AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments

TL;DR

This paper introduces AFEL-REC, a scalable recommender system designed for social learning environments that leverages social data like tags to improve recommendation accuracy and coverage.

Contribution

The paper presents a novel scalable architecture for AFEL-REC that effectively integrates diverse social data types for real-time learning resource recommendations.

Findings

Using social tags improves recommendation accuracy.

Social data increases coverage of recommendations.

AFEL-REC performs well in real-time scenarios.

Abstract

In this paper, we present preliminary results of AFEL-REC, a recommender system for social learning environments. AFEL-REC is build upon a scalable software architecture to provide recommendations of learning resources in near real-time. Furthermore, AFEL-REC can cope with any kind of data that is present in social learning environments such as resource metadata, user interactions or social tags. We provide a preliminary evaluation of three recommendation use cases implemented in AFEL-REC and we find that utilizing social data in form of tags is helpful for not only improving recommendation accuracy but also coverage. This paper should be valuable for both researchers and practitioners interested in providing resource recommendations in social learning environments.