Recommender systems are efficient tools for filtering online information, which is
widespread owing to the changing habits of computer users, personalization trends, and …

With the rapid growth of information technology, the current era is witnessing an exponential
increase in the generation and collection of web data. Projecting the right information to the …

E-commerce customers demand quick and easy access to products in large search spaces
according to their needs and preferences. To support and facilitate this process …

This study proposes a novel collaborative filtering framework which integrates both
subjective and objective information to generate recommendations for an active consumer …

Recommender systems (RSs) are designed to provide personalized recommendations to
users. Recently, knowledge graphs (KGs) have been widely introduced in RSs to improve …

Resource matching and recommendation is an important topic in the field of cloud
computing. While a lot of cloud resource discovery and negotiation models have been …

In order to reflect the actual case of users' decisions and rating patterns, and solve the
sparsity problem of traditional collaborative filtering recommendation algorithms, a trapezoid …

The web page recommenders predict and recommend the web pages to the users based on
the behavior of their search history. The web page recommender system analyzes the …

Recently, models for user representation learning have been widely applied in click-through-
rate (CTR) and conversion-rate (CVR) prediction. Usually, the model learns a universal user …

ABSTRACT Web Recommendation Systems are implemented by using collaborative
filtering approach. It is a specific type of information filtering system that aims to predict the …