Generally, many adaptive systems are developed and used in various fields. The effort to build the user's profile is repeated from one system to another due to the lack of interoperability and synchronization. Therefore, to provide an effective interoperability is a complex challenge due to the evolution of the user's profiles and its heterogeneity. The user's profiles evolution is not taken into account in the interoperable system. In our work, we are interested in the educational field. In this context, we propose a novel interoperable architecture allowing the exchange of the learner's profile information between different adaptive educational cross-systems to provide an access corresponding to the learners' needs. This architecture is automatically adapted to the learner's profiles that evolve over time and are syntactically, semantically and structurally heterogeneous. An experimental study shows the effectiveness of our architecture.

No abstract

Purpose Generally, the user requires customized information reflecting his/her current needs and interests that are stored in his/her profile. There are many sources which may provide beneficial information to enrich the user’s interests such as his/her social network for recommendation purposes. The proposed approach rests basically on predicting the reliability of the users’ profiles which may contain conflictual interests. The paper aims to discuss this issue. Design/methodology/approach This approach handles conflicts by detecting the reliability of neighbors’ profiles of a user. The authors consider that these profiles are dependent on one another as they may contain interests that are enriched from non-reliable profiles. The dependency relationship is determined between profiles, each of which contains interests that are structured based on k-means algorithm. This structure takes into consideration not only the evolutionary aspect of interests but also their semantic relationships. Findings The proposed approach was validated in a social-learning context as evaluations were conducted on learners who are members of Moodle e-learning system and Delicious social network. The quality of the created interest structure is assessed. Then, the result of the profile reliability is evaluated. The obtained results are satisfactory. These results could promote recommendation systems as the selection of interests that are considered of enrichment depends on the reliability of the profiles where they are stored. Research limitations/implications Some specific limitations are recorded. As the quality of the created interest structure would evolve in order to improve the profile reliability result. In addition, as Delicious is used as a main data source for the learner’s interest enrichment, it was necessary to obtain interests from other sources, such as e-recruitement systems. Originality/value This research is among the pioneer papers to combine the semantic as well as the hierarchical structure of interests and conflict resolution based on a profile reliability approach.

No abstract

Generally, the user requires customized data reflecting his current needs represented in terms of interests that are stored in his profile. Therefore, taking into account user's profile is significant to improve the returned results. Day by day, the user becomes more and more active in social networks and uses different distributed systems. In this context, the problem is that the access to user's interests becomes more and more difficult mainly after updating and/or enriching the user's profile. This may produce cognitive overload problem, which is time consuming in terms of browsing the user's profile. This problem can be solved by reorganizing user's interests. Most of the proposed reorganization methods use machine learning algorithms and different similarity measures. As the user's interests are characterized by their popularity and freshness, other approaches combine these characteristics into the notion of temperature in order to keep in the profile uniquely the corresponding interests for a period of time. In this paper, we propose an approach to reconstruct the user's profile by taking into account the semantic relationships between interests and by respectively merging the temperature and the k-means learning algorithm.