Context-aware features have been widely recognized as important factors in recommender systems. However, as a major technique in recommender systems, traditional Collaborative Filtering (CF) does not provide a straightforward way of integrating the context-aware information into personal recommendation. We propose a Coupled Collaborative Filtering (CCF) model to measure the contextual informati...

Novel research works in recommender systems have illustrated the benefits of exploiting contextual information, such as the time and location of a suggested place of interest, in order to better predict the user ratings and produce more relevant recommendations. But, when deploying a context-aware system one must put in place techniques for operating in the cold-start phase, i.e., when no or fe...

THE WORLD POPULATION’S average age has been increasing gradually over the past 50 years mainly because of medical and healthcare advances. However, millions of people suffer from chronic respiratory diseases, arthritis, and back pain.1 In addition, exposure to air pollution causes millions of illnesses and premature deaths annually worldwide2 and harms the health of children, the elderly, and p...

Recommender systems have been widely applied to assist user’s decision making by providing a list of personalized item recommendations. Context-aware recommender systems (CARS) additionally take context information into considering in the recommendation process, since user’s tastes on the items may vary from contexts to contexts. Several context-aware recommendation algorithms have been propose...

This study proposes a novel recommender system to provide the advertisements of context-aware services. Our proposed model is designed to apply a modified collaborative filtering (CF) algorithm with regard to the several dimensions for the personalization of mobile devices – location, time and the user’s needs type. In particular, we employ a classification rule to understand user’s needs type ...

Anonymous recommender systems are the electronic pendant to vendors, who ask the customers a few questions and subsequently recommend products based on the answers. In this article we will propose attribute aware classifier-based approaches for such a system and compare it to classifier-based approaches that only make use of the product IDs and to an existing knowledge-based system. We will sho...

Recommender systems have been a hot research area recently. One of the most widely used methods is Collaborative Filtering(CF), which selects items for an individual user from other similar users. However, CF may not fully reflect the procedure of how people choose an item in real life, for users are more likely to ask friends for opinions instead of asking strangers with similar interests. Rec...

Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user [20]. In the music domain recommender systems can support information search and discovery tasks by helping the user to find relevant music items, for instance, new music tracks, or artists that the user may not even know [18, 9]. Several techniques have been proposed but most of t...

In today’s world, Recommender System (RS) is the most effective means used to manage huge amount of multimedia content available on internet. RS learns user preferences and relationships among users items. It helps discover new interesting items make use different media types such as text, audio, video images. can act an information filtering model which overcome issues related over-fitting exc...