The document discusses using trust-based recommender algorithms to improve recommendations on social platforms. It proposes using a T-index approach which models user trust relationships as a graph and calculates trust scores. An experiment tests this approach against collaborative filtering on four datasets, finding T-index achieves higher F1 scores and creates a more balanced trust network structure. The conclusions are that trust-based recommendations can improve performance for sparse data and support users in finding relevant content or connections on social networks.

1. page 1

2. page 2

3. Recommender systems?
page 3

4. Open Discovery Space (ODS)
A socially-powered, multilingual
open learning infrastructure
in Europe
Recommendations!
Which algorithm best fits ODS platform?
page 4

5. Towards a Trust-based
Recommender for Social platforms
Soude Fazeli, PhD candidate
Dr. Hendrik Drachsler
Prof. Dr. Peter Sloep
page 5

6. Recommender algorithms
1. Content-based
page 6
2. Collaborative filtering ✓

7. Collaborative filtering algorithms
page 7

8. Similarity
Sparsity!
page 8

9. Trustworthy users == like-minded users
Improving prediction accuracy of recommendations
page 9

10. •
•
•
•
•
Golbeck’s TidalTrust
Trust-aware recommender by Massa and Avesani
Andersen et al’s axiomatic approach
T-BAR by Bellaachia and Alathel
And many more…
All require users to give explicit trust ratings!
page 10

11. • Lathia et al.’s trust-based
recommender #neal-lathia #recsys
• Trust model by O’Donovan and
Smyth
Centralized approaches à scalability problem
page 11

12. A social recommender system:
T-index approach
1. Description
• Trust networks: a graph
• Nodes: users
• Edges: trust relationships
• Weights: trust values originating from similarity
• Each user can be assumed as an agent
• Improve the process of finding nearest neighbors
• T-index
• TopTrustee
page 12

13. A social recommender system:
T-index approach
2. Trust propagation mechanism
• A new trust relationship between two far unconnected users is
inferred if and only if:
• Condition 1:
•
page 13
• Mutual trust value between intermediate users is higher than a
certain threshold (v)
Condition 2:
• The number of connecting edges is lower than an upper bound
(L)

14. rated
rated
Alice
Bob
Carol
rated
rated
if A trusts B and B trusts C, then A trusts C if and only if
condition 1 is met
and
condition 2 is met
page 14

15. A social recommender system:
T-index approach
3. T-index?
• T-index: measure of users’ trustworthiness
• H-index: the impact of publications of an author
Indegree (ua) = 7
Indegree (ub) = 5
T-index (ua ) = 2
T-index (ub ) = 4
page 15
Note! Cluster: a group of users
who all trust a common user as
the most trustworthy one (central
user)

16. A social recommender system:
T-index approach
3. T-index?
page 16

17. A social recommender system:
T-index approach
4. What T-index is for?
• TopTrustee : a list of top raters of an item sorted by T-index
• Helps the process of finding nearest neighbors
•
page 17
Providing access to trustworthy users across the trust network including
even those outside the traversal path length limit (L)

18. Social data
page 18

19. • RQ1: How to generate more accurate and thus,
more relevant recommendations by using the
social data originating from social activities of
users within an online environment?
• RQ2: Can the use of the inter-user trust
relationships that originate from the social
activities of users within an online environment
further evolve the network of users?
page 19

20. Hypothesis
page 20

21. • H1: The extended T-index algorithm outperforms
the classical collaborative filtering algorithms in
terms of F1 score, which is a combined value of
precision and recall of the generated
recommendations.
• H2: Using T-index-based trust relationships
between users improves the structure of the trust
network by providing balanced degree distribution
of the users.
page 21

22. Data-driven study
1. Goal
To find out which recommender algorithms best performs
and thus, is suitable for social online platforms like ODS
platform
page 22

23. Data-driven study
2. Method
• Testing several recommender algorithms
• Classical collaborative filtering algorithms using traditional
nearest neighbors method
• T-index approach using graph-based method
• Datasets
• MovieLens – standard dataset
• MACE, OpenScout, Travel well -- similar to the future ODS
dataset
• Using Mahout
page 23

24. Data-driven study
3. Setting
• v = 0.1 (Condition 1), L = 2 (Condition 2)
• Training set 80% and test set 20%
• Sizes of neighborhoods n= (3,5,7,10)
• Size of TopTrustee list m=5
page 24

25. Data-driven study
4. Result
4.1. F1 score
0.1"
0.09"
0.08"
0.07"
0.06"
0.05"
0.04"
0.03"
0.02"
0.01"
0"
OpenScout%
0.14"
0.12"
0.1"
0.08"
Tanimoto3Jaccard"(CF1)"
0.06"
Loglikelihood"(CF2)"
Euclidean"(CF3)"
0.04"
Euclidean"(CF3)"
Graph4based"(CF4)"
0.02"
Graph3based"(CF4)"
Tanimoto4Jaccard"(CF1)"
Loglikelihood"(CF2)"
F1@10%
F1@10%
MACE%
0"
3"
5"
7"
3"
10"
5"
Travel%well%
10"
MovieLens%
0.25"
0.08"
0.2"
0.06"
Tanimoto3Jaccard"(CF1)"
0.04"
Loglikelihood"(CF2)"
Euclidean"(CF3)"
0.02"
Graph3based"(CF4)"
0"
3"
5"
7"
size%of%neighborhood%(n)%
page 25
10"
F1@10%
0.1"
F1@10%
7"
size%of%neighborhood%(n)%
size%of%neighborhood%(n)%
Tanimoto0Jaccard"(CF1)"
0.15"
Loglikelihood"(CF2)"
0.1"
Euclidean"(CF3)"
0.05"
Graph0based"(CF4)"
0"
3"
5"
7"
10"
size%of%neighborhood%(n)%
F1 of the extended T-index and Tanimoto algorithms for different
datasets, based on the size of neighborhood

26. Data-driven study
4.2. Created trust network
Without T-index
page 26
With T-index

27. 2. Data-driven study
4.3. Degree centrality
250"
200"
150"
MovieLens"
degree%
OpenScout"
100"
MACE"
Travel"well"
50"
0"
u1"
u2"
u3"
u4"
u5"
u6"
u7"
u8"
u9"
u10"
Top)10%central%users%
Degree distribution of top-10 central users for
different datasets
page 27

28. Conclusion
• The aim is to support user in social platforms to
find the most suitable content or people
• Recommender systems can be a solution
• Using trust-based approaches to improve
performance of recommender systems even in
case of sparsity
page 28

29. Ongoing and Further work
• Testing recommender algorithms on more datasets coming from
social networking sites
• Go online with the ODS platform (October 2013)
• User evaluation study (February 2013)
page 29

30. Soude
Fazeli
PhD
candidate
Open
University
of
the
Netherlands
Centre
for
Learning
Sciences
and
Technologies
(CELSTEC)
PO-­‐Box
2960
6401
DL
Heerlen,
The
Netherlands
email:
soude.fazeli@ou.nl
page 30