Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks

by   Jibing Gong, et al.

Massive open online courses (MOOCs), which provide a large-scale interactive participation and open access via the web, are becoming a modish way for online and distance education. To help users have a better study experience, many MOOC platforms have provided the services of recommending courses to users. However, we argue that directly recommending a course to users will ignore the expertise levels of different users. To fill this gap, this paper studies the problem of concept recommendation in a more fine-grained view. We propose a novel Heterogeneous Information Networks based Concept Recommender with Reinforcement Learning (HinCRec-RL) incorporated for concept recommendation in MOOCs. Specifically, we first formulate the concept recommendation in MOOCs as a reinforcement learning problem to better model the dynamic interaction among users and knowledge concepts. In addition, to mitigate the data sparsity issue which also exists in many other recommendation tasks, we consider a heterogeneous information network (HIN) among users, courses, videos and concepts, to better learn the semantic representation of users. In particular, we use the meta-paths on HIN to guide the propagation of users' preferences and propose a heterogeneous graph attention network to represent the meta-paths. To validate the effectiveness of our proposed approach, we conduct comprehensive experiments on a real-world dataset from XuetangX, a popular MOOC platform from China. The promising results show that our proposed approach can outperform other baselines.


Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View

Massive open online courses are becoming a modish way for education, whi...

Reinforced Meta-path Selection for Recommendation on Heterogeneous Information Networks

Heterogeneous Information Networks (HINs) capture complex relations amon...

Course Concept Expansion in MOOCs with External Knowledge and Interactive Game

As Massive Open Online Courses (MOOCs) become increasingly popular, it i...

Modeling User Repeat Consumption Behavior for Online Novel Recommendation

Given a user's historical interaction sequence, online novel recommendat...

RRCN: A Reinforced Random Convolutional Network based Reciprocal Recommendation Approach for Online Dating

Recently, the reciprocal recommendation, especially for online dating ap...

SuperCone: Modeling Heterogeneous Experts with Concept Meta-learning for Unified Predictive Segments System

Understanding users through predicative segments play an essential role ...

Query-based Interactive Recommendation by Meta-Path and Adapted Attention-GRU

Recently, interactive recommender systems are becoming increasingly popu...

Please sign up or login with your details

Forgot password? Click here to reset