Traditional Collaborative filtering algorithm works by using only the past experience of a
user. To overcome the limitations of the traditional collaborative algorithm, an item based …

Nowadays the explosion of information sources has shaped how library users access
information and provide feedback on their preferences. Therefore, faced with this explosion …

Recommendation systems are gaining popularity with the proliferation of the Internet of
People (IoP). The popularity and use of online social networks facilitate integrating these …

Collaborative filtering (CF) is one of the most practical approaches on recommendation
systems by predicting users' preferences for items based on the user-item interaction …

To the best of our knowledge, few studies have focused on the inconsistency between user
ratings and reviews as well as natural noise management in recommender systems (RSs) …

In this study, we focus on the problem of information expiration when using the traditional
collaborative filtering algorithm and propose a new collaborative filtering algorithm by …

Recommender Systems have proven to be of great aid in dealing with the issue of
Information Overload by improving the user experience through quality recommendations. In …

Contrastive learning (CL) has recently catalyzed a productive avenue of research for
recommendation. The efficacy of most CL methods for recommendation may hinge on their …

The quality of user-generated reviews is significant for users to understand recommendation
results and make online purchasing decisions correctly. However, the reliability of a review …

User basic data (eg user gender, user age and user ID, etc.) and item basic data (eg item
name, item category, etc.) are important side data that can be used to enhance the …