Survey on Deep Learning-based Personalized Learning Resource Recommendation

Abstract

With the deep integration of information technology and education,novel online education,as a pivotal component of smart education,provides learners with convenient online e-learning platforms and rich learning resources.However,the rapid development of online education modes has also led to significant challenges such as “knowledge overload” and “knowledge dis-orientation”,which severely limits learners' educational gains and efficiency.In recent years,learning resource recommendation,as a key technology for information filtering,aims to analyze learners' historical behaviors,capture their underlying learning needs,and ultimately achieve personalized learning resource recommendation services.Accurate personalized learning resource recommendations can effectively address the challenges of “knowledge overload” and “knowledge disorientation” in online education,making it an indispensable core function in major online e-learning platforms.In addition,with the continuous advancement of deep learning technologies,research on deep learning-based personalized learning resource recommendation has become a focal area of interdisciplinary study in computer science and education.Therefore,this paper systematically analyzes existing research from multiple dimensions and levels,guided by the research questions of “how to achieve personalized learning resource recommendations” and “how to evaluate recommendation results”.Specifically,the paper firstly categorizes and summarizes the per-sonalized recommendation process of learning resources from five dimensions,including characteristics,recommendation objectives,deep learning technologies,integration methods of side information,and recommendation patterns,to answer the question of how to realize personalized recommendation of learning resources.Second,this paper inductively compares the evaluation process of recommendation results from three aspects,including datasets,evaluation metrics,and experimental methods,and provides unified download links for all open-source datasets,to answer the question of how to evaluate the recommendation results.Finally,this paper explores future research trends of learning resource recommendation from two perspectives:overcoming the inherent limitations of current recommendation methods as well as integrating and utilizing external emerging technologies.