Abstract:
Finding relevant information on the Internet has become a major issue today due to information
overload. Recommender systems are the best solution to this problem. Recommender system is
algorithms aimed at suggesting items of interest to users.
There are many techniques proposed in recommender systems. Collaborative filtering is a
common method widely used in recommender systems. However, collaborative filtering
techniques still have some problems: cold start. In this study, we propose a book recommender
system that uses collaborative filtering with demographic data. We applied the user’s age,
gender, and occupation to find similarities between users. We cluster users by using K-means
clustering. Then the recommender system suggests books that were previously interested by
users in the group to new users. Extensive experiments are conducted on user ratings and a
dataset of books that include users to evaluate the effectiveness of the proposed model. The
performance of the proposed model was evaluated using the precision, recall, and F1 Score
metrics that support the effectiveness of the proposed model. The proposed model performance
is done by two ways of an experiment. The performance of the proposed model performs around
68.05% of Precision, 42.46% of Recall and 52.1% of the average of F1_score for the experiment
based on individual user similarity in the system. And also performs around 93.75% of precision,
40.25% of recall and 56.31% F1-score for the similarity of users based on the similarity of users
within the same cluster which is better than the first experiment.
