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Visualising module dependencies in academic recommendations

Authors:

Nina Hagemann,

Michael P. O'Mahony,

Barry SmythAuthors Info & Claims

IUI '19 Companion: Companion Proceedings of the 24th International Conference on Intelligent User Interfaces

Pages 77 - 78

https://doi.org/10.1145/3308557.3308701

Published: 16 March 2019 Publication History

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Abstract

Starting their academic career can be overwhelming for many young people. Students are often presented with a variety of options within their programmes of study and making appropriate and informed decisions can be a challenge. Compared to many other areas in our everyday life, recommender systems remain underused in the academic setting. In this part of our research we use non-negative matrix factorisation to identify dependencies between modules, visualise sequential recommendations, and bring structure and clarity into the academic module space.

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Nina Hagemann, Michael P. O'Mahony, and Barry Smyth. 2018. Module Advisor: Guiding Students with Recommendations. In International Conference on Intelligent Tutoring Systems. Springer, 319--325.

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Cited By

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Ahmadian Yazdi HSeyyed Mahdavi SAhmadian Yazdi H(2024)Dynamic educational recommender system based on Improved LSTM neural networkScientific Reports10.1038/s41598-024-54729-y14:1Online publication date: 22-Feb-2024
https://doi.org/10.1038/s41598-024-54729-y
Ahmadian HMahdavi Chabok SKheirabadi M(2023)A Recommender System for Scientific Resources Based on Recurrent Neural NetworksJournal of Information Systems and Telecommunication (JIST)10.61186/jist.39439.11.44.28211:44(282-293)Online publication date: 16-Dec-2023
https://doi.org/10.61186/jist.39439.11.44.282
Hagemann NO’Mahony MSmyth B(2023)Visual Module Exploration: A Live-User EvaluationKI - Künstliche Intelligenz10.1007/s13218-023-00800-137:2-4(213-225)Online publication date: 1-Mar-2023
https://doi.org/10.1007/s13218-023-00800-1
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Index Terms

Visualising module dependencies in academic recommendations
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Recommender systems aim to identify items that a user may like. In this paper, we discuss a three-way decision approach which provides a more meaningful way to recommend items to a user. Besides recommended items and not recommended items, the proposed ...
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Memory-based collaborative filtering (CF) techniques have been widely implemented for predicting ratings to unseen items by aggregating ratings of similar users or items in recommender systems (RS). Usually, sufficient ratings from similar users or ...

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Published In

IUI '19 Companion: Companion Proceedings of the 24th International Conference on Intelligent User Interfaces

March 2019

173 pages

ISBN:9781450366731

DOI:10.1145/3308557

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 16 March 2019

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IUI '19: 24th International Conference on Intelligent User Interfaces

March 16 - 20, 2019

California, Marina del Ray

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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Ahmadian Yazdi HSeyyed Mahdavi SAhmadian Yazdi H(2024)Dynamic educational recommender system based on Improved LSTM neural networkScientific Reports10.1038/s41598-024-54729-y14:1Online publication date: 22-Feb-2024
https://doi.org/10.1038/s41598-024-54729-y
Ahmadian HMahdavi Chabok SKheirabadi M(2023)A Recommender System for Scientific Resources Based on Recurrent Neural NetworksJournal of Information Systems and Telecommunication (JIST)10.61186/jist.39439.11.44.28211:44(282-293)Online publication date: 16-Dec-2023
https://doi.org/10.61186/jist.39439.11.44.282
Hagemann NO’Mahony MSmyth B(2023)Visual Module Exploration: A Live-User EvaluationKI - Künstliche Intelligenz10.1007/s13218-023-00800-137:2-4(213-225)Online publication date: 1-Mar-2023
https://doi.org/10.1007/s13218-023-00800-1
Hagemann NO’Mahony MSmyth B(2021)A Live-User Evaluation of a Visual Module Recommender and Advisory System for Undergraduate StudentsArtificial Intelligence XXXVIII10.1007/978-3-030-91100-3_24(299-312)Online publication date: 14-Dec-2021
https://dl.acm.org/doi/10.1007/978-3-030-91100-3_24
Ajanovski VGiannakos MSindre GLuxton-Reilly ADivitini M(2020)Tools for Analysis of Curricula Evolution Across Computer Science Curriculum GuidelinesProceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education10.1145/3341525.3393995(539-540)Online publication date: 15-Jun-2020
https://dl.acm.org/doi/10.1145/3341525.3393995
Ajanovski VIhantola PFalkner N(2019)Body of Knowledge ExplorerProceedings of the 19th Koli Calling International Conference on Computing Education Research10.1145/3364510.3364531(1-5)Online publication date: 21-Nov-2019
https://dl.acm.org/doi/10.1145/3364510.3364531

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Hybrid group recommendations for a travel service

Cost-sensitive three-way recommendations by learning pair-wise preferences

A fuzzy Bayesian approach to integrate user and item based collaborating filtering for enhanced recommendations

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Recommendations

Hybrid group recommendations for a travel service

Cost-sensitive three-way recommendations by learning pair-wise preferences

A fuzzy Bayesian approach to integrate user and item based collaborating filtering for enhanced recommendations

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