In a mobile setting, user preferences vary in different contexts. Advances in mobile technologies have made the collection of user context information feasible, and as a result, the context-aware mobile recommender system field has been formed. Although there exist several different approaches to incorporating context into the recommendation process, context-aware recommendations are still diff...

Context-aware recommender systems (RSs) have been mostly evaluated offline, and very few studies have tried to assess their benefit from the user perspective. Moreover, little research was previously dedicated to integrate the weather context into RSs that could benefit from that information, such as tourism applications. This paper presents a novel context-aware mobile recommender system, name...

WHO estimates that the number of people with diabetes will grow 114% by 2030.It declares that, patients have to play a major role to control and therapy of diabetes by being provided with updated knowledge about the disease and different aspects of available treatments, diet therapy in particular. In this regard, diets recommender Systems would be helpful. They are techniques and tools which su...

Recommender systems are important building blocks in many of today’s e-commerce applications including targeted advertising, personalized marketing and information retrieval. In recent years, the importance of contextual information has motivated many researchers to focus on designing systems that produce personalized recommendations in accordance with the available contextual information of us...

Recommender systems provide personalized recommendation for their users. These systems are still needed to be optimized to provide more effective recommendations. In some models the context of user and the item is considered during the recommendation process so that it would be possible to make a better estimation of the user's rating. In this article context aware recommender models are addres...

recommender systems (rs) provide personalized recommendation according to user need by analyzing behavior of users and gathering their information. one of the algorithms used in recommender systems is user-based collaborative filtering (cf) method. the idea is that if users have similar preferences in the past, they will probably have similar preferences in the future. the important part of col...

Recommender systems are efficient tools that overcome the information overload problem by providing users with the most relevant contents. This is generally done through user’s preferences/ratings acquired from log files of his former sessions. Besides these preferences, taking into account the interaction context of the user will improve the relevancy of recommendation process. In this paper, ...

Process-aware Recommender systems can provide critical decision support functionality to aid business process execution by recommending what actions take next. Based on recent advances in the field of deep learning, we present a novel memory-augmented neural network (MANN) based approach for constructing process-aware recommender system. We propose architecture, namely Write-Protected Dual Cont...

This paper describes the context-aware mobile tourist application COMPASS that adapts its services to the user’s needs based on both the user’s interests and his current context. In order to provide context-aware recommendations, a recommender system has been integrated with a contextaware application platform. We describe how this integration has been accomplished and how users feel about such...