A dynamic recommender system as reinforcement for personalized education by a fuzzly linguistic web system

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-

Integrating Quality Criteria in a Fuzzy Linguistic Recommender System for Digital Libraries

Recommender systems can be used in an academic environment to assist users in their decision making processes to find relevant information. In the literature we can find proposals based in user’ profile or in item’ profile, however they do not take into account the quality of items. In this work we propose the combination of item’ relevance for a user with its quality in order to generate more profitable and accurate recommendations. The system measures item quality and takes it into account as new factor in the recommendation process. We have developed the system adopting a fuzzy linguistic approach.Projects TIN2010-17876, TIC5299 y TIC-599

Introducing CSP Dataset: A Dataset Optimized for the Study of the Cold Start Problem in Recommender Systems

Recommender systems are tools that help users in the decision-making process of choosing items that may be relevant for them among a vast amount of other items. One of the main problems of recommender systems is the cold start problem, which occurs when either new items or new users are added to the system and, therefore, there is no previous information about them. This article presents a multi-source dataset optimized for the study and the alleviation of the cold start problem. This dataset contains info about the users, the items (movies), and ratings with some contextual information. The article also presents an example user behavior-driven algorithm using the introduced dataset for creating recommendations under the cold start situation. In order to create these recommendations, a mixed method using collaborative filtering and user-item classification has been proposed. The results show recommendations with high accuracy and prove the dataset to be a very good asset for future research in the field of recommender systems in general and with the cold start problem in particular.Spanish Government PID2019-103880RB-I00Andalusian Agency project P20_0067

A risk-aware fuzzy linguistic knowledge-based recommender system for hedge funds

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

Trust Based Fuzzy Linguistic Recommender Systems as Reinforcement for Personalized Education in the Field of Oral Surgery and Implantology

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-

Web platform for learning distributed databases’ queries processing

A distributed database is a collection of data stored in different locations of a distributed system. The processing of queries in distributed databases is quite complex but of great importance for information management. Students who have to learn that process have serious difficulties for understanding them. On this work we present a web platform for helping the students learning the processing and optimization of queries in distributed databases. The novelty of this platform is that as far as we know, there is no similar graphical tool. It allows to visualize step by step the different phases of distributed query processing, showing how are they forming, making it easier for the students to understand these concepts. Moreover, having this web platform available, always and everywhere, indirectly have an impact on other competences like encouraging students’ autonomous work and self-learning, adapting the teaching to its one-time necessities and reinforcing the advantages to apply information techniques in the teaching field. The results of the developed tests to validate the platform's functionalities and student's satisfaction were very positive.This work has been developed thanks to the funding of the project PID46-201617 of the Universidad de Jaén

Integrating Ontologies and Fuzzy Logic to Represent User-Trustworthiness in Recommender Systems

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 UJA2013/08/41TIN2013-40658-PTIC5299TIC-5991TIN2012-36951 co-financed by FEDER and TIC610

Sistemas de recomendaciones lingüísticos difusos para la difusión de información en bibliotecas digitales

Tesis Univ. Granada. Departamento de Ciencias de la Computación e Inteligencia ArtificialEsta tesis doctoral ha sido desarrollada con la financiación de la beca predoctoral adscrita al proyecto de investigación de excelencia P10-TIC-05299 de la Junta de Andalucía. También ha sido subvencionada por los proyectos TIN2010-22145-C02-01 y TIN2010-17876 del Ministerio de Ciencia e Innovació

Leveraging Localized Social Media Insights for Early Warning Systems

Social Media is more and more positioning as the channel to implement early warning systems for prompt reaction to events that might affect a company’s core business. The enabling of geo-location for social media interactions unlocks new possibilities for decision making scenarios aiming at triage situation where the quality of service decays. In this work we provide a system and a set of soft metrics to quantify the impact created by the reaction of people directly affected by an incident in a particular area in order to facilitate the service providers’ appropriate reaction, the decision making in marketing activities and to unveil customer acquisition opportunities applying the system to the competitors’ customers.TIN2013-40658-PTIN2016-75850-

Emotional profiling of locations based on social media

Social Media is increasingly becoming an integral part of our lives and a place where an ever growing portion of our daily communication takes place. As we communicate, we reveal our emotions and this emotional chronicle is kept in our Social Media history. As the access to Internet became more pervasive, Social Media platforms could also store the location where the interactions took place, enabling the analysis of the emotions in these locations. Pursuing this idea, we suggest a method to create the emotional profile of a location based on the long-term emotional rating of the geo-localized SM interactions. In this paper we present our method based on a multivariate kernel density function of SM interactions on a Russell’s inspired circumplex plane, explain how we extract the emotions from Social Media Interactions relying on a modified version of extended Affective Norms for English Words and validate our approach with real-life locations