Year: 2025
Type: article
Abstract: Group recommendation services are an important way to foster collaborative learning among users and enhance learning effectiveness in online education. However, existing group recommendation methods fail to consider the diverse learning behaviors and collaborative relationships among members (users), which brings challenges in capturing group consensus. Group consensus is key to improving the accuracy of recommendations and capturing the real needs of members. In response to this issue, we propose a Multi-behavior Enhanced Group Recommendation (MEGR) model for smart educational services, which captures the multi-behavior features and collaborative relationships of members to model group consensus. Specifically, we design specialized interaction subgraphs to capture multi-behavioral features for learning members’ preferences. Additionally, we model the independence of each behavior to avoid over-relying on any single one. Subsequently, we model intra-group collaboration and potential dependencies by projecting the member’s preference representations into multiple different spaces, which are used to capture group consensus. The experimental results on the two real-world educational datasets, MOOCCube and EdNet, show that our MEGR improves group recommendation performance by an average of 0.97% and 2.10%, respectively, compared to the best baseline methods.
Source: Discover Computing
Authors: Duiqiang Wang, Qing Yang, Xianglin Wu, Zezheng Wu, Jingwei Zhang, Songxing He
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $1,520
Awards: G1673392488, G6911281488, G7059875469
OpenAlex ID: W4409783428