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The rapid advances in Web technologies are promoting the development of new pedagogic models based on virtual teaching. In this framework, personalized services are necessary. Recommender systems can be used in an academic environment to assist users in their teaching-learning processes. In this paper, we present a trust based recommender system, adopting a fuzzy linguistic modeling, that provides personalized activities to students in order to reinforce their education, and applied it in the field of oral surgery and implantology. We don’t take into account users with similar ratings history but users in which each user can trust and we provide a method to aggregate the trust information. This system can be used in order to aid professors to provide students with a personalized monitoring of their studies with less effort. The results obtained in the experiments proved to be satisfactory., TIN2016-75850-R

Social Media (SM) has become the easiest, cheapest and fastest channel for companies to identify the events that affect their customers. The geo-location capabilities of the SM interactions enable Early Warning Systems to alert not only when the quality of service decays, but also where and how many customers are impacted. In this paper we present a system and a set of supporting metrics that exploit the geo-localized SM stream, quantify the perceived impact of events, incidents, etc. on a particular area over time. Industrial service providers can add this perceptional perspective to their standard monitoring tools to enable a prompt and appropriate reaction, the decision-making in marketing activities and to unveil customer acquisition opportunities applying the system to the competitors’ customers.

The job market is rapidly changing. Artificial Intelligence and automation technologies are reshaping the career market. Everyday, new jobs appear and new skills are added to the scope of existing job profiles. At the same time, some skills that once were assumed to be "must-haves" for particular jobs are no longer requested and some jobs are even becoming obsolete. The speed of changes as well as the increasing complexity of the job market introduce a key new challenge: there is no clear definition for a particular job in terms of skills and scope and consequently, people holding the same job title cannot be assumed to be actually doing the same thing. In addition, applicants find difficult to develop career paths, as the mapping of skills to particular jobs are fuzzier than ever before. In this article, we present a novel approach to homogenize the job definition, gathering first subject matter expertise using semantic expansion techniques on collaborative wikies, applying a word embeddings supported method to mine the skills from existing job posts and finally executing a semantic matching algorithm to converge to a consistent skills mapping. In order to show how our method performs, we apply it to one of the most popular, yet heterogeneous modern jobs, the data scientist and discuss the results obtained for the English speaking market., This paper has been developed with the FEDER financing under Project TIN2016-75850-R

One of the most difficult tasks for hedge funds investors is selecting a proper fund with just the right level level of risk. Often times, the issue is not only quantifying the hedge fund risk, but also the level the investors consider just right. To support this decision, we propose a novel recommender system, which is aware of the risks associated to different hedge funds, considering multiple factors, such as current yields, historic performance, diversification by industry, etc. Our system captures the preferences of the investors (e.g. industries, desired level of risk) applying fuzzy linguistic modeling and provides personalized recommendations for matching hedge funds. To demonstrate how our approach works, we have first profiled more than 4000 top hedge funds based on their composition and performance and second, created different simulated investment profiles and tested our recommendations with them., This paper has been developed with the FEDER financing under Project TIN2016-75850-R.

The significant advances in information and communication technologies are changing the process of how information is accessed. The internet is a very important source of information and it influences the development of other media. Furthermore, the growth of digital content is a big problem for academic digital libraries, so that similar tools can be applied in this scope to provide users with access to the information. Given the importance of this, we have reviewed and analyzed several proposals that improve the processes of disseminating information in these university digital libraries and that promote access to information of interest. These proposals manage to adapt a user’s access to information according to his or her needs and preferences. As seen in the literature one of the techniques with the best results, is the application of recommender systems. These are tools whose objective is to evaluate and filter the vast amount of digital information that is accessible online in order to help users in their processes of accessing information. In particular, we are focused on the analysis of the fuzzy linguistic recommender systems (i.e., recommender systems that use fuzzy linguistic modeling tools to manage the user’s preferences and the uncertainty of the system in a qualitative way). Thus, in this work, we analyzed some proposals based on fuzzy linguistic recommender systems to help researchers, students, and teachers access resources of interest and thus, improve and complement the services provided by academic digital libraries., TIN2016-75850-R, TIN2013-40658-P

We present a quality-based fuzzy linguistic recommender system for researchers. We propose the use of some bibliometrics measures as the way to quantify the quality of both items and users. The system takes into account the measured quality as the main factor for the re-ranking of the top-N recommendations list in order to point researchers to the latest and the best papers in their domain. To prove the accuracy improvement, we conduct a study involving dierent recommendation approaches. The results obtained proved to be satisfactory within the research departments who took part on the tests.

Information Technology and Quantitative Management (ITQM 2015), The seek of a personalized and quality education is the objective of Bologna process, but to carry out this task has a major economic impact. To soften this impact, one possible solution is to make use of recommender systems, which have already been introduced in several academic fields. In this paper, we present AyudasCBI, a novel fuzzy linguistic Web system that uses a recommender system to provide personalized activities to students to reinforce their individualized education. This system can be used in order to aid professors to provide students with a personalized monitoring of their studies with less effort. To prove the system, we conduct a study involving some students, aiming at measuring their performance. The results obtained proved to be satisfactory compared with the rest of the students who did not take part of the study., Projects TIC-5991 and TIN2013-40658-P

Information Technology and Quantitative Management (ITQM 2015), Recommender systems can be used to assist users in the process of accessing to relevant information. In the literature we can find sundry approaches for generating personalized recommendations and all of them make use of different users’ and/or items’ features. Building accurate profiles plays an essential role in this context, so that the system's success depend to a large extent on the ability of the learned profiles to represent the user's preferences. An ontology works very well to characterize the users profiles. In this paper we develop an ontology to characterize the trust between users using the fuzzy linguistic modelling, this way in the recommendation generation process we do not take into account users with similar ratings history but users in which each user can trust. We present our ontology and provide a method to aggregate the trust information captured in the trust-ontology and to update the user profiles based on the feedback., Projects UJA2013/08/41, TIN2013-40658-P, TIC5299, TIC-5991, TIN2012-36951 co-financed by FEDER and TIC6109

Recommender systems evaluate and filter the great amount of information available on the Web, so they could be used to help users in their access processes to relevant information. In the literature we can find a lot of approaches for generating personalized recommendations. Hybrid recommender systems combine in different ways several approaches, so these recommendation strategies represent a promising solution for multiple applications. In this paper we propose a hybrid fuzzy linguistic recommender system to help the Technology Transfer Office staff in the dissemination of research resources interesting for the users. The system recommends users both specialized and complementary research resources and additionally, it discovers potential collaboration possibilities in order to form multidisciplinary working groups.

In the recent times the amount of information coming overwhelms us, and because of it we have serious problems to access to relevant information, that is, we suffer information overload problems. Recommender systems have been applied successfully to avoid the information overload in different scopes, but the number of electronic resources daily generated keeps growing and the problem still remain. Therefore, we find a persistent problem of information overload. In this paper we propose an improved recommender system to avoid the persistent information overload found in a University Digital Library. The idea is to include a memory to remember selected resources but not recommended to the user, and in such a way, the system could incorporate them in future recommendations to complete the set of filtered resources, for example, if there are a few resources to be recommended or if the user wishes output obtained by combination of resources selected in different recommendation rounds.