AIST 2015 Conference Paper Presentation

1. USER SIMILARITY COMPUTATION FOR
COLLABORATIVE FILTERING USING
DYNAMIC IMPLICIT TRUST
Presented By:
FALGUNI ROY
Institute of Information Technology (IIT)
University of Dhaka, Dhaka, Bangladesh
5/10/2015
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SUBMISSION ID: 136

2. AUTHORS
 Falguni Roy
 Sheikh Muhammad Sarwar
 Institute of Information Technology, University of Dhaka,
Dhaka, Bangladesh
 Mahamudul Hasan
 Department of Computer Science and Engineering, University
of Dhaka, Dhaka, Bangladesh
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3. OUTLINE
 Overview of Trust Based Recommender System
 Problem of Existing System
 Our Contribution
 Background
 Proposed Framework
 Experimental Results
 Conclusion
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4. TRUST BASED RECOMMENDER SYSTEM
 Guo et al. defines trust in recommender system as
“Trust is defined as one's belief towards the ability
of others in providing valuable ratings“ [1].
 Express the integrity in the relationship between
two entities.
 Trust used to scale similarity.
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5. TRUST PROPERTIES [2]
 Asymmetry: Two users u and v involved in a trust
relationship, user u trusting user v cannot guarantee
that user v will trust user u to the same extent.
 Transitivity: If users u trusts v, and v trusts p, it can
be inferred that users u trusts p to some extent.
 Dynamicity: Trust established and changed over
time as more evidences or experience arrive.
 Context Dependence: A user who is trustworthy in
movies may not be trustable in IT technology.
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6. TYPE OF TRUST
 On the basis of trust computation
 Explicit Trust
 Implicit Trust
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7. EXPLICIT TRUST
 Trust value is calculated by pre-existing social link between
users.
 The link is defined as either by defining “web of trust” or
assigning a “trust statement”.
 Limitations
 Additional user effort.
 Binary format bounds the users to express their degree of trust to
a user.
 New users have to first build sufficient trust link before receive
services from RS.
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8. IMPLICIT TRUST
 Extract trust values between users based on
item ratings –
 Analyzing rating patterns,
 Rating values, and
 Historical behavior of ratings
 Trustworthiness of a user is determined by the
prediction accuracy of a user in the past [2].
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9. PROBLEM OF EXISTING SYSTEM
 Existing Implicit Trust based RS
 Don’t concern about users’ changing interests.
 Treats users’ similarity as symmetric.
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10. OUR CONTRIBUTION
 Define a framework which considers trust, time and
similarity in a single function and deals the existing
problems, mentioned in pervious slide.
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11. BACKGROUND
 Qusai Shambour et al. [4] (TM1)
 Lathia et al. [5] (TM2)
 Papagelis et al. [6] (TM3)
 Hwang et al. [7] (TM4)
 O'Donovan et al. [8] (TM5)
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12. A COMPARISON OF DIFFERENT TRUST METRICS IN
TERMS OF TRUST PROPERTIES [2][3]
Methods Asymmetry Transitivity Dynamicity Context
Dependence
TM1 [4] No Yes No No
TM2 [5] No Yes No No
TM3 [6] No Yes, iff s> ϴ
ϴ = 0.707
No No
TM4 [7] No Yes No No
TM5 [8] No Yes No No
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13. PROPOSED FRAMEWORK
 The proposed framework consists of the following
modules:
 Similarity Computation module (SC),
 Trust Computation module (TC) and
 Combined Trust and Similarity Computation module
(CTSC)
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14. SIMILARITY COMPUTATION MODULE
 Extract a neighborhood of similar minded users for
the target user.
 Similarity is calculated by integrating Pearson
Correlation Coefficient (PCC) and Jaccard similarity
method [4] defined as JPCC.
 JPCC(a,b) = PCC(a,b) * Jaccard(a,b) ……….. (1)
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15. TRUST COMPUTATION MODULE
 Implicit trust is populated by defining the similarity or
degree of similarity between the users [4].
 Proposed a new implicit trust method for determining
the implicit trust between the users as an integration of
Mean Square Difference (MSD) and Confidence and
consider users’ changing interests to support trust
properties.
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16. TRUST COMPUTATION MODULE(CONT…)
 If user “b” delivered high accurate recommendation in
the past to the active user “a” , then user “b” should
acquire a high trust score from active user “a “
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…………..…… (2)
 Here,
 λ = Decay Rate (person wise constant).
 T = Tr – Ti, where, Tr denotes the most recent item rated time
of user b and Ti denotes the specific time when user b rates
item i.

17. TRUST COMPUTATION MODULE(CONT…)
o The pattern of forgetting information of Human is
non-linear [9][10]
λ = 1/ Tmedian …………………… (3)
o Here,
o T = Tr – Ti, where, Tr denotes the most recent item rated time
of user b and Ti denotes the specific time when user b rates
each item i.
o Determine Tmedian from the collection of all T of a user
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18.  MSD: Used to measure the degree of similarity between
two users.
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……………… (4)
o Confidence: Expresses the reliability of the association
between two users which is influenced by the changing
number of co-rated items between them in the system.
…… ………… (5)
……...……. (6)

19. COMBINED TRUST AND SIMILARITY
COMPUTATION MODULE
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TJPCC(a,b) =
𝐽𝑃𝐶𝐶 𝑎,𝑏 +𝑇𝑟𝑢𝑠𝑡(𝑎,𝑏)
2
………(7)

20. EXISTING PROBLEMS DEAL BY PROPOSED TRUST
METHOD
 Proposed method supports asymmetric property of trust which
means that the degree of trust between two users will not be
same. As a consequence, the proposed method provides two
different similarities for a user-pair and it is based on the trust
value of a user on another user.
 The proposed method uses the recommender’s items’ rating time
at TC module to pay concern on the recommender’s changing
interests and it gives more importance to the recommender’s
recent preferences compared to his/her old preferences at the
time of trust computation, which will effects on similar users
definition.
 The trust value, computed by proposed method, is transitive
because we could build indirect trust connection between users
with this trust value.
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21. EXPERIMENTAL RESULTS
Dataset Movielens
Users 6040
Movies 3952
Ratings 1000209
Rating Time Information 2 year
Min and Max Value of Rating 1-5
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22. EXPERIMENTAL RESULTS (CONT…)
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TABLE 1. MAE of Proposed and Different Similarity & Trust Method

23. CONCLUSION
 Main contribution lies in defining a framework which
considers trust, time and similarity in a single
function.
 Proposed model considers three properties of trust
in a single framework.
 Experimental results show reasonable accuracy.
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24. REFERENCE
1. Guibing Guo. “Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender
systems”. In Proceedings of the 7th ACM conference on Recommender systems, pages 451-454. ACM, 2013
2. Soude Fazeli, Babak Loni, Alejandro Bellogin, Hendrik Drachsler, and Peter Sloep. “Implicit vs. explicit trust
in social matrix factorization”. In Proceedings of the 8th ACM Conference on Recommender systems, pages
317-320. ACM, 2014.
3. Guibing Guo, Jie Zhang, Daniel Thalmann, Anirban Basu, and Neil Yorke-Smith. “From ratings to trust: an
empirical study of implicit trust in recommender systems.” 2014
4. Q. Shambour and J. Lu, “A trust-semantic fusion-based recommendation approach for e-business applications,”
Decision Support Systems, vol. 54, no. 1, pp. 768–780, 2012.
5. N. Lathia, S. Hailes, and L. Capra. “Trust-based collaborative filtering”. In Trust Management II, 2008.
6. M. Papagelis, D. Plexousakis, and T. Kutsuras. Alleviating the sparsity problem of collaborative filtering using
trust inferences. In Trust management. 2005.
7. Chein-Shung Hwang and Yu-Pin Chen. “Using trust in collaborative filtering recommendation”. In New trends
in applied articial intelligence, pages 1052-1060. Springer, 2007.
8. John O'Donovan and Barry Smyth. Trust in recommender systems. In Proceedings of the 10th international
conference on Intelligent user interfaces, pages 167-174. ACM, 2005.
9. Y. Ding and X. Li, Time weight collaborative filtering," in Proceedings of the 14th ACM international
conference on Information and knowledge management, pp. 485{492, ACM, 2005.
10. “Memory: A Contribution to Experimental Psychology -- Ebbinghaus (1885/1913)“ Retrieved 2007-08-23.
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25. Thanks For Being Attentive to
The Presentation
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