Learning Peer Recommendation Based on Weighted Heterogeneous Information Networks on Online Learning Platforms

Abstract

With the development of online education, there is an urgent need to solve the problem of the low completion rate of online learning courses. Although learning peer recommendation can effectively address this problem, prior studies of learning peer-recommendation methods extract only a portion of the interaction information and fail to take into account the heterogeneity of the various types of objects (e.g., students, teachers, videos, exercises, and knowledge points). To better motivate students to complete online learning courses, we propose a novel method to recommend learning peers based on a weighted heterogeneous information network. First, we integrate the above different objects, various relationships between objects, and the attribute values to links in a weighted heterogeneous information network. Second, we propose a method for automatically generating all meaningful weighted meta-paths to extract and identify meaningful meta-paths. Finally, we use the Bayesian Personalized Ranking (BPR) optimization framework to discover the personalized weights of target students on different meaningful weighted meta-paths. We conducted experiments using three real datasets, and the experimental results demonstrate the effectiveness and interpretability of the proposed method.

Research on Joint Recommendation Algorithm for Knowledge Concepts and Learning Partners Based on Improved Multi-Gate Mixture-of-Experts

“…Liu et al [13] proposed an adaptive clustering and community recommendation algorithm based on incremental tensors, which uses tensor modelling to preserve the integrity of the data, and in this way recommends appropriate learning partners to students in various contexts. In order to alleviate learners' loneliness during online learning, Shou et al [19] proposed a learning partner recommendation model based on a weighted heterogeneous information network, which extracts more complete interaction information by automatically generating all meaningful meta-paths to reveal students' unique preferences. The above studies have compensated for the lack of information completeness in the learning partner recommendation model to a certain extent; however, the importance of accurately modelling learners from multiple dimensions of human-computer interaction information, student-student interaction information, and students' interest characteristics cannot be ignored.…”

“…MF [30] The method maps user-item interactions into a low-dimensional potential space and uses the inner product of the user and the item in space to model user interactions metapath2vec [28] A representation learning model based on random walks and skip-grams to generate paths in heterogeneous networks using random walks and learns node representations using skip-grams LPRWHIN [19] The model proposes a method for automatically extracting and identifying meaningful meta-paths and uses the BPR optimisation framework to learn the importance of different meta-paths of students in order to recommend learning partners…”

MDBF: Meta-Path-Based Depth and Breadth Feature Fusion for Recommendation in Heterogeneous Network

“…Usually, the evaluation metrics of recommendation algorithm are precision, recall, and F1 score [30].…”