Enhancing cultural recommendations through social and linked open data

In this article, we describe a hybrid recommender system (RS) in the artistic and cultural heritage area, which takes into account the activities on social media performed by the target user and her friends, and takes advantage of linked open data (LOD) sources. Concretely, the proposed RS (1) extracts information from Facebook by analyzing content generated by users and their friends; (2) performs disambiguation tasks through LOD tools; (3) profiles the active user as a social graph; (4) provides her with personalized suggestions of artistic and cultural resources in the surroundings of the user’s current location. The last point is performed by integrating collaborative filtering algorithms with semantic technologies in order to leverage LOD sources such as DBpedia and Europeana. Based on the recommended points of cultural interest, the proposed system is also able to suggest to the active user itineraries among them, which meet her preferences and needs and are sensitive to her physical and social contexts as well. Experimental results on real users showed the effectiveness of the different modules of the proposed recommender.

     

Prerequisites between learning objects: Automatic extraction based on a machine learning approach

One standing problem in the area of web-based e-learning is how to support instructional designers to effectively and efficiently retrieve learning materials, appropriate for their educational purposes. Learning materials can be retrieved from structured repositories, such as repositories of Learning Objects and Massive Open Online Courses; they could also come from unstructured sources, such as web hypertext pages. Platforms for distance education often implement algorithms for recommending specific educational resources and personalized learning paths to students. But choosing and sequencing the adequate learning materials to build adaptive courses may reveal to be quite a challenging task.In particular, establishing the prerequisite relationships among learning objects, in terms of prior requirements needed to understand and complete before making use of the subsequent contents, is a crucial step for faculty, instructional designers or automated systems whose goal is to adapt existing learning objects to delivery in new distance courses. Nevertheless, this information is often missing. In this paper, an innovative machine learning-based approach for the identification of prerequisites between text-based resources is proposed. A feature selection methodology allows us to consider the attributes that are most relevant to the predictive modeling problem. These features are extracted from both the input material and weak-taxonomies available on the web. Input data undergoes a Natural language process that makes finding patterns of interest more easy for the applied automated analysis. Finally, the prerequisite identification is cast to a binary statistical classification task. The accuracy of the approach is validated by means of experimental evaluations on real online coursers covering different subjects.     

A web-based training system for business letter writing

As with the growing degree of office automation and diffuse use of electronic media, such as e-mails, written business communication is becoming a key element to promote synergies, relationships and disseminating information about products and services. Task recognition and the definition of strategies and suitable vocabularies are some of the activities that office workers deal with each time a communicative intent has to be effectively transferred and understood by a given addressee. This paper introduces a web-based intelligent training system based on the constructivism theory and self-directed learning paradigms for assisting company workers in the drafting business letters-writing task. A case-based engine suggests ad hoc rhetorical letters that users have the chance to adapt to their particular contexts and save them into user-defined case libraries.