The document outlines a tutorial on collaborative team recommendation methods, focusing on techniques for effective team formation among skilled users. It covers concepts such as team development phases, traditional versus computational approaches to team recommendation, and introduces learning-based heuristics for predicting successful team compositions. The tutorial is organized by key topics including introduction, pioneering techniques, challenges, and practical applications.

1. Tutorial on
Collaborative Team Recommendation for Skilled Users: Objectives, Techniques, and New Perspectives
Fani’s Lab! at UMAP24
Photo: https://www.instagram.com/daviddoubilet/

2. 2
Fani’s Lab!, School of Computer Science, University of Windsor, Canada

3. Mahdis Saeedi, PhD
Post Doctoral Researcher
University of Windsor, Canada
Hossein Fani, PhD
Assistant Professor
University of Windsor, Canada
Christine Wong
Undergrad Student
University of Windsor, Canada
Organizers

4. Outline
I) Introduction and Background (Hossein)
II) Pioneering Techniques (Mahdis)
III) Learning-based Heuristics (Mahdis)
IV) Challenges and New Perspectives (Mahdis)
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives
Hands-on: OpeNTF (Christine)

5. 5
Outline
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives
I) Introduction and Background (Hossein)
II) Pioneering Techniques
III) Learning-based Heuristics
IV) Challenges and New Perspectives
Hands-on: OpeNTF

6. 6
What Is a Team?
A group of users who collaborate together with a common
purpose in order to accomplish the requirements of a task.
[Brannik et al., Psychology Press, 1997]
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

7. 7
What Is a Team?
A group of users who independently endeavor to
accomplish their individual tasks to reach a shared goal or
value, while actively interacting and adapting.
[Zzkarian et. Al., IIE transactions,1999]
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

8. 8
Team vs. Group
Main differences:
- Coordination
- Being responsible for the success
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

9. 9
Dead Poet Society, 1998
Peter Weir
Robin Williams, We don’t read and write poetry because it’s cute …

10. 10

11. The Big Lebowski, 1998
Joel & Ethan Coen
Jeff Bridges, John Goodman, Steve Buscemi
11

12. 12
Italy, 2006

13. 13
Development for a Team
Four distinct phases of development for a team
[Tuckman et. Al., Psychological bulletin ,1965]
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

14. 14
Development for a Team
• Forming: human resource allocation by a leader,
Four distinct phases of development for a team
[Tuckman et. Al., Psychological bulletin ,1965]
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

15. 15
Development for a Team
• Forming: human resource allocation by a leader,
• Storming: developing shared goals and values resulting in conflicts,
Four distinct phases of development for a team
[Tuckman et. Al., Psychological bulletin ,1965]
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

16. 16
Development for a Team
• Forming: human resource allocation by a leader,
• Storming: developing shared goals and values resulting in conflicts,
• Norming: conflict resolution between team members,
Four distinct phases of development for a team
[Tuckman et. Al., Psychological bulletin ,1965]
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

17. 17
Development for a Team
• Forming: human resource allocation by a leader,
• Storming: developing shared goals and values resulting in conflicts,
• Norming: conflict resolution between team members,
• Performing: focusing on individual tasks to reach the shared goal or value.
Four distinct phases of development for a team
[Tuckman et. Al., Psychological bulletin ,1965]
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

18. Tuckman, Bruce W. "Developmental sequence in small groups." Psychological bulletin 63.6 (1965): 384.

19. 19
Development for a Team
• Forming: human resource allocation by a leader,
• Storming: developing shared goals and values resulting in conflicts,
• Norming: conflict resolution between team members,
• Performing: focusing on individual tasks to reach the shared goal or value.
Four distinct phases of development for a team
[Tuckman et. Al., Psychological bulletin ,1965]
Are all developed teams successful?
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

20. 20
Development for a Team
• Forming: human resource allocation by a leader,
• Storming: developing shared goals and values resulting in conflicts,
• Norming: conflict resolution between team members,
• Performing: focusing on individual tasks to reach the shared goal or value.
Four distinct phases of development for a team
[Tuckman et. Al., Psychological bulletin ,1965]
Are all developed teams successful? What is success?!
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

21. 21
Italy, World Cup 2006
What is success?

22. US$1.446 billion vs. no Oscar!
22
What is success?

23. 23
What is success?

24. https://www.facebook.com/share/p/kXYaYaRvRCr5K2Ze
https://openreview.net/forum?id=idpCdOWtqXd60
24
What is success?

25. 25
Successful Team
A team that achieve a desired outcome or fulfill a goal is a successful team.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

26. 26
Produced Published
Issued
Released
Success

27. 27
Traditional approach
Teams were formed manually by relying on human experience and instinct in a tedious,
error-prone, and suboptimal process.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

28. 28
Teams were formed manually by relying on human experience and instinct in a tedious,
error-prone, and suboptimal process.
Difficulties:
Traditional approach
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

29. 29
o Large number of candidates
- Different knowledge
- Different culture
- Different characteristic
o Hidden personal and societal biases
- Race
- Gender
- Popularity
o Multitude of criteria to optimize
- Communication cost
- Budget
- Time
Traditional approach
Teams were formed manually by relying on human experience and instinct in a tedious,
error-prone, and suboptimal process.
Difficulties:
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

30. 30
o Large number of candidates
- Different knowledge
- Different culture
- Different characteristic
o Hidden personal and societal biases
- Race
- Gender
- Popularity
o Multitude of criteria to optimize
- Communication cost
- Budget
- Time
Traditional approach
Teams were formed manually by relying on human experience and instinct in a tedious,
error-prone, and suboptimal process.
Difficulties:
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives
Manual team recommendation on a large scale is almost impossible

31. 31
Computational Approach
A variety of disciplines have long been endeavoring to find algorithmic solutions.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

32. Team Allocation, Team Selection, Team Composition, Team Configuration, Team Recommendation, Team Formation
32
Computational Approach
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

33. 33
Team Recommendation Works in Time
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

34. 34
Outline
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives
I) Introduction and Background
II) Pioneering Techniques
III) Learning-based Heuristics
IV) Challenges and New Perspectives
Hands-on: OpeNTF

35. 35
Graph-based Team Recommendation
Underlying network structure is a key factor to form an effective team.
[Miller, Computational Modeling and Organization Theories. AAAI Press, 2001]
[Gaston et al., Proceedings of the 1st NAACSOS Conference, 2003]
[Gaston et al., AAAI Technical Report, 2004]
[Chen, INCoS, 2010]
• Organizations’inherent hierarchical network structure.
• Users' social and collaborative ties
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

36. 36
User Network
Synergistic interdisciplinary discoveries from social network analysis and graph
theory.
The problem of team recommendation has been translated into graph mining.
The user network is considered as an attributed graph:
users as nodes,
skills as node attributes,
edges as ties between users.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

37. 37
Subgraph Optimization
What is the specific implication of this statement?
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

38. 38
Subgraph Optimization Objectives
• Communication Cost
• Proficiency
• Personnel Cost
• Geographical Distance
• Density
• Multi-Objective
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

39. 39
Subgraph Optimization Objectives
• Communication Cost
• Proficiency
• Personnel Cost
• Geographical Distance
• Density
• Multi-Objective
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

40. 40
• A metric to measures how effectively users communicate.
• lower communication cost in a team indicates easier
communication, better understanding and collaborations among
team members.
• Communication cost Team performance
Communication Cost (φ)
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

41. 41
Communication cost computations:
•
•
Communication Cost (φ)
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

42. 42
Objective overview
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

43. • Non-temporal (Static)
• Temporal (Dynamic)
43
Communication Cost Minimization Methods
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

44. • Non-temporal (Static)
• Temporal (Dynamic)
44
Communication Cost Minimization Algorithms (φ)
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

45. • Sum of distances
• Sum of edge weights
• Diameter of the subgraph
• Cost of the spanning tree
45
Non-temporal (Static) Communication Cost
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

46. • Non-temporal (Static)
• Temporal (Dynamic)
46
Communication Cost Minimization Algorithms (φ)
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

47. 47
Temporal (Dynamic)Communication Cost
Temporal (dynamic) communication cost is based on the fact that
the least communication cost exists between users who could
maintain many successful collaborations over time till recently or
currently.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

48. 48
Subgraph Optimization Objectives
• Communication Cost
• Proficiency
• Personnel Cost
• Geographical Distance
• Density
• Multi-Objective
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

49. 49
Proficiency of users indicates the level of expertise in a particular profession
or a skill.
• h-index
• number of citations
Proficiency(ϕ)
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

50. 50
Proficiency(ϕ)
Authority [Zihayat et. Al., Authority-based Team Discovery in Social Networks. In EDBT. OpenProceedings.org, 2017]
Sum of inverse of experties level
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

51. 51
Objective overview
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

52. 52
Multi-Objective Optimization
• Communication cost and proficiency
[Zihayat et. al., AMW, 2018]
[Zihayat et. al., EDBT, 2017]
• Communication cost and personnel cost
[Aijun et. al., SDM, 2013]
[Kargar et. al., CSE, 2013]
[Zihayat et. al., AMW, 2018]
• Density and proficiency
[Juarez et. al., GECCO., 2018]
• Communication cost, personnel cost and proficiency
[Zihayat et. al., WI-IAT, 2014]
• Dynamic communication cost, geographical proximity, and proficiency
[Selvarajah et. al., Expert Syst. Appl., 2021]
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

53. 53
Objective overview
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

54. 54
Pioneering Techniques
• Subgraph Optimization Objectives
• Subgraph Optimization Algorithms
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

55. 55
Subgraph Optimization Algorithms
Subgraph optimization problems are proven to be NP-hard
[Karp, Complexity of computer computations, Springer, 2010]
Heuristics have been developed to solve this problem in polynomial
time through greedy and approximation algorithms.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

56. 56
Communication Cost Minimization Algorithms
Lappas et al. is the first attempt to form a team based on the subgraph
optimization on the user network. [Lappas et. al., KDD, 2009]
Key Concept: Reducing the cost of communication between team
members.
 Diameter-Based Optimization RarestFirst
 Spanning Tree-Based Optimization CoverSteiner and
EnhancedSteiner
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
&
New
Perspectives

57. 57
Outline
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives
I) Introduction and Background
II) Pioneering Techniques
III) Learning-based Heuristics
IV) Challenges and New Perspectives
Hands-on: OpeNTF

58. 58
Learning-based Heuristics
Paradigm Shift to Learning-Based Methods
• Learn the inherent structure of ties among users and their skills.
• Utilize all past (un)successful team compositions as training samples.
• Predict future teams and their performance.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

59. 59
Learning-based Heuristics
Advantages of Learning-Based Methods:
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

60. 60
Learning-based Heuristics
Advantages of Learning-Based Methods:
• Efficiency: Enhanced by iterative and online learning procedures.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

61. 61
Learning-based Heuristics
Advantages of Learning-Based Methods:
• Efficiency: Enhanced by iterative and online learning procedures.
• Efficacy: Improved prediction and team performance.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

62. 62
Learning-based Heuristics
Advantages of Learning-Based Methods:
• Efficiency: Enhanced by iterative and online learning procedures.
• Efficacy: Improved prediction and team performance.
• Scalability: Can handle larger and more dynamic user networks.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

63. 63
Learning-based Heuristics
Advantages of Learning-Based Methods:
• Efficiency: Enhanced by iterative and online learning procedures.
• Efficacy: Improved prediction and team performance.
• Scalability: Can handle larger and more dynamic user networks.
• Dynamic Adaptation: Better suited for temporal conditions.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

64. 64
Learning-based Heuristics
• Model Architecture
• Training Strategies
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

65. 65
Model Architecture
o Feedforward
o Variational Bayesian
o Graph Representation Learning
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

66. 66
Learning-based Heuristics
Team
Given a set of skills S ={i} and a set
of users E ={j}, a team of users
e⊂E ; e ≠ ∅ that collectively cover
the skill set s⊂S ; s ≠ ∅ is shown by
(s,e) along with its success status
y∈{0,1}. Further, T = {(s, e)𝑦: y∈{0,1}
indexes all previous teams.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

67. 67
Learning-based Heuristics
Team
Given a set of skills S ={i} and a set
of users E ={j}, a team of users
e⊂E ; e ≠ ∅ that collectively cover
the skill set s⊂S ; s ≠ ∅ is shown by
(s,e) along with its success status
y∈{0,1}. Further, T = {(s, e)𝑦: y∈{0,1}
indexes all previous teams.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

68. 68
Learning-based Heuristics
Team
Given a set of skills S ={i} and a set
of users E ={j}, a team of users
e⊂E ; e ≠ ∅ that collectively cover
the skill set s⊂S ; s ≠ ∅ is shown by
(s,e) along with its success status
y∈{0,1}. Further, T = {(s, e)𝑦: y∈{0,1}
indexes all previous teams.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

69. 69
Learning-based Heuristics
Team Recommendation
Given a subset of skills s and all teams T, the
Team Recommendation problem aims at
identifying an optimal subset of users 𝑒∗
such that their collaboration in the predicted
team (s, 𝑒∗) is successful, that is (s, 𝑒∗)𝑦=1 ,
while avoiding a subset of users 𝑒′ resulting
in (s, 𝑒′)𝑦=0 . More concretely, the Team
Recommendation problem is to find a
mapping function f of parameters 𝜽 from the
powerset of skills to the powerset of s such
that 𝑓𝜽 ∶ 𝑃 𝑆 → 𝑃(𝐸), 𝑓𝜽 s = 𝑒∗
.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

70. Team Recommendation
Given a subset of skills s and all teams T, the
Team Recommendation problem aims at
identifying an optimal subset of users 𝑒∗
such that their collaboration in the predicted
team (s, 𝑒∗) is successful, that is (s, 𝑒∗)𝑦=1 ,
while avoiding a subset of users 𝑒′ resulting
in (s, 𝑒′)𝑦=0 . More concretely, the Team
Recommendation problem is to find a
mapping function f of parameters 𝜽 from the
powerset of skills to the powerset of s such
that 𝑓𝜽 ∶ 𝑃 𝑆 → 𝑃(𝐸), 𝑓𝜽 s = 𝑒∗
. 70
Learning-based Heuristics
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

71. Team Recommendation
Given a subset of skills s and all teams T, the
Team Recommendation problem aims at
identifying an optimal subset of users 𝑒∗
such that their collaboration in the predicted
team (s, 𝑒∗) is successful, that is (s, 𝑒∗)𝑦=1 ,
while avoiding a subset of users 𝑒′ resulting
in (s, 𝑒′)𝑦=0 . More concretely, the Team
Recommendation problem is to find a
mapping function f of parameters 𝜽 from the
powerset of skills to the powerset of s such
that 𝑓𝜽 ∶ 𝑃 𝑆 → 𝑃(𝐸), 𝑓𝜽 s = 𝑒∗
. 71
Learning-based Heuristics
f
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

72. Team Recommendation
Given a subset of skills s and all teams T, the
Team Recommendation problem aims at
identifying an optimal subset of users 𝑒∗
such that their collaboration in the predicted
team (s, 𝑒∗) is successful, that is (s, 𝑒∗)𝑦=1 ,
while avoiding a subset of users 𝑒′ resulting
in (s, 𝑒′)𝑦=0 . More concretely, the Team
Recommendation problem is to find a
mapping function f of parameters 𝜽 from the
powerset of skills to the powerset of s such
that 𝑓𝜽 ∶ 𝑃 𝑆 → 𝑃(𝐸), 𝑓𝜽 s = 𝑒∗
. 72
Learning-based Heuristics
f
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

73. 73
Model Architecture
o Feedforward
o Variational Bayesian
o Graph Representation Learning
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

74. 74
Feedforward
Neural Team Recommendation
Given the training set T, Neural
Team Recommendation estimates
𝑓𝜽 (s) using a multi-layer neural
network that learns, from T , to
map a vector representation of
subset of skills s, referred to as
𝒗𝒔 ,to a vector representation of
subset of experts 𝑒∗ , referred to
as 𝒗𝒆∗ , by maximizing the
posterior (MAP) probability of 𝜽 in
𝑓𝜽 over T, that is, 𝑎𝑟𝑔𝑚𝑎𝑥 𝜽 p(𝜽|T)
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

75. h = 𝜋(𝜽1𝑣𝒔+𝒃1)
𝑙𝑜𝑔𝑖𝑡𝑠 → z = 𝜽2𝒉 + 𝒃2
𝑣𝑒∗ = σ (z)
75
Neural Team Recommendation
Given the training set T, Neural
Team Recommendation estimates
𝑓𝜽 (s) using a multi-layer neural
network that learns, from T , to
map a vector representation of
subset of skills s, referred to as
𝒗𝒔 ,to a vector representation of
subset of experts 𝑒∗ , referred to
as 𝒗𝒆∗ , by maximizing a posterior
(MAP) probability of 𝜽 in 𝑓𝜽 over T,
that is, 𝑎𝑟𝑔𝑚𝑎𝑥 𝜽 p(𝜽|T)
Feedforward
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

76. 76
Neural Architecture
o Feedforward
o Variational Bayesian
o Graph Representation Learning
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

77. 77
𝜽~(𝜇, 𝜎) q(𝜽)=𝒩 𝜇, 𝜎
Parameters' means and variances are
estimated by minimizing the Kullback-
Leibler divergence between q and p
KL(q∥ p(𝜽|T )= ‫׬‬ q(𝜽|𝜇, 𝜎) log[
𝑞
𝑝(𝜽|𝑇)
]d𝜽
= 𝐸𝑞 log [
𝑞
𝑝(𝜽|𝑇)
] , KL(q∥
p)≥ 0
Variational Bayesian
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

78. 78
Neural Architecture
o Feedforward
o Variational Bayesian
o Graph Representation Learning
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

79. 79
Graph Representation Learning
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

80. 80
Graph Representation Learning
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

81. 81
Graph Representation Learning
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

82. 82
Learning-based Heuristics
• Model Architecture
• Training Strategies
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

83. 83
Training Strategies
• Negative Sampling
• Streaming Strategy
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

84. 84
Training Strategies
• Negative Sampling
• Streaming Strategy
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

85. 85
Negative Sampling
Most available data in team recommendation domain only consists
of successful teams.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

86. 86
Negative Sampling
Most available data in team recommendation domain only consists
of successful teams.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

87. 87
Negative Sampling
Closed-World Assumption
• No currently known successful team is considered unsuccessful
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

88. Optimization function discriminates successful from unsuccessful
teams through negative sampling.
88
Negative Sampling
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives
Positive Samples
Probability Distribution
Negative Samples
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

89. 89
Training Strategies
• Negative Sampling
• Streaming Strategy
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

90. 90
Streaming Strategy
Predicting future successful teams of users who can effectively collaborate is
challenging due to experts’ temporality of
• Skill sets
• Levels of expertise
• Collaboration ties
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

91. 91
Neural model learns vector representations for users and skills
at time interval 𝑡
Initiates learning for the next time interval 𝑡 + 1
Streaming Strategy
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

92. 92
Streaming Strategy
Neural model learns vector representations for users and skills
at time interval 𝑡
Initiates learning for the next time interval 𝑡 + 1
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

93. 93
Streaming Strategy
Neural model learns vector representations for users and skills
at time interval 𝑡
Initiates learning for the next time interval 𝑡 + 1
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

94. 94
• Dataset
• Effectiveness
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives
Evaluation Methodology

95. Evaluation Methodology
• Dataset
• Effectiveness
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

96. Dataset
o DBLP
o IMDB
o USPT
o GitHub
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

97. Dataset
o DBLP
o IMDB
o USPT
o GitHub
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

98. Dataset
o DBLP
o IMDB
o USPT
o GitHub
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

99. Dataset
o DBLP
o IMDB
o USPT
o GitHub
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

100. Dataset
o DBLP
o IMDB
o USPT
o GitHub
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

101. 101
Dataset Statistics and Distribution
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

102. Evaluation Methodology
• Dataset
• Effectiveness
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

103. 103
Effectiveness
Classification Metrics
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

104. 104
Effectiveness
Ranking Metrics
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

105. 105
Evaluation Metrics
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
Challenges
and
New
Perspectives

106. 106
Outline
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
hallenges
and
New
Perspectives
I) Introduction and Background
II) Pioneering Techniques
III) Learning-based Heuristics
IV) Challenges and New Perspectives
Hands-on: OpeNTF

107. 107
Challenges and New Perspectives
• Fair and Diverse Team Recommendation
• Spatial Team Recommendation
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
hallenges
and
New
Perspectives

108. 108
Challenges and New Perspectives
• Fair and Diverse Team Recommendation
• Spatial Team Recommendation
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
hallenges
and
New
Perspectives

109. 109
Fair and Diverse Team Recommendation
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
hallenges
and
New
Perspectives
The primary focus of existing team recommendation methods is
maximizing the models’ efficacy, largely ignoring diversity in the
recommended users.

110. 110
Fair and Diverse Team Recommendation
The primary focus of existing team recommendation methods is
maximizing the models’ efficacy, largely ignoring diversity in the
recommended users.
Fairness in machine learning algorithms guarantees where a disadvantaged
group also known as a protected group, should be treated similarly to the
advantaged group as a whole.
[Altenburger et.al., AAAI Conference on Web and Social Media, 2017]
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
hallenges
and
New
Perspectives

111. 111
Fair and Diverse Team Recommendation
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
hallenges
and
New
Perspectives
There is little to no diversity-aware algorithmic method that
mitigates unfair societal biases in team recommendation models.

112. 112
Fair and Diverse Team Recommendation
There is little to no diversity-aware algorithmic method that
mitigates unfair societal biases in team recommendation models.
Notions of Group Fairness:
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
hallenges
and
New
Perspectives

113. 113
Fair and Diverse Team Recommendation
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
hallenges
and
New
Perspectives
There is little to no diversity-aware algorithmic method that
mitigates unfair societal biases in team recommendation models.
Notions of Group Fairness:
• Demographic Parity
• Equality of Opportunity

114. 114
Fair and Diverse Team Recommendation
Demographic Parity :
Enforces the membership in a team to be independent of values of a
protected attribute for team members.
Male Female
[P(𝑒0 ∈ 𝑇) = P(𝑒1
′
∈ 𝑇)] ∧ [P(𝑒0 ∉ 𝑇) = P(𝑒1
′
∉ 𝑇)]
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
hallenges
and
New
Perspectives

115. 115
Fair and Diverse Team Recommendation
Skilled Male
Skilled Female
[P(𝑒0 ∈ 𝑇|𝑒0 , ) = P(𝑒1
′
∈ 𝑇|𝑒1
′
, )]
𝑆𝑒1
′ ∩ 𝑆 ≠ ∅
𝑆𝑒0
∩ 𝑆 ≠ ∅
Equality of Opportunity:
Enforces the skilled membership in a team to be independent of values of
a protected attribute for team members.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
hallenges
and
New
Perspectives

116. 116
Fair and Diverse Team Recommendation
Debiasing algorithms can be categorized based on their integration into
the machine learning pipeline:
- Pre-process: No work, to the best of our knowledge
- In-process: Little work, vivaFemme [Moasses et.al., BIAS-SIGIR, 2024]
- Post-process: Little work, Adila [Loghmani et.al., BIAS-ECIR, 2022], [Geyik et.al., KDD, 2019],
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
hallenges
and
New
Perspectives

117. 117
Fair and Diverse Team Recommendation
• Pre-processing methods modify data or label through re-sampling
heuristics before model training.
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
hallenges
and
New
Perspectives

118. 118
Fair and Diverse Team Recommendation
• In-processing techniques adjust the optimization process of models to
balance accuracy and fairness.
[Moasses et.al., BIAS-SIGIR, 2024]
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
hallenges
and
New
Perspectives

119. 119
Fair and Diverse Team Recommendation
• Post-processing methods modify model outputs during inference,
which may involve altering thresholds, scoring rules, or reranking the
recommended list of items.
[Loghmani et.al., BIAS-ECIR, 2022], [Geyik et.al., KDD, 2019]
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
hallenges
and
New
Perspectives

120. 120
Challenges and New Perspectives
• Fair and Diverse Team Recommendation
• Spatial Team Recommendation
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
hallenges
and
New
Perspectives

121. 121
Spatial Team Recommendation
The majority of existing methods use skills as a primary
factor while overlooking geographical location and the
corresponding ties it leads to between users in a team.
- Time zone
- Region
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
hallenges
and
New
Perspectives

122. https://fani-lab.github.io/OpeNTF/tutorial/umap24/
All materials are available on the tutorial website.
• List of related papers
• Slides
• Video
• Link to libraries
122
Resources
Introduction
and
Background
Pioneering
Techniques
Learning-based
Heuristics
hallenges
and
New
Perspectives