TY - GEN
T1 - Data Extraction and Integration for Scholar Recommendation System
AU - Chakraborty, Jaydeep
AU - Thopugunta, Gurusrikar
AU - Bansal, Srividya
PY - 2018/4/9
Y1 - 2018/4/9
N2 - Recommendation systems have been an integral part of massive open online courses (MOOCs). With a large amount of availability of data and resources, recommending scholars and professors through general reviews and academic advisor applications has become a tiresome job. Finding professors and scholars relevant to a student's area of interest involves a combination of multiple factors like field of study, depth of research area, research background of professors, ongoing research opportunities, etc. As recommending scholars and professors deals with so many different factors, it is very complex and unreliable when done manually. In this paper, we present a content-based mining approach to go through all relevant resources, extract required information, and use it to recommend a list of scholars based on student's area of interest. For our experimental model, we gathered information about a number of professors at our institution from various web resources such as IEEE, Springer, ACM, Sciencedirect, arxiv and department website. We use topic modeling and clustering algorithms in our content-based mining approach. We present a comparative analysis of the following topic model algorithms: latent dirichlet allocation (LDA), hierarchical dirichlet process(HDP), latent semantic analysis (LSA) and clustering techniques: k-means and hierarchical clustering in determining the most accurate recommendation list of professors or scholars.
AB - Recommendation systems have been an integral part of massive open online courses (MOOCs). With a large amount of availability of data and resources, recommending scholars and professors through general reviews and academic advisor applications has become a tiresome job. Finding professors and scholars relevant to a student's area of interest involves a combination of multiple factors like field of study, depth of research area, research background of professors, ongoing research opportunities, etc. As recommending scholars and professors deals with so many different factors, it is very complex and unreliable when done manually. In this paper, we present a content-based mining approach to go through all relevant resources, extract required information, and use it to recommend a list of scholars based on student's area of interest. For our experimental model, we gathered information about a number of professors at our institution from various web resources such as IEEE, Springer, ACM, Sciencedirect, arxiv and department website. We use topic modeling and clustering algorithms in our content-based mining approach. We present a comparative analysis of the following topic model algorithms: latent dirichlet allocation (LDA), hierarchical dirichlet process(HDP), latent semantic analysis (LSA) and clustering techniques: k-means and hierarchical clustering in determining the most accurate recommendation list of professors or scholars.
KW - Clustering
KW - Hierarchical Dirichlet Process
KW - Hierarchical clustering
KW - Latent Dirichlet Allocation
KW - Latent Semantic Analysis
KW - Recommendation System
KW - Topic modeling
KW - k-means clustering
UR - http://www.scopus.com/inward/record.url?scp=85048399510&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048399510&partnerID=8YFLogxK
U2 - 10.1109/ICSC.2018.00079
DO - 10.1109/ICSC.2018.00079
M3 - Conference contribution
AN - SCOPUS:85048399510
T3 - Proceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018
SP - 397
EP - 402
BT - Proceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Semantic Computing, ICSC 2018
Y2 - 31 January 2018 through 2 February 2018
ER -