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

Jiasheng Zhang; Thai Le; Yiming Liao; Dongwon Lee

doi:10.1109/ICDMW.2019.00168

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 proceeding › Conference contribution

1 Scopus citations

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 language	English (US)
Title of host publication	Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
Editors	Panagiotis Papapetrou, Xueqi Cheng, Qing He
Publisher	IEEE Computer Society
Pages	1122-1125
Number of pages	4
ISBN (Electronic)	9781728146034
DOIs	https://doi.org/10.1109/ICDMW.2019.00168
State	Published - Nov 2019
Event	19th IEEE International Conference on Data Mining Workshops, ICDMW 2019 - Beijing, China Duration: Nov 8 2019 → Nov 11 2019

Publication series

Name	IEEE International Conference on Data Mining Workshops, ICDMW
Volume	2019-November
ISSN (Print)	2375-9232
ISSN (Electronic)	2375-9259

Conference

Conference	19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
Country/Territory	China
City	Beijing
Period	11/8/19 → 11/11/19

All Science Journal Classification (ASJC) codes

Computer Science Applications
Software

Access to Document

10.1109/ICDMW.2019.00168

Cite this

Zhang, J., Le, T., Liao, Y., & Lee, D. (2019). PATHFINDER: Graph-based itemset embedding for learning course recommendation and beyond. In P. Papapetrou, X. Cheng, & Q. He (Eds.), Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019 (pp. 1122-1125). Article 8955535 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2019-November). IEEE Computer Society. https://doi.org/10.1109/ICDMW.2019.00168

Zhang, Jiasheng ; Le, Thai ; Liao, Yiming et al. / PATHFINDER : Graph-based itemset embedding for learning course recommendation and beyond. Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019. editor / Panagiotis Papapetrou ; Xueqi Cheng ; Qing He. IEEE Computer Society, 2019. pp. 1122-1125 (IEEE International Conference on Data Mining Workshops, ICDMW).

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title = "PATHFINDER: Graph-based itemset embedding for learning course recommendation and beyond",

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",

author = "Jiasheng Zhang and Thai Le and Yiming Liao and Dongwon Lee",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019 ; Conference date: 08-11-2019 Through 11-11-2019",

year = "2019",

month = nov,

doi = "10.1109/ICDMW.2019.00168",

language = "English (US)",

series = "IEEE International Conference on Data Mining Workshops, ICDMW",

publisher = "IEEE Computer Society",

pages = "1122--1125",

editor = "Panagiotis Papapetrou and Xueqi Cheng and Qing He",

booktitle = "Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019",

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Zhang, J, Le, T, Liao, Y & Lee, D 2019, PATHFINDER: Graph-based itemset embedding for learning course recommendation and beyond. in P Papapetrou, X Cheng & Q He (eds), Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019., 8955535, IEEE International Conference on Data Mining Workshops, ICDMW, vol. 2019-November, IEEE Computer Society, pp. 1122-1125, 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019, Beijing, China, 11/8/19. https://doi.org/10.1109/ICDMW.2019.00168

PATHFINDER: Graph-based itemset embedding for learning course recommendation and beyond. / Zhang, Jiasheng; Le, Thai; Liao, Yiming et al.
Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019. ed. / Panagiotis Papapetrou; Xueqi Cheng; Qing He. IEEE Computer Society, 2019. p. 1122-1125 8955535 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2019-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - PATHFINDER

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AB - 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

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M3 - Conference contribution

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Zhang J, Le T, Liao Y, Lee D. PATHFINDER: Graph-based itemset embedding for learning course recommendation and beyond. In Papapetrou P, Cheng X, He Q, editors, Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019. IEEE Computer Society. 2019. p. 1122-1125. 8955535. (IEEE International Conference on Data Mining Workshops, ICDMW). doi: 10.1109/ICDMW.2019.00168

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

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