Profiling clients in the tourism sector using fuzzy linguistic models based on 2-tuples

This work has been funded by the Spanish State Research Agency through the project PID2019-103880RB-I00 / AEI / 10.13039/501100011033.Customer segmentation is a key piece of a company's business strategy. This paper presents a segmentation of the online users of tourism platforms through the recency, frequency and helpfulness of the users. 2-tuples model is applied to these variables to be more precise without loss of information. In addition, the functionality of the proposal made by the authors is verified through a use case in which TripAdvisor opinioners are segmented in reference to the experience lived in hotels and tourist accommodation.Spanish Government PID2019-103880RB-I00 / AEI / 10.13039/50110001103

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

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-

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

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

Sistemas de acceso a la información basados en información lingüística difusa y técnicas de filtrado

Tesis Univ. Granada. Departamento de Ciencias de la Computación e Inteligencia Artificial. Leída el 5 de mayo de 200

A linguistic multi-criteria decision making methodology for the evaluation of tourist services considering customer opinion value

a consequence of the exponential growth in online data, tourism sector has experimented a radical transformation. From this large amount of information, opinion makers can be benefited for decision making in their purchase process. However, it can also harm them according to the information they consult. In fact, being benefited or harmed by the information translates into greater or lesser satisfaction after the purchase. This will largely depend on the published opinions that they take into account, which in turn depend on the value of the opinioner who publishes said information. In this paper, the authors propose a methodology that integrates multiple decision-making techniques and with which it is intended to obtain a ranking of hotels through the opinions of their past clients. To do this, the customer value is obtained using the Recency, Frequency, Helpfulness model. The information about the users found in the social networks is managed and aggregated using the fuzzy linguistic approach 2-tuples multi-granular. In addition, we have verified the functionality of this methodology by presenting a business case by applying it on TripAdvisor data.Spanish State Research Agency PID2019-103880RB-I00/AEI/10.13039/501100011033 TIN2016-75850-RNational Natural Science Foundation of China (NSFC) 71725001 71910107002State key R&D Program of China 2020YFC0832702Major project of the National Social Science Foundation of China 19ZDA09

A comparison between Fuzzy Linguistic RFM Model and traditional RFM model applied to Campaign Management. Case study of retail business.

Recency Frequency Monetary Value (RFM) is a clear and descriptive way to classify a customer database based on purchasing behavior that direct marketers have used with success since almost the last twenty years. Despite the fashion that exists lately around predictive models and artificial intelligence, direct marketing’s RFM, still has a place in modern database marketing. In a real business environment, RFM can still be useful when models are not practical because it is user friendly and the outcome is always interpretable. It can also be used to combine with other models. In this paper, we show a real example about how easy, accurate and explainable can be a customer segmentation based on the traditional RFM model and the 2-tuple RFM model applied to a customer database. It will help us to better understand the benefits of applying the 2-tuple model instead of the traditional one. We will be able to see how, by applying a k-means clustering on top of the 2-tuple model, segments have a great applicability from the business point of view. By using descriptive variables, we will clarify the cluster description and the model will provide us an extremely clear idea about how customers behave. The main goal for developing this example was to define the best target for a direct campaign communication. Data used for this analysis belongs to a worldwide home furniture, Scandinavian Retailer, and are related to its loyalty program which give us the members’ historical purchase information.TIN2016-75850-

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.TIN2016-75850-RTIN2013-40658-