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 ...

Identifying a customer profile of interest is a challenging task for sellers. Preferences and profile features can range during the time in accordance with current trends. In this paper we investigate the application of different model-based Collaborative Filtering (CF) techniques and in particular propose a trusted approach to user-based clustering CF. We propose a Trust-aware Clustering Colla...

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...

Recommender systems, notably collaborative and hybrid information filtering approaches, vitally depend on neighborhood formation, i.e., selecting small subsets of most relevant peers from which to receive personal product recommendations. However, common similarity-based neighborhood forming techniques imply various drawbacks, rendering the conception of decentralized recommender systems virtua...

Collaborative filtering recommender systems are typically unable to generate adequate recommendations for newcomers. Empirical evidence suggests that the incorporation of a trust network among the users of a recommender system can significantly help to alleviate this problem. Hence, users are highly encouraged to connect to other users to expand the trust network, but choosing whom to connect t...

Recommender systems (RS) have been used for suggesting items (movies, books, songs, etc.) that users might like. RSs compute a user similarity between users and use it as a weight for the users’ ratings. However they have many weaknesses, such as sparseness, cold start and vulnerability to attacks. We assert that these weaknesses can be alleviated using a Trust-aware system that takes into acco...