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A Hybrid Recommender System based on DeepCF and Wide Linear Model for K12 Online Education

Published: 17 March 2022 Publication History

Abstract

Compared to traditional classroom education, online education has the advantage of personalized teaching and is able to significantly improve learning efficiency and outcomes. K12 online education refers to online education serving students across 12 grade levels, from primary school to high school. As there are a plethora of courses offered on online education platforms, how to recommend proper courses for students is a serious challenge. In order to resolve this problem, this study proposes a hybrid recommendation model that combines the deep collaborative filtering (DeepCF) model and the wide linear model. Together, in the hybrid model, these models can integrate course features, student features, and side information. The DeepCF model learns low-dimension latent representations for both courses and students and integrates them into matrix factorization to predict ratings. The wide linear model uses a factorization machine to design and select features automatically. The hybrid model can achieve good performance and alleviate the problem of sparse features and cold start. Experimental results demonstrate that compared with the collaborative filter model, the hybrid model achieved a significant improvement with a 12.7% relative increase in AUC metric.

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Cited By

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  • (2024)Investigating the Use of Deep Learning and Implicit Feedback in K12 Educational Recommender SystemsIEEE Transactions on Learning Technologies10.1109/TLT.2023.327342217(112-123)Online publication date: 1-Jan-2024
  • (2024)A Systematic Literature Review on AI-Based Recommendation Systems and Their Ethical ConsiderationsIEEE Access10.1109/ACCESS.2024.345105412(121223-121241)Online publication date: 2024

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                AICCC '21: Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference
                December 2021
                246 pages
                ISBN:9781450384162
                DOI:10.1145/3508259
                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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                Published: 17 March 2022

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                • (2024)Investigating the Use of Deep Learning and Implicit Feedback in K12 Educational Recommender SystemsIEEE Transactions on Learning Technologies10.1109/TLT.2023.327342217(112-123)Online publication date: 1-Jan-2024
                • (2024)A Systematic Literature Review on AI-Based Recommendation Systems and Their Ethical ConsiderationsIEEE Access10.1109/ACCESS.2024.345105412(121223-121241)Online publication date: 2024

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