Title: DEVELOPING SOME RECOMMENDATION METHODS TO SUPPORT ACADEMIC INFORMATION SEARCH BASED ON SOCIAL NETWORK ANALYSIS APPROACH
[Ph.D. thesis in Computer Science]
Author: Huynh Ngoc Tin
Summary:
Searching for scientific information to carry out research-related tasks is a frequent and essential need for researchers, especially for young researchers who lack experience in finding and identifying relevant information. Meanwhile, experienced researchers face information overload. To make it easier for them to access useful academic information, recommendation systems in the academic field have been a solution of interest in recent years. Common academic information recommendation problems include recommending papers, collaborations, submitting papers, etc. The traditional approaches for recommendation systems, such as Demographic Filtering, Content-Based Filtering, Collaborative Filtering, and Hybrid approaches, face challenges such as big data, lack of standardized benchmark data for experimental evaluation, low accuracy, cold-start issues, and a lack of suitable methods for evaluating recommendation quality. Emerging approaches to developing new recommendation system methods include social network analysis, context information exploitation, and hybrid methods [23]. In reality, people's interests and decisions are often influenced by those in their relationships. Researchers often need advice from friends, colleagues, and mentors to make important decisions related to research tasks. Therefore, the thesis adopts a social network analysis approach (considering time factors) combined with some other information to address the limitations of some common methods and applies them to recommend academic information.
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