B9.5 C4 Average T=7

21. Example ItemScore A9 B9 C3 D1 ItemScore B10 C5 A4 D3 BeautyComfort ItemScore A6.5 B9.5 C4 Average T=4 One step less!

22. Instance-optimality Theorem: If the aggregation function is strictly monotone and every two items in a list have distinct grades, then TA is instance-optimal – Intuition: If an algorithm stops on input I before reaching the threshold, then we can design an input I' on which it is wrong, by changing values it did not see – TA sees at most K items more than any algorithm on any input Strict monotonicity is needed to avoid "lucky guesses" in breaking ties – Thm. In general no instance-optimal algorithm exists Theorem: TA is instance-optimal against all algorithms that do not "guess" – i.e. do not do random access to an item they did not see in sorted access

23. Restricted Sorted Access Some rankings are not available as sorted – E.g. distances from a map site Then we can revise TA to do sorted access only on the list where it is possible And still instance-optimal! (Against algorithms that work under the same restrictions, of course)

24. No Random Access Maintain bottom and upper bounds for every item (worst and best grades) Best is the aggregation of what we have seen and the worst we have seen in every list, Worst is the aggregation with what we have seen and zeros Keep in the list those with top-K "worst" grades – Break ties by "best" grades Halt if we have k items in the list, and the best grade for every item out of the list is less than the k'th in the list

25. Example ItemScore A9 B9 C3 D1 ItemScore B10 C5 A4 D3 BeautyComfort ItemScore A4.5<S<9 Average

26. Example ItemScore A9 B9 C3 D1 ItemScore B10 C5 A4 D3 BeautyComfortAverage ItemScore A4.5<S<9 B5<S<10

27. Example ItemScore A9 B9 C3 D1 ItemScore B10 C5 A4 D3 BeautyComfortAverage ItemScore A4.5<S<9 B9.5

28. Example ItemScore A9 B9 C3 D1 ItemScore B10 C5 A4 D3 BeautyComfortAverage ItemScore A4.5<S<9 B9.5 C2.5<S<5

29. Example ItemScore A9 B9 C3 D1 ItemScore B10 C5 A4 D3 BeautyComfort ItemScore A6.5 B9.5 C4 Average ItemScore A4.5<S<9 B9.5 C4

30. Example ItemScore A9 B9 C3 D1 ItemScore B10 C5 A4 D3 BeautyComfort ItemScore A6.5 B9.5 C4 Average ItemScore A6.5 B9.5 C4

31. Example ItemScore A9 B9 C3 D1 ItemScore B10 C5 A4 D3 BeautyComfort ItemScore A6.5 B9.5 C4 Average ItemScore A6.5 B9.5 C4 Score(D)<3