Due to the digital era, there is a flood of information available online .
Recommendation systems can be utilized to improve user experience. Authors in this
chapter proposed to develop a hybrid book recommendation system using
Collaborative Filtering (CF) and Content-based Filtering (CBF) techniques. The
content filtering method identifies the features of the object, and then, based on those
features, a decision can be made. Collaborative filtering works based on user
interaction patterns. The proposed method overcomes the drawbacks of data sparsity in
collaborative filtering and the cold-start problem in content-based systems. To
overcome these issues, the dataset is pre-processed to find the popular books, ratings,
and a list of active users. Authors have applied k-Nearest Neighbors (k-NN) for feature
and metric selection so that the recommendation engine can work properly. The
performance of the proposed model is measured in terms of performance metrics, and
the outcome has proved that it is a precise and accurate recommendations model with
high recall, precision, F1-Score, and accuracy metrics.
Keywords: Book recommendation systems, Collaborative filtering, Contentbased filtering, K-nearest neighbours.
