Abstract:
The increased variety of content in e-commerce and entertainment services creates a
new research gap in the field of recommendation systems. Traditional recommendation
systems are not capable of dealing with a variety of data because the nature of the data
is unstructured and heterogeneous. In the past decade, due to the exponential growth of
multimedia content in the video game industry, researchers investigated the importance
of personalized video game recommendation techniques. It has been noticed that the
previous methods have not investigated the importance of visual and textual semantic
content for video game recommendations. The researchers have contributed to the field
of multimedia item recommendations using low-level and aesthetic visual features.
However, they suffer from less feature utilization and significant performance decay
because they usually only consider traditional features such as product information and
user information. One potential approach to alleviating such issues is to utilize item
semantic information along with hybrid filtering techniques of recommendation
systems. This study proposed a novel method named deep hybrid semantic multimedia
recommendation systems (MMRSs) to deal with textual and visual semantic features
for multimedia recommendation systems. The primary contribution of the presented
work is to show improvement in the accuracy of the multimedia recommendations
system by using both textual and visual semantic content. In the presented thesis, a
semantic-based video game recommendation system utilizing deep learning methods
a
for visual and textual content learning and user profile learning has been proposed. The
proposed approach employs hybrid techniques to expand users’ profiles. The user
profile expansion is based on the semantic content of games’ visual and textual
modalities. We have used two datasets of video games which are Goggle-Play and
Amazon for evaluation purposes. The evaluation results have shown that the proposed
pproach accuracy have been improved by up to 94% for Google-Play dataset and for
the Amazon datasets we have received 78% as compared to the other state-of-the-art
methods
