An Enhanced Item-Based Collaborative Filtering Approach for Book Recommender System Design

Abstract

The massive amounts of web-sourced data have made accessibility of precise (tailored) information for end-users a challenging task. If this personalized information-filtering technology is possibly thought to get automated at the machine level, this necessitates the design of an appropriate machine-assisted recommender system that suffices both system and end-user requirements. This paper attempts to make use of an innovative hybrid approach to build a prototype of a machine-assisted recommender that can be used as a tool in the physical library of universities, organizations, and institutions. In this manuscript, an Enhanced Item-Based Collaborative Filtering Approach for Book Recommender System Design is proposed to predict the popular books based on the transactions found in the issue/return transaction database. Demographic attributes of books are used for the precise calculation of item-item similarity with cosine/correlation coefficient by discovering all significant associations rules in the formulated item set and it provides the recommendations. The proposed algorithm’s performances are calculated based on accuracy, precision, and recall.