The recommendation system (RS) suffers badly from the cold start problem (CSP) that occurs due to lack of sufficient information about new customers, purchase history, and browsing data. Moreover, data sparsity problems also arise when interaction is made among a limited number items. These issues not only pose negative impact on but significantly condense diversity choices available particular...

Many tourists who travel to explore different cultures and cities worldwide aim find the best tourist sites, accommodation, food according their interests. This objective makes it harder for decide plan where go what do. Aside from hiring a local guide, an option which is beyond most travelers’ budgets, majority of sojourners nowadays use mobile devices search or recommend interesting sites on ...

The main challenge of recommender systems is to be able to identify and recommend items that have a greater chance of meeting the interests of their users, which generally have a very subjective and heterogeneous nature. It is imperative, then, that recommender systems, from the identification of each user's profile, could recommend personalized items. However, the user’s profile is not enough ...

Location-Based Social Networks (LBSNs) allow users to post ratings and reviews and to notify friends of these posts. Several models have been proposed for Point-of-Interest (POI) recommendation that use explicit (i.e. ratings, comments) or implicit (i.e. statistical scores, views, and user influence) information. However the models so far fail to capture sufficiently user preferences as they ch...

Privacy is an important issue in Context-aware recommender systems (CARSs). In this paper, we propose a privacy-preserving CARS in which a user can limit the contextual information submitted to the server without sacrificing a significant recommendation accuracy. Specifically, for users, we introduce a client-side algorithm that the user can employ to generalize its context to some extent, in o...

In this paper, we introduce a novel situation-aware approach to improve a context based recommender system. To build situationaware user profiles, we rely on evidence issued from retrieval situations. A retrieval situation refers to the social-spatiotemporal context of the user when he interacts with the recommender system. A situation is represented as a combination of socialspatiotemporal con...

Development of Web 2.0 enabled users to share information online, which results into an exponential growth of world wide web data. This leads to the so-called information overload problem. Recommender Systems (RS) are intelligent systems, helping on-line users to overcome information overload by providing customized recommendations on various items. In real world, people are willing to take adv...