Abstract:
Recommender systems are fundamental solutions to information overload on the web due
to the availability of multitude information sources. This system filters and presents
relevant information to customer and/ or online users, a small subset of items that she/he
is most likely to be interested in. The application of Recommendation schemes ranges
from entertainment application (i.e., movies, music) to online newspapers information
sources to recommend for the users based on their preferences. News recommendation
scheme utilize features of the news itself and information about users to suggest and
recommend relevant news items to the users towards the interest they have.
However, the effectiveness of existing news recommendation scheme is limited during a
scenario where information about a user or information about set of users in the system is
unavailable. This leads to the occurrence of new user cold start problem. Therefore, the
main objective of the study is: designing news recommender system using hybrid
approaches to address new user cold start problem to ease and suggest more related news
article for new users.
In order to achieve the aforementioned objective, User Demographic Information (Data) with
Hybrid Recommendation system is proposed. This hybrid recommender scheme combines
content-based and collaborative filtering approach with demographic filtering.
To evaluate the effectiveness of the proposed model, an extensive experiment is conducted
using news articles dataset with user rating value and user demographic data. The
performance of the proposed model evaluated using precision, Recall and F1-Score
metrics which support the effectiveness of the proposed model. The proposed model
performance is done by two ways of experiment. So, the performance of proposed model
performs around 68.05% of Precision, 42.46% of Recall and 52.1% of 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.
