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Online Social Networks and Media 7 (2018) 1–11
Contents lists available at ScienceDirect
Online Social Networks and Media
journal homepage: www.elsevier.com/locate/osnem
Social trust model for rating prediction in recommender systems:
Effects of similarity, centrality, and social ties
Anahita Davoudi ∗, Mainak Chatterjee
University of Central Florida, Orlando, FL 32816, United States
a r t i c l e i n f o
Article history:
Received 2 October 2017
Revised 22 May 2018
Accepted 25 May 2018
a b s t r a c t
The success of e-commerce companies is becoming increasingly dependent on product recommender sys-
tems which have become powerful tools that personalize the shopping experience for users based on user
interests and interactions. Most modern recommender systems concentrate on finding the relevant items
for each user based on their interests only, and ignore the social interactions among users. Some recom-
mender systems, rely on the ‘trust’ of users. However in social science, trust, as a human characteristic,
is a complex concept with multiple facets which has not been fully explored in recommender systems.
In this paper, to model a realistic and accurate recommender system, we address the problem of so-
cial trust modeling where trust values are shaped based users characteristics in a social network. We
propose a method that can predict rating for personalized recommender systems based on similarity,
centrality and social relationships. Compared with traditional collaborative filtering approaches, the ad-
vantage of the proposed mechanism is its consideration of social trust values. We use the probabilistic
matrix factorization method to predict user rating for products based on user-item rating matrix. Similar-
ity is modeled using a rating-based (i.e., Vector Space Similarity and Pearson Correlation Coefficient) and
connection-based similarity measurements. Centrality metrics are quantified using degree, eigen-vector,
Katz and PageRank centralities. To validate the proposed trust model, an Epinions dataset is used and the
rating prediction scheme is implemented. Comprehensive analysis shows that the proposed trust model
based on similarity and centrality metrics provide better rating prediction rather than using binary trust
values. Based on the results, we find that the degree centrality is more effective compared to other cen-
tralities in rating prediction using the specific dataset. Also trust model based on the connection-based
similarity performs better compared to the Vector Space Similarity and Pearson Correlation Coefficient
similarities which are rating based. The experimental results on real-world dataset demonstrate the ef-
fectiveness of our proposed model in further improving the accuracy of rating prediction in social rec-
(model-based method) [41] , Similarity-based [11] , and Centrality-
based [11] . Trustwalker is a trust-based recommender system
which follows multiple random walks through the network start-
ing from a specific user and makes a rating prediction based on
the similar items observed throughout the walks. In SoRec, the
trust values are binary and the social recommendation is done us-
ing probabilistic matrix factorization. Item-based method is imple-
mented using Pearson Correlation as the item similarity. We also
tried two derivatives of our proposed model by using pure similar-
ity which ignores centrality parameter (similarity-based method)
to build the trust factor and the centrality-based method which
uses pure centrality when building the trust factor and ignores the
similarity between users.
Table 1 shows the values for the various measures for different
methods. 75% of rating information was used to estimate the re-
A. Davoudi, M. Chatterjee / Online Social Networks and Media 7 (2018) 1–11 9
Fig. 13. Errors for various training set sizes using degree centrality and connection-
based similarity.
Fig. 14. The probability distribution of error for rating estimation using binary trust
and the proposed trust model.
Fig. 15. Absolute error ratio for rating estimation using binary trust and the pro-
posed trust model.
Fig. 16. The quartile plot of actual versus estimated rating for the proposed trust
model.
Fig. 17. The quartile plot of actual versus estimated rating for the binary model.
Table 1
Comparison with other methods.
Methods RMSE Coverage (%) Precision F-measure
Our method 1.144 100 0.714 0.833139
TrustWalker 1.16 82 0.71 0.761045
SoRec 1.229 100 0.69275 0.818491
Item-based 1.23 77 0.6925 0.729197
Similarity-based 1.209 100 0.69775 0.82197
Centrality-based 1.217 100 0.69575 0.820581
m
m
a
s
7
t
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aining 25% of the user’s ratings for performance evaluation. Our
ethod outperforms all other methods in terms of RMSE, Cover-
ge, Precision and F-measure. For our method we use β = 0 . 4 , for
imilarity-Based β = 1 , and for centrality-Based, β = 0 .
. Conclusions
With emerging applications of social networks and considering
he role of social interactions in our daily life decisions, extract-
ng information from user’s social relationships is becoming a pop-
lar method for predicting user’s behavior. To consider and bal-
nce these factors, this paper proposes a social trust model that
10 A. Davoudi, M. Chatterjee / Online Social Networks and Media 7 (2018) 1–11
[
incorporates the preference similarity, user’s centrality, and social
relation in order to predict the rating for the social recommender
system. We capture the trust relationships between users consider-
ing users with similar profile and their importance. We argue that
users with more similarity would trust each other more; also users
with higher importance would be trusted more. Similarity is quan-
tified by using rating-based approaches and a connection-based
centralities. The importance of users is modeled by degree, eigen-
vector centrality, Katz and PageRank centralities. We define trust
as a linear combination of similarity and centrality using a weight-
ing parameter. The proposed framework is validated using real data
from Epinions. Our result indicates that the proposed trust model
produces better rating estimation in terms of the mean absolute
error (MAE), the root mean squared error (RMSE) and error dis-
tribution, compared to the traditional binary trust model which
is widely used in recommender systems. Trust enforced by degree
centrality shows better performance compared to other centrality
methods. The same conclusion is valid for connection-based sim-
ilarity compared to rating-based. The trust relationships are also
observed to be more dependent on the similarity rather than cen-
trality. The proposed framework can thus be effectively applied to
electronic retailers in promoting their products and services.
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I
P
H
s
c
s
Anahita Davoudi received the B.S. degrees in Computer
Engineering from the Amirkabir University of Technol-ogy (Tehran Polytechnic), Tehran, Iran, in 2008, and mas-
ter in Electrical Engineering from University of Texas Ar-
lington in 2012. She received the Ph.D. degree in Com-puter Science from the University of Central Florida (UCF),
Orlando, FL, USA. Her research interests include SocialRecommender Systems, Social Data Science and Compu-
tational Social Science.
Mainak Chatterjee is an Associate Professor in the de-
partment of Electrical Engineering and Computer Science,University of Central Florida, Orlando. He received the BSc
degree in physics (Hons.) from the University of Calcutta,
the ME degree in electrical communication engineeringfrom the Indian Institute of Science, Bangalore, and the
Ph.D. degree from the Department of Computer Scienceand Engineering from the University of Texas at Arlington.
His research interests include economic issues in wirelessnetworks, applied game theory, cognitive radio networks,
dynamic spectrum access, and mobile video delivery. He
has published over 200 conferences and journal papers.He got the Best Paper Awards in IEEE Globecom 2008 and
EEE PIMRC 2011. He is the recipient of the AFOSR sponsored Young Investigatorrogram (YIP) Award. He co-founded the ACM Workshop on Mobile Video (MoVid).
e serves on the editorial board of Elsevier Computer Communications and Perva-ive and Mobile Computing Journals. He has served as the TPC Co-Chair of a dozen
onferences. He also serves on the executive and technical program committee of