Recommendation in Collaborative E-Learning by Using Linked Open Data and Ant Colony Optimization

摘要

Social tagging activities allow the wide set of web users, especially learners, to add free annotations on educational resources to express their interests and automatically generate folksonomies. Folksonomies have been involved in a lot of recommendations approaches. Recently, supported by semantic web technologies, the Linked Open Data (LOD) allow to set up links between entities in the web to join information in a single global data space. This paper demonstrates how structured content accessible via LOD can be leveraged to support educational resources recommender in folksonomies and overcome the limited capabilities to analyze resources information. Another limitation of resources recommendation is the content overspecialization conducting in the incapacity to recommend relevant resources diverse from the ones that learner previously knows. To address these issues, we proposed to take advantage of the richness of the open and linked data graph of DBpedia and Ant Colony Optimization (ACO) to learn users' behavior. The basic idea is to iteratively explore the RDF data graph to produce relevant and diverse recommendations as an alternative of going through the tedious phase of calculating similarity to attain the same goal. Using ant colony optimization, our system performs a search for the appropriate paths in the LOD graph and selects the best neighbors of an active learner to provide improved recommendations. In this paper, we show that ACO also in the problem of recommendation of novel diverse educational resources by exploring LOD is able to deliver good solutions.