Ebrahim Bagheri
Software / Data Engineering
Like-minded user communities in social networks are usually identified by either considering explicit structural connections between users (link analysis) or users' topics of interest expressed in their posted contents (content analysis). The effective identification of user communities on social networks enables applications such as news recommendation, user prediction and community selection, just to name a few.
The objective of this project will be to identify user communities based on the similarity of their future unobserved topics of interest/
Students will learn about cutting edge techniques in Natural Language Processing and Information Retrieval.
The work will build upon and improve the work presented in https://link.springer.com/article/10.1007/s10791-018-9337-y
The group will be responsible to replicate the work presented in the baseline paper, work with the FLC to further enhance the algorithm, implement the enhancements, perform experiments and report on the findings. All students will be responsible for understanding the baseline paper and participating in group brainstorming sessions.
Preparation of the dataset for the experiments and assist with the implementation of the enhancements to the baseline.
Implementation and Replication of the baseline code.
Leading the implementation of the enhanced code as well as running the experiments.
N/A
EB05: Inferring social communities using unobserved future interests | Ebrahim Bagheri | Not yet submitted at No time