The importance of customer-oriented marketing has increased for companies in recent
decades. With the advent of one-customer strategies, especially in e-commerce, traditional …

Recommender Systems present a high-level of sparsity in their ratings matrices. The
collaborative filtering sparse data makes it difficult to: 1) compare elements using memory …

Clustering based recommender systems have been demonstrated to be efficient and
scalable to large-scale datasets. However, due to the employment of dimensionality …

In the current digital landscape, both information consumers and producers encounter
numerous challenges, underscoring the importance of recommender systems (RS) as a vital …

Abstract Personalized Recommender Systems (RS) are used to help people reduce the
amount of time they spend to find items they are interested in. Collaborative Filtering (CF) is …

Memory based algorithms, generally referred as similarity based Collaborative Filtering (CF)
algorithm, is one of the most widely accepted approaches to provide service …

Quality of service (QoS) has been playing an increasingly important role in today's Web
service environment. Many techniques have been proposed to recommend personalized …

Recommender systems seek to find the interesting items by filtering out the worthless items.
Collaborative filtering is one of the most successful recommendation approaches. It typically …

Collaborative filtering is one of the widely used recommendation technique. It provides
automated and personalized suggestions to consumers for selecting variety of products by …

Commonly used similarity-based algorithms in memory-based collaborative filtering may
provide unreliable and misleading results. In a cold start situation, users may find the most …