1. Group Recommendation: Semantics and Efficiency
Sihem Amer-Yahia (Yahoo! Labs), Senjuti Basu-Roy (Univ. of Arlington), Ashish Chawla (Yahoo! Inc), Gautam Das (Univ. of Arlington), and Cong Yu (Yahoo! Labs).

2. Recommendation
Individual User Recommendation : “Which movie should I watch?” “What city should I visit?” “What book should I read?” “What web page has the information I need?” 2

3. Group Recommendation 3 Individual User Recommendation
User seeking intelligent ways to search through the enormous volume of information available to her.
How to Recommend?
Movies – for a family!
Restaurants – for a work group lunch!
Change the appearance
Places to visit – using a travel agency!
Solution : Group Recommendation
Helps socially acquainted individuals find content of interest to all of them together. 3

4. Existing Solution
No prior work on efficient processing for Group Recommendation
Existing solutions aggregate ratings (referred to as relevance) among group members
Preference Aggregation: aggregates group members’ prior ratings into a single virtual user then computes recommendations for that user
Rating Aggregation: aggregate individual ratings on the fly using
Average
Least Misery: computes min rating 4

5. Why Is Rating Aggregation Not Enough?
Task: recommend a movie to group G ={u1, u2 ,u3}
relevance (u1,”God Father”) = 5
relevance (u2, “God Father”) = 1
relevance (u3, ”God Father”) = 1
relevance (u1, ”Roman Holiday”) = 3
relevance (u2, “Roman Holiday”) = 3
relevance (u3, ”Roman Holiday”) = 1
Average Relevance and Least Misery fail to distinguish between “God Father” and “Roman Holiday”
But, group members agree more on “Roman Holiday” with higher ratings
Difference in opinion (Disagreement) between members may be important in Group Recommendation Semantics. 5

6. Outline Motivation Modeling & Problem Definition Algorithms
Optimization Opportunities
Experimental Evaluation
Conclusion & Future Work 6

7. Semantics in Group Recommendation
Relevance (average or least misery) and Disagreement in a recommendation’s score computed using a consensus function
Group G ={u1, u2 ,u3} relevance(u1,”God Father”) = 5, relevance(u2, “God Father”) = 1, relevance=(u3,” God Father”) = 1
Average Pair-wise Variance
= (|(5-1)|+|(1-1)|+|(1-5)|)/ = [( )2 + (1-2.33)2
+(1-2.33)2] / 3
score (G, “God Father”) = w1 x w2 x (1-.2) (after normalization) 7

8. Problem Definition Top-k Group Recommendation:
Given a user group G and a consensus function F, return a list of k items to the group such that each item is new to all users in G and the returned k- items are sorted in decreasing value of F 8

9. Efficient Recommendation Computation
Average and Least Misery are monotone
Sort user relevance list in decreasing value
Apply Threshold Algorithm TA
i1,2
i3,2
i2,2
i1,0
i4,2
i2,0
i4,0
ILu ILu2
For a user group G = {u1, u2}, 4 Sorted Accesses are required to compute top-1 item for Group Recommendation.
Pruning operates only on the relevance component of consensus function.
How to do pruning on Disagreement component also? 9

10. Role of Disagreement Lists
Pair-wise disagreement lists are computed from individual relevance lists
Disagreement lists are sorted in increasing values
Items encountered in disagreement lists play a significant role in attaining early stoppage
ILu ILu DL(u1,u2)
i1,2
i3,2
i2,2
i1,0
i4,2
i3,0
i2,0
i4,0
Top-1 item for u1, u2 can be obtained after 3 Sorted Accesses if DL(u1,u2) is present.
Without DL(u1,u2) 4 Sorted Accesses is required. 10

11. Outline Motivation Modeling & Problem Definition Algorithms
Optimization Opportunities
Experimental Evaluation
Conclusion & Future Work 11

12. Relevance Only (RO) Algorithm
Task : Recommend Top-1 item for a group of 3 users, u1, u2, and u3.
Input: 3 Relevance Lists (ILu1 ,ILu2 ,ILu3 )
Relevance lists are sorted in decreasing scores
Lists are chosen in round-robin fashion during Top-k computation.
Performance is calculated by computing no of Sorted Access
ILu1
i1,4
i3,4
i4,4,
i2,2
ILu2
i2,4
i4,4
i1,2
i3,2
ILu3
i3,3
i1,3
i4,3
i2,3
Top-k Buffer
i1,1.33
Threshold is : 1.93 12

13. Relevance Only (RO) Algorithm
ILu1
i1,4
i3,4
i4,4,
i2,2
ILu2
i2,4
i4,4
i1,2
i3,2
ILu3
i3,3
i1,3
i4,3
i2,3
Top-k Buffer
i1,1.33
i2,1.33
Threshold is : 1.86 13

14. Relevance Only (RO) Algorithm
ILu1
i1,4
i3,4
i4,4,
i2,2
ILu2
i2,4
i4,4
i1,2
i3,2
ILu3
i3,3
i1,3
i4,3
i2,3
Top-k Buffer
i1,1.33
i2,1.33
i3,1.33
Threshold is : 1.73 14

15. Relevance Only (RO) Algorithm
ILu1
i1,4
i3,4
i4,4,
i2,2
ILu2
i2,4
i4,4
i1,2
i3,2
ILu3
i3,3
i1,3
i4,3
i2,3
Top-k Buffer
i1,1.33
i2,1.33
i3,1.33
Threshold is : 1.73 15

16. Relevance Only (RO) Algorithm
ILu1
i1,4
i3,4
i4,4,
i2,2
ILu2
i2,4
i4,4
i1,2
i3,2
ILu3
i3,3
i1,3
i4,3
i2,3
Top-k Buffer
i4,1.6
i1,1.33
i2,1.33
i3,1.33 16
Threshold is : 1.73

17. Relevance Only (RO) Algorithm
ILu1
i1,4
i3,4
i4,4,
i2,2
ILu2
i2,4
i4,4
i1,2
i3,2
ILu3
i3,3
i1,3
i4,3
i2,3
Top-k Buffer
i4,1.6
i1,1.33
i2,1.33
i3,1.33 17
Threshold is : 1.73

18. Relevance Only (RO) Algorithm
ILu1
i1,4
i3,4
i4,4
i2,2
ILu2
i2,4
i4,4
i1,2
i3,2
ILu3
i3,3
i1,3
i4,3
i2,3
Top-k Buffer
i4,1.6
i1,1.33
i2,1.33
i3,1.33 18
Threshold is : 1.73

19. Relevance Only (RO) Algorithm
After 8 Sorted Accesses (3 on IL(u1), 3 on IL(u2) and 2 on IL(u3))
ILu1
i1,4
i3,4
i4,4
i2,2
ILu2
i2,4
i4,4
i1,2
i3,2
ILu3
i3,3
i1,3
i4,3
i2,3
Top-k Buffer
i4,1.6
i1,1.33
i2,1.33
i3,1.33
Threshold is : 1.6
IT STOPS!
Top-1 Item is i4 19

20. Full Materialization (FM) Algorithm
Input: 3 Relevance Lists (ILu1 ,ILu2 ,ILu3 ) and 3 pair-wise materialized disagreement lists (DLu1,u2, DLu1,u3, DLu2,u3)
Relevance lists are sorted in decreasing scores and Disagreement lists are sorted in increasing disagreement scores.
ILu1
i1,4
i3,4
i4,4
i2,2
ILu2
i2,4
i4,4
i1,2
i3,2
ILU3
i3,3
i1,3
i2,3
DLu1,u2
i4,0
i3,2
i2,2
i1,2
DLu1,u3
i4,1
i3,1
i2,1
i1,2
DLu2,u3
i4,1
i1,1
i3,1
i2,1
Top-k Buffer 20
i1,1.33
Threshold is : 1.93

21. Full Materialization (FM) Algorithm
ILu1
i1,4
i3,4
i4,4
i2,2
ILu2
i2,4
i4,4
i1,2
i3,2
ILu3
i3,3
i1,3
i2,3
DLu1,u2
i4,0
i3,2
i2,2
i1,2
DLu1,u3
i4,1
i3,1
i2,1
i1,2
DLu2,u3
i4,1
i1,1
i3,1
i2,1
i1,1.33
i2,1.33
Threshold is : 1.86
Top-k Buffer 21

22. Full Materialization (FM) Algorithm
ILu1
i1,4
i3,4
i4,4
i2,2
ILu2
i2,4
i4,4
i1,2
i3,2
ILu3
i3,3
i1,3
i2,3
DLu1,u2
i4,0
i3,2
i2,2
i1,2
DLu1,u3
i4,1
i3,1
i2,1
i1,2
DLu2,u3
i4,1
i1,1
i3,1
i2,1
i1,1.33
i2,1.33
i3,1.33
Top-k Buffer
Threshold is : 1.73 22

23. Full Materialization (FM) Algorithm
ILu1
i1,4
i3,4
i4,4
i2,2
ILu2
i2,4
i4,4
i1,2
i3,2
ILu3
i3,3
i1,3
i2,3
DLu1,u2
i4,0
i3,2
i2,2
i1,2
DLu1,u3
i4,1
i3,1
i2,1
i1,2
DLu2,u3
i4,1
i1,1
i3,1
i2,1
i4,1.6
i2,1.33
i3,1.23
i4,1.33
Threshold is : 1.73
Top-k Buffer 23

24. Full Materialization (FM) Algorithm
ILu1
i1,4
i3,4
i4,4
i2,2
ILu2
i2,4
i4,4
i1,2
i3,2
ILu3
i3,3
i1,3
i2,3
DLu1,u2
i4,0
i3,2
i2,2
i1,2
DLu1,u3
i4,1
i3,1
i2,1
i1,2
DLu2,u3
i4,1
i1,1
i3,1
i2,1
i4,1.6
i2,1.33
i3,1.23
i4,1.33
Top-k Buffer
Threshold is : 1.66 24

25. Full Materialization (FM) Algorithm
ILu1
i1,4
i3,4
i4,4
i2,2
ILu2
i2,4
i4,4
i1,2
i3,2
ILu3
i3,3
i1,3
i2,3
DLu1,u2
i4,0
i3,2
i2,2
i1,2
DLu1,u3
i4,1
i3,1
i2,1
i1,2
DLu2,u3
i4,1
i1,1
i3,1
i2,1
After 6 Sorted Accesses(1 on each list)
i4,1.6
i2,1.33
i3,1.23
i4,1.33
Top-k Buffer
Threshold is : 1.6
Score(i4) = Threshold = 1.6
IT STOPS!
Top-1 item is i4 25

26. Outline Motivation Modeling & Problem Definition Algorithms
Optimization Opportunities
Experimental Evaluation
Conclusion & Future Work 26

27. Optimization Opportunities
Partial Materialization: Given a space budget to materialize only a subset of n(n-1)/2 disagreement lists, which m lists to materialize?
Threshold Sharpening in Recommendation Computation: How to sharpen threshold during top-k computation? 27

28. Why Partial Materialization ?
A set of 10,000 users has disagreement lists
Only 10% of the disagreement lists can be materialized, given a space budget
Problem : Which lists should we choose so that those gives “maximum benefit” during query processing?
Intuition : Materialize only those lists that significantly improves efficiency.
Recommendation Algorithm needs to be adapted to it (refer to as PM in the paper) 28

29. Disagreement lists Materialization Algorithm
Sort the table with decreasing difference (#SAs) and consider first m rows
User Pair
#SAs without disagreement list
#SAs with disagreement lists
Difference in #SAs
{u1,u2}
200
100
{u3,u4}
290
195 95
{u10,u9}
170 70
{u6,u7}
230
190 40
{u2,u3}
175
145 30
{u5,u6}
179 21
{u7,u8}
120 20 -- m 29

30. Threshold Sharpening
Can we exploit the dependencies between relevance and disagreement lists and sharpen thresholds in FM, RO and PM algorithms?
ILu1
i1,0.5
i3,0.5
---
ILu2
i2,0.5
i3, 0.4
---
DLu1,u2
i3,0.2
I1,0.3
---
Threshold = 1.3
Maximize
(iu1+ iu2)/2 + (1- |iu1-iu2|)
s.t.
0 <= iu1 <= 0.5
0.2 <=| iu1 – iu2 |<= 1
New Threshold = 1.2 30

31. Outline Motivation Modeling & Problem Definition Algorithms
Optimization Opportunities
Experimental Evaluation
Conclusion & Future Work 31

32. Experiments 32 Used Dataset User Studies Performance Experiments
MovieLens data set
71,567 users, 10,681 movies, 10,000,054 ratings
User Studies
Compare the effectiveness of proposed Group Recommendation algorithms with existing approaches using Amazon Mechanical Turk users.
Small and large groups of similar, dissimilar and random users are formed.
Algorithms Average Relevance Only (AR), Least Misery Only (LM), Consensus with Pair-wise Disagreements (RP), Consensus with Disagreement Variance (RV) are compared
Performance Experiments
Performance (no of sorted accesses) comparison of FM, RO and PM varying group size, similarity and no of returned items.
Effectiveness of partial Materialization
Effectiveness of Threshold Sharpening 32

33. Disagreement is important for Dissimilar User Group
Misery Only (MO) is the best model for similar user group
Disagreement is important for dissimilar users. Consensus with Disagreement Variance (RV80) is the best model there. 33

34. Summary of Performance Results
Presence of Disagreement lists improves performance for dissimilar user groups
Sometime Partial Materialization (PM) is the best solution
For the same query, different disagreement lists contribute differently in Top-k processing
Optimization during threshold calculation improves overall performance
Animations take out 34

35. Performance Results
Less sorted accesses (SAs) are required for more similar user groups
Disagreement lists are important for Dissimilar user groups
FM is the best performer for very dissimilar user groups, RO is the best algorithm for very similar user groups.
Sometimes only few disagreement lists attain the best performance. Therefore Partial Materialization is important
Optimization during threshold calculation always achieves better performance (less #SAs) than without optimization case. 35

36. Outline Motivation Modeling & Problem Definition Algorithms
Optimization Opportunities
Experimental Evaluation
Conclusion & Future Work 36

37. Novel optimization opportunities are present.
Conclusion
Disagreement impacts both quality and efficiency of Group Recommendation.
Threshold algorithm, TA can be adapted to compute group recommendations.
Novel optimization opportunities are present.
We ask user to rate movies. We compute similarity from there. 37

38. Ongoing and Future Work
Can disagreement lists be optimized such that they consume less space and contain same information?
Can group recommendation algorithms be adapted to work with those optimized lists?
We ask user to rate movies. We compute similarity from there. 38

39. Thank You ! 39

40. Modeling Semantics in Group Recommendation
Distinguish Relevance and Disagreement in a recommendation’s score
Combine Average Relevance with Disagreement
Combine Least Misery with Disagreement
Disagreement = difference in relevance among group members Group G ={u1, u2 ,u3} relevance(u1,”God Father”) = 5, relevance(u2, “God Father”) = 1, relevance=(u3,” God Father”) = 1
Average Pair-wise Disagreements (G,” God Father”) = (|(5-1)|+|(1-1)|+|(1-5)|)/ 3
Disagreement Variance Disagreement Variance (G,” God Father”) = [( )2 + (1-2.33)2 + (1-2.33)2] / 3 40

41. Consensus Function and Problem Definition
A weighted sum of relevance and disagreement such that for each item, its group relevance is maximized and group disagreement is minimized.
score (G, “God Father”) = w1 x w2 x (1-.2) (after normalization)
Top-k Group Recommendation:
Given a user group G and a consensus function F, return a list of k items to the group such that each item is new to all users in G and the returned k-items are sorted in decreasing value of F. 41