A review expresses the concerned aspects and corresponding assessments a customer has
towards a particular item. Extracting the user's interests and product's features from their …

In the traditional recommendation algorithms, due to the rapid development of deep learning
and Internet technology, user-item rating data is becoming increasingly sparse. The simple …

Although matrix factorization (MF) based collaborative filtering (CF) and deep learning
approaches have achieved great success, there is still much room for improvement in …

User review data is helpful in alleviating the data sparsity problem in many recommender
systems. In review-based recommendation methods, review data is considered as auxiliary …

Many studies utilizing review information to overcome the rating data sparsity obstacle in
recommender systems because its abundant information and outstanding explainability …

Recommender systems across many Internet services have become a critical part of online
businesses, as consumers would refer to them before making decisions. However, the lack …

User interest modeling is crucial for personalized news recommendation. Existing
personalized news recommendation methods usually take the news data as the minimum …

Analyzing user behaviors is a conventional approach to accomplish personalized
recommendation. However, due to the intrinsic complexity of user behaviors, modeling the …

The main task of personalized recommendation is capturing users' interests based on their
historical behaviors. Most of recent advances in recommender systems mainly focus on …

Recommendation models based on rating behavior often fail to properly deal with the
problem of data sparsity, resulting in the cold-start phenomenon, which limits the …