A blog article recommendation generating mechanism using an SBACPSO algorithm

In recent years blog-assisted learning has been used widely in higher education for improving writing and collaboratively sharing work online. However, methods for gathering useful information to be used as auxiliary-learning materials from the multitude of blog articles in the blogosphere has been seldom investigated. This paper proposes an individualized blog article recommendation mechanism to provide quality blog articles that accord with users’ learning topics. First, an IR-based technique was applied to extract and score index terms. The top three index terms were then entered into Google’s blog search engine to find the raw recommended blog articles. To avoid the situation where frequent topic-changing leads to a deficiency of article data on a specific learning topic, a forgetting rate was employed to simulate the phenomenon of changing learning topics. Subsequently, an extended Serial Blog Article Composition Particle Swarm Optimization (SBACPSO) algorithm was employed to provide optimal recommended materials to users. We evaluated the system’s performance to find the appropriate article population size. Finally, user satisfaction regarding both the system and recommended content were gauged to find the system’s limitations and possible improvements. This study is of importance in that it provides users with dynamic blog article recommendation, improved online information discovery skills and opportunities to socialize with other bloggers.