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All content in this area was uploaded by Thanaphon Phukseng on Jun 30, 2015
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Content uploaded by Thanaphon Phukseng
Author content
All content in this area was uploaded by Thanaphon Phukseng on Jun 30, 2015
Content may be subject to copyright.
Content uploaded by Thanaphon Phukseng
Author content
All content in this area was uploaded by Thanaphon Phukseng on Jun 30, 2015
Content may be subject to copyright.
Scholarly Article)
*: thanaphon134@yahoo.com
Recommender System Using Trust and Expert
*, ,
Thanaphon Phukseng*, Sucha Smanchat and Sunantha Sodsee
Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok,
Pracharat Road, Wongsawang, Bangsue, Bangkok
:; ;
Abstract
Recommender systems are crucial for consumers who have too much information about
products and services. The system assists in decision making by presenting the most appropriate
facts to consumers. However, problems, such as data sparsity and cold start, are still encountered
when using the systems. Therefore, certain techniques have been combined with the system in
order to solve the problems and enhance the effectiveness of the recommender system. This
article aims at reviewing existing researches regarding recommender systems that use trust and
expert techniques as well as the combination of the two techniques to develop the
recommender system. The analytical review shows that the combination of the trust and expert
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techniques can solve most of the problems and improve the efficiency of the recommender
system.
Keywords:recommender system; trust; expert
1.
exchange)
selling/buying)
E-commerce)
data
overload [1]
data sparsity)
cold start)
2-4]
trust) expert)
2.
[5]
6
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519
[5]Content-based, Collaborative
Filtering, Demographic, Knowledge-based,
Community-based Hybrid recommen-
dation
Content-based
[6]
[5]
Collaborative filtering
[6]
Demographic
[5]
Knowledge-based
Casebase [5]
Community-based
[5]
Hybrid recommendation
Collabo-
rative filtering Content-
based
[1,5]
3. -
1.0
2.0
[7] social
networks)
Facebook, Line LinkIn
Flickr, Instragram
Youtube
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520
social commerce) [8]
[8]
data overload
-
(social recommendation
networks)
Amazon.com
Facebook [9]E-Bay.com
Feedback)
Content-based, Collaborative filtering
Hybrid recommendation
Collaborative filtering
4.
4.1
Artificial Intelligence)
[10]
Fuzzy logic)
[11]
B2C [12]
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521
3
Very recommendable)Recom-
mendable)Not match interes-
ting)
Artificial neural
networks)
neurons
[13]
[14]
[6]
3
1
1 [6]
1
Back propagation neural networks
Linear regression
3Back propagation
neural networks
Genetic
algorithms)
15]
Collaborative
filtering 16]
Collaborative
filtering
Content-
based
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522
4.2
Semantic
technology)
17,18]
Taxonomy
Ontology
17]
4.3
Trust)
Collaborative filtering
Facebook [19]
2
Trust by relation)
Trust by reputation)
Trust-semantic fusion-
based recommendation [4]
2
2 Trust-semantic fusion-based recommendation [4]
Trust-
semantic fusion-based recommendation
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523
Trust propagation)
Collaborative filtering
,
Trust
information collection)
Trust
evaluation)
Graph-based)
Interaction-based)
Hybrid) (
Trust dissemination)
Visualization)
Trust-based
recommendation)
Trust type)
Calculative)
Relational) (
Trust property)
Context specific)
Dynamic)
Propagative)
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524
Composable) Trust
model)
Sequence-base
trust)
Sequence-base trust
Sequence-base Trust [
Multi-
criteria trust enhanced collaborative filtering
(MC-TeCF) [1]
4
4
MC-TeCF
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525
8]
5
MC-TeCF [
5
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526
5
3
3
24]
Hybrid personal and group trust model) [25]
6
Hybrid personal and group trust model [
6
Hybrid personal and group trust model
Hybrid personal trust, HPT)
(Item-level group trust)
Dynamic trust
Relevant trust
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527
walker [3]
Random walk)
6.
Expert)
,
2
Multi-facet
Semantic
reasoning)
Twitter [30] 3
1)
Tweets) (2)
Mini biographicanduser list)
(3)
Early
adoption (EA)
Heavy access
(HA)
Niche-item access (NA)
3
Online forum
7
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528
32]
7
4 (1) Semantic
analysis module
(2)Expertise analysis module
-
3)Relation analysis module
4)Social network-based markow
chain analysis module
Online forum
(Query)
Content-based
Collaborative filtering
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529
7.
5
MARec (Multi-
agent recommender) [33]
Expert agent)
MARec8
8 MARec
Case base
Case base
Case base
Trust
mechanism)
8 MARec
9
9
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530
34]
1
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531
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532
2
8.1
[4,34]
2
[1,20,34]
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