Recommender systems, providing users with personalized recommendations from a
plethora of choices, have been an important component for e-commerce applications to …

Recommender systems (RSs) are heavily used in e-commerce to provide users with high
quality, personalized recommendations from a large number of choices. Collaborative …

Collaborative Filtering (CF) is one of the best performing and most widely used approaches
for recommender systems. Although significant progress has been made in this area, current …

Commercial recommender systems in general are used to evaluate very large product sets.
In a user–item rating database, though users are very active, there are a few rating of the …

Collaborative filtering (CF) is one of the most popular recommender system technologies. It
tries to identify users that have relevant interests and preferences by calculating similarities …

Collaborative Filtering (CF) is one of the most commonly used recommendation methods.
CF consists in predicting whether, or how much, a user will like (or dislike) an item by …

Collaborative filtering (CF) is the most successful approach of Recommender Systems that
has been applied in a wide range of applications. In this approach, historical rating data is …

In E-commerce environment, a recommender system recommend products of interest to its
users. Several techniques have been proposed in the recommender systems. One of the …

We develop a novel framework, named as-injection, to address the sparsity problem of
recommender systems. By carefully injecting low values to a selected set of unrated user …

Recommender systems are one of the most important technologies in e-commerce to help
users filter out the overload of information. However, current mainstream recommendation …