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1. C-DeepTrust: A Context
Trust Prediction Model in Online Social
Networks
Abstract
Trust prediction provides valuable support for decision making, information
dissemination, and product promotion in online social networks. As a complex
concept in the social network community, trust relationships among people
can be established virtually based on: 1) their interaction behaviors, e.g., the
ratings and comments that they provided; 2) the contextual information
associated with their interactions, e
temporal features of interactions and the time periods when the trust
relationships hold. Most of the existing works only focus on some aspects of
trust, and there is not a comprehensive study of user trust de
considers and incorporates 1)
a context-aware deep trust prediction model C
DeepTrust: A Context-Aware Deep
Trust Prediction Model in Online Social
Trust prediction provides valuable support for decision making, information
dissemination, and product promotion in online social networks. As a complex
in the social network community, trust relationships among people
can be established virtually based on: 1) their interaction behaviors, e.g., the
ratings and comments that they provided; 2) the contextual information
associated with their interactions, e.g., location and culture; and 3) the relative
temporal features of interactions and the time periods when the trust
relationships hold. Most of the existing works only focus on some aspects of
trust, and there is not a comprehensive study of user trust development that
considers and incorporates 1)–3) in trust prediction. In this article, we propose
aware deep trust prediction model C-DeepTrust to fill this gap. First,
Aware Deep
Trust Prediction Model in Online Social
Trust prediction provides valuable support for decision making, information
dissemination, and product promotion in online social networks. As a complex
in the social network community, trust relationships among people
can be established virtually based on: 1) their interaction behaviors, e.g., the
ratings and comments that they provided; 2) the contextual information
.g., location and culture; and 3) the relative
temporal features of interactions and the time periods when the trust
relationships hold. Most of the existing works only focus on some aspects of
velopment that
3) in trust prediction. In this article, we propose
DeepTrust to fill this gap. First,

2. we conduct user feature modeling to obtain the user’s static and dynamic
preference features in each context. Static user preference features are
obtained from all the ratings and reviews that a user provided, while dynamic
user preference features are obtained from the items rated/reviewed by the
user in time series. The obtained context-aware user features are then
combined and fed into the multilayer projection structure to further mine the
context-aware latent features. Finally, the context-aware trust relationships
between users are calculated by their context-aware feature vector cosine
similarities according to the social homophily theory, which shows a pervasive
property of social networks that trust relationships are more likely to be
developed among similar people. Extensive experiments conducted on two
real-world datasets show the superior performance of our approach compared
with the representative baseline methods.