Indiana University-Purdue University Indianapolis (IUPUI) / The dramatic improvement in information and communication technology (ICT) has
made an evolution in learning management systems (LMS). The rapid growth in LMSs has
caused users to demand more advanced, automated, and intelligent services. CourseNet working is a next-generation LMS adopting machine learning to add personalization, gamifi cation, and more dynamics to the system. This work tries to come up with two recommender
systems that can help improve CourseNetworking services.
The first one is a social recommender system helping CourseNetworking to track user
interests and give more relevant recommendations. Recently, graph neural network (GNN)
techniques have been employed in social recommender systems due to their high success in
graph representation learning, including social network graphs. Despite the rapid advances in
recommender systems performance, dealing with the dynamic property of the social network
data is one of the key challenges that is remained to be addressed. In this research, a novel
method is presented that provides social recommendations by incorporating the dynamic
property of social network data in a heterogeneous graph by supplementing the graph with
time span nodes that are used to define users long-term and short-term preferences over
time.
The second service that is proposed to add to Rumi services is a hashtag recommendation
system that can help users label their posts quickly resulting in improved searchability of
content. In recent years, several hashtag recommendation methods are proposed and de veloped to speed up processing of the texts and quickly find out the critical phrases. The
methods use different approaches and techniques to obtain critical information from a large
amount of data. This work investigates the efficiency of unsupervised keyword extraction
methods for hashtag recommendation and recommends the one with the best performance to use in a hashtag recommender system.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/33370 |
| Date | 05 1900 |
| Creators | Mohammad Jafari, Behafarid |
| Contributors | King, Brian, Luo, Xiao, Jafari, Ali, Zhang, Qingxue |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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