Álvaro Tejeda-Lorente

A hybrid recommender system for the selective dissemination of research resources in a Technology Transfer Office

Recommender systems could be used to help users in their access processes to relevant information. Hybrid recommender systems 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. Thus, this system becomes an application that can be used to help the Technology Transfer Office staff to selectively disseminate the research knowledge and to increase its information discovering properties and personalization capacities in an academic environment.

CARESOME: A system to enrich marketing customers acquisition and retention campaigns using social media information

Abstract The enabling of geo-localization for Social Media content opens the door to a new set of applications based on the voice of the customer. For any company it is critical to understand both their own and their competitors’ strengths and weaknesses in all locations where they offer a service. With this motivation we created a Customers Acquisition and REtention system based on SOcial MEdia (CARESOME). Our system extracts and separates all social media interactions in a given location by market player and communication purpose and quantifies the impact of each single interaction over a given time period. To model the impact of the social media interactions, CARESOME relies on a set of metrics based on both intrinsic and extrinsic components—including Entity Engagement Index, Differential Perception Factor, Tie-Strength and Number of Exposed users—. In addition to the definition of our impact quantification metrics, we provide a thorough discussion about the design decisions taken to build our system. To illustrate the behavior of our system, we show-case a real world scenario from the airline industry based on two major airports in Great Britain.

REFORE: A recommender system for researchers based on bibliometrics

Abstract Recommender systems (RSs) exploit past behaviors and user similarities to provide personalized recommendations. There are some precedents of usage in academic environments to assist users finding relevant information, based on assumptions about the characteristics of the items and users. Even if quality has already been taken into account as a property of items in previous works, it has never been given a key role in the re-ranking process for both items and users. In this paper, we present REFORE, a quality-based fuzzy linguistic REcommender system FOr REsearchers. We propose the use of some bibliometric measures as the way to quantify the quality of both items and users without the interaction of experts as well as the use of 2-tuple linguistic approach to describe the linguistic information. 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 out researchers to the latest and the best papers in their research fileds. To prove the accuracy improvement, we conduct a study involving different recommendation approaches, aiming at measuring their performance gain. The results obtained proved to be satisfactory for the researchers from different departments who took part on the tests.

A new model to quantify the impact of a topic in a location over time with Social Media

A method to quantify the impact of a topic over time in a location is proposed.We adopt the Recency, Frequency and Monetary model to ease the application.We introduce the concept of Social Media Engagement and Exposure to a topic.Our system harvests and processes geo-located Twitter to create the impact metrics.For the evaluation, we discussed the application to different real-world topics. Social Media can be used as a thermometer to measure how society perceives different news and topics. With the advent of mobile devices, users can interact with Social Media platforms anytime/anywhere, increasing the proportion of geo-located Social Media interactions and opening new doors to localized insights. This article suggests a new method built upon the industry standard Recency, Frequency and Monetary model to quantify the impact of a topic on a defined geographical location during a given period of time. We model each component with a set of metrics analyzing how users in the location actively engage with the topic and how they are exposed to the interactions in their Social Media network related to the topic. Our method implements a full fledged information extraction system consuming geo-localized Social Media interactions and generating on a regular basis the impact quantification metrics. To validate our approach, we analyze its performance in two real-world cases using geo-located tweets.

TPLUFIB-WEB: A fuzzy linguistic Web system to help in the treatment of low back pain problems

Low back pain affects a large proportion of the adult population at some point in their lives and has a major economic and social impact. To soften this impact, one possible solution is to make use of Information and Communication Technologies. Recommender systems, which exploit past behaviors and user similarities to predict possible user needs, have already been introduced in several health fields. In this paper, we present TPLUFIB-WEB, a fuzzy linguistic Web system that uses a recommender system to provide personalized exercises to patients with low back pain problems and to offer recommendations for their prevention. This system may be useful to reduce the economic impact of low back pain, help professionals to assist patients, and inform users on low back pain prevention measures. TPLUFIB-WEB satisfies the Web quality standards proposed by the Health On the Net Foundation (HON), Official College of Physicians of Barcelona, and Health Quality Agency of the Andalusian Regional Government, endorsing the health information provided and warranting the trust of users.

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

Abstract 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.

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

Abstract 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 systems success depend to a large extent on the ability of the learned profiles to represent the users 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.

A Dynamic Recommender System as Reinforcement for Personalized Education by a Fuzzly Linguistic Web System

Abstract 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.

Emotional Profiling of Locations Based on Social Media

Abstract 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 Russells 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.

Aiding in the Treatment of Low Back Pain by a Fuzzy Linguistic Web System

Low back pain affects a large proportion of the adult population at some point in their lives and has a major economic and social impact. To soften this impact, one possible solution is to make use of recommender systems, which have already been introduced in several health fields. In this paper, we present TPLUFIB-WEB, a novel fuzzy linguistic Web system that uses a recommender system to provide personalized exercises to patients with low back pain problems and to offer recommendations for their prevention. This system may be useful to reduce the economic impact of low back pain, help professionals to assist patients, and inform users on low back pain prevention measures. A strong part of TPLUFIB-WEB is that it satisfies the Web quality standards proposed by the Health On the Net Foundation (HON), Official College of Physicians of Barcelona, and Health Quality Agency of the Andalusian Regional Government, endorsing the health information provided and warranting the trust of users.