Improving recentness of the ICT book recommendation using an adaptive rules-based recommender system
Mochammad Husni, Tubagus Mohammad Akhriza, Sarifuddin Madenda, Eri Prasetyo Wibowo · International Journal of Computer Applications in Technology · 2022
The traditional library book Recommendation System (RS) has limitations where all recommendations only refer to internal book borrowing transactions; while the development of science and technology, especially in the field of ICT, has exceeded the theme of the recommended books. As a result, the recentness of the recommendations is questionable. As a solution, this article proposes an adaptive-rules-based book RS while at the same time introducing a dimension to measure the quality of recommendations namely recentness. It measures how up-to-date the recommended book theme is, against a set of trending themes extracted from external publications. An experiment was conducted to measure the book recommendations generated by the new RS in a library, compared to a collection of recent publications in the IEEE Xplore database. At first, the recentness of the recommendation was only around 23.5-57.1%, but it was successfully increased from 47.6% to 76.2% by the proposed RS.