PATHFINDER: Graph-based itemset embedding for learning course recommendation and beyond

Jiasheng Zhang, Thai Le, Yiming Liao, Dongwon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We demonstrate a tool, named as PATHFINDER, that captures and visualizes rich latent relationships among courses as a graph, mines students' past course performance data, and recommends pathways or top-k courses most helpful to a given student, using an itemset embedding based learning model. With dedicated design for the asymmetric, non-additive and non-negative challenges specific to the problem, our model for helpfulness achieves the best performance among competing models. We demonstrate the visualization of four course relationships (e.g., mandatory, prerequisite, helpful, and top-k) in a graph. The PATHFINDER demo is publicly available at: http://140.82.60.177:8000

Original languageEnglish (US)
Title of host publicationProceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
EditorsPanagiotis Papapetrou, Xueqi Cheng, Qing He
PublisherIEEE Computer Society
Pages1122-1125
Number of pages4
ISBN (Electronic)9781728146034
DOIs
StatePublished - Nov 2019
Event19th IEEE International Conference on Data Mining Workshops, ICDMW 2019 - Beijing, China
Duration: Nov 8 2019Nov 11 2019

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2019-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
CountryChina
CityBeijing
Period11/8/1911/11/19

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software

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