Improving book lending service in UTM Library using apriori rule-mining technique

Abstract

The continuous advancement in technology has redefined the nature and strategy of service provision to customers in all works of life. Academic libraries in institution of higher learning are not exempted from this struggle for relevance to provide improved services to their demanding customers. In order to protect its huge investment in library collections especially books and maintain patronage from students in the university, UTM Library, Perpustakaan Sultanah Zanariah must improve its book lending services to counter the tough competition from rival media and service providers in the same business realm. This research seeks to recommend the best books to UTM students when they put PSZ’s book lending service to use by developing a book recommender system which uses an association rule mining technique called Apriori algorithm. An added feature to improve the recommendation’s from this application is ensuring recommended books are highly rated whereby all ratings are provided by trustworthy and popular book selling and reading sites such as Amazon and Goodreads. The result from application testing showed wide acceptance and emphases by students to integrate this feature in the existing library portal as majority believed this integration will aid an improvement in their knowledge as they borrow the best books with higher ratings while also enjoying a better and richer search experience.