Purchase prediction can help e-commerce planners plan their stock and personalised offers.
Word2Vec is a well-known method to explore word relations in sentences for sentiment …

One of the most important concerns about recommender systems is the filter bubble
phenomenon. While recommender systems try to personalize information, they tighten the …

Recommender Systems are a predictive model for personalized suggestions utilizing past
interactions and experiences. Collaborative filtering is the most popular and successful …

Abstract Information overload is a challenge in e-commerce platforms. E-shoppers may have
difficulty selecting the best product from the available options. Recommender systems (RS) …

In this paper, we focus on the problem of rank-sensitive proportionality preservation when
aggregating outputs of multiple recommender systems in dynamic recommendation …

Cold-start problem is one of the main challenges for the recommender systems. There are
many methods developed for traditional recommender systems to alleviate the drawback of …

There are certain challenges in news recommender systems that arise due to changing
users' preferences over dynamically generated news articles. It is important to expose users …

Due to the increase in the importance of giving real-time recommendation to e-commerce
users, session-based recommender systems become more popular. Session-based …

Over the past two decades, there has been a rapid increase in the number of online sales.
This has resulted in an increase in the data collected about the users' behaviour which has …

Recommender systems (RSs) have been extensively developed to provide personalized
recommendations for the end users from the near-infinite options on the internet. Accuracy is …