Recommendation systems can help internet users to find interesting things that match more with their profile. With the development of the digital age, recommendation systems have become indispensable in our lives. On the one hand, most of recommendation systems of the actual generation are based on Collaborative Filtering (CF) and their effectiveness is proved in several real applications. The main objective of this paper is to improve the recommendations provided by collaborative filtering using clustering. Nevertheless, taking into account the intrinsic relationship between users can enhance the recommendations performances. On the other hand, cooperative game theory techniques such as Shapley Value, take into consideration the intrinsic relationship among users when creating communities. With that in mind, we have used SV for the creation of user communities. Indeed, our proposed algorithm preforms into two steps, the first one consists to generate communities user based on Shapley Value, all taking into account the intrinsic properties between users. It applies in the second step a classical collaborative filtering process on each community to provide the Top-N recommendation. Experimental results show that the proposed approach significantly enhances the recommendation compared to the classical collaborative filtering and k-means based collaborative filtering. The cooperative game theory contributes to the improvement of the clustering based CF process because the quality of the users communities obtained is better.

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Abstract:We propose in this paper a knowledge-based framework for a mobile OLAP. Its main goal is to allow decision-makers to, efficiently, access datasets in OLAP system anywhere and anytime. The challenge of this work is to be able to improve decisional performances while overcoming capability context. To achieve this goal, the framework integrates in a systematic, generic and extensible way, knowledge in the context-aware recommender process, on one hand. On the other hand, it proposes contextual recommendations. For that purpose, the context-aware recommender system exploits the knowledge extracted (profile in a particular situation 'contextual profile') automatically and the current user's contextual profile to compute a list of contextual recommendations (analysis) adapted to the capability context. We conducted a set of experiments to evaluate the performance of our knowledge-based framework. The results are encouraging and show that our framework contributes significantly to improve mobile OLAP navigation.Keywords: OLAP; contextual recommender system; data-mining; K2; OLAM; CBR; knowledge-based system; decision support systems.Reference to this paper should be made as follows: Rezoug, N., Boussaid, O. and Nader, F. (2015) This paper is a revised and expanded version of a paper entitled 'A contextual personalized recommender system for mobile OLAP' presented at the Information Technology and E-service (ICITes), Sousse, Tunis, 24-26 March 2012.

OLAP systems facilitate analysis by providing a multidimensional data space which decision makers explore interactively by a succession of OLAP operations. However, these systems are developed for a group of decision makers or topic analysis "subject-oriented", which are presumed, have identical needs. It makes them unsuitable for a particular use. Personalization aims to better take into account the user; first this paper presents a summary of all work undertaken in this direction with a comparative study. Secondly we developed a search algorithm for class association rules between query type and user (s) to deduce the profile of a particular user or a user set in the same category. These will be extracted from the log data file of OLAP server. For this we use a variant of prediction and explanation algorithms. These profiles then form a knowledge base. This knowledge base will be used to generate automatically a rule base (ACE), for assigning weights to the attributes of data warehouses by type of query and user preferences. More it will deduce the best contextual sequence of requests for eventual use in a recommended system.

Studies in diabetic systems are vital for both diabetes patients and the medical industry because it is possible to use these systems to improve healthcare quality in several ways. These systems can help doctors to treat patients with all types of conditions. This research selected service oriented architecture with a decision aspect (SOAda) as the core architecture for the reason that it functions well when the domain decision is not adequately clear. This chapter demonstrates the study of the diabetes systems within service-oriented architecture and provides clinical management and advices for patients. Its aim is to (1) identify the current research state in the area, (2) assist in deriving the key problems and features with the SOAda, and (3) use these key features so that they could serve as a guide in the development of a new tool to address the decision problems in SOAda.