Juan Bernabé-Moreno

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.

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.

Quantifying the emotional impact of events on locations with social media

Abstract The world nowadays is almost inconceivable without the existence of Social Media. An ever growing part of our daily communicational activity takes place in the social digital platforms, where not only what we say is kept, but also when and increasingly where we say it. The way we communicate is very insightful, as the words we chose in our communication reveal our emotional state. Inspired by these ideas, we created a new method to quantify the emotional impact of an event on a particular location in absolute terms but also broken down to the different emotional states. To support that, we explored different modelling approaches for the emotional profiling of locations adopting the well established Pleasantness-Arousal-Dominance paradigm. Apart from defining our method, we explain in this paper the procedure of emotions extraction from Social Media Interactions relying on a modified version of extended Affective Norms for English Words, describe the system we implemented to validate our method and discuss the overall performance of our approach with different emotionally rich events in three known locations.

Using Bibliometrics and Fuzzy Linguistic Modeling to Deal with Cold Start in Recommender Systems for Digital Libraries

Every recommender system approach suffers the cold start problem to a greater or lesser extent. To soften this impact, the more common solution is to find the way of populating users profiles either using hybrid approach or finding external data sources. In this paper, we present a fuzzy linguistic approach that using bibliometrics aids to soft or remove the necessity of interaction of users providing them with personalized profiles built beforehand, thus reducing the cold start problem. To prove the effectiveness of the system, we conduct a test involving some researchers, aiming to build their profiles automatically. The results obtained proved to be satisfactory for the researchers.

Fuzzy Linguistic Recommender Systems for the Selective Diffusion of Information in Digital Libraries

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.