Manuel J. Barranco

A mobile 3D-GIS hybrid recommender system for tourism

The amount of touristic and travel information existing on the Internet is overwhelming. Recommender systems are typically used to filter irrelevant information and to provide personalized and relevant services to tourists. In this context, mobile devices are particularly useful because of their ubiquitous nature that turns them into an attractive platform for assisting on-the-move tourists to choose points of interest to visit according to their physical location. However, mobile devices also present several usability limitations that should be considered in order to provide information in a direct and intuitive way. In this paper, we present a novel mobile recommender system that brings together a hybrid recommendation engine and a mobile 3D GIS architecture. This system allows tourists to benefit from innovative features such as a 3D map-based interface and real-time location-sensitive recommendations. The details related to the design and implementation of the proposed solution are also presented, along with an empirical evaluation of user experience with the mobile application.

A Knowledge Based Recommender System with Multigranular Linguistic Information

Recommender systems are applications that have emerged in the e-commerce area in order to assist users in their searches in electronic shops. These shops usually offer a wide range of items that cover the necessities of a great variety of users. Nevertheless, searching in such a wide range of items could be a very difficult and time-consuming task. Recommender systems assist users to find out suitable items by means of recommendations based on information provided by different sources such as: other users, experts, item features, etc. Most of the recommender systems force users to provide their preferences or necessities using an unique numerical scale of information fixed in advance. In spite of this information is usually related to opinions, tastes and perceptions, therefore, it seems that is usually better expressed in a qualitative way, with linguistic terms, than in a quantitative way, with precise numbers. We propose a Knowledge Based Recommender System that uses the fuzzy linguistic approach to de...

A multigranular linguistic content‐based recommendation model

Recommendation systems are a clear example of an e‐service that helps the users to find the most suitable products they are looking for, according to their preferences, among a vast quantity of information. These preferences are usually related to human perceptions because the customers express their needs, taste, and so forth to find a suitable product. The perceptions are better modeled by means of linguistic information due to the uncertainty involved in this type of information. In this article, we propose a content‐based recommendation model that will offer a more flexible context to improve the final recommendations where the preferences provided by the sources will be modeled by means of linguistic variables assessed in different linguistic term sets. The proposal consists of offering a multigranular linguistic context for expressing the preferences instead of forcing users to use a unique scale. Then the content‐based recommendation model will look for the most suitable product(s), comparing them with the customer(s) information according to its resemblance.

A Context-Aware Mobile Recommender System Based on Location and Trajectory

Recommender systems have typically been used in tourism applications to filter out irrelevant information and to provide personalized recommendations to the users. With the advent of mobile devices and ubiquitous computing, RSs have begun to incorporate Location Based Services (LBS) into mobile tourism guides to provide users with interesting points of interest (POIs) according to their contextual information, mainly physical location. In this paper, we propose a context-aware system for mobile devices that incorporates some implicit contextual information that is scarcely used in the literature: the user’s speed and his trajectory. This system has been specifically crafted to assist travelling users by providing them with smart and personalized POIs along their route taking into account their current location and driving speed.

Weighting of Features in Content-Based Filtering with Entropy and Dependence Measures

AbstractContent-based recommender systems (CBRS) are tools that help users to choose items when they face a huge amount of options, recommending items that better fit the users profile. In such a process, it is very interesting to know which features of the items are more important for each user, thus the CBRS provides them higher weight. The Term Frequency-Inverse Document Frequency (TF-IDF) method is one of the most used for weighting of features, however, it does not provide the best results when the features are multi-valued. In this contribution, it is proposed a new method for obtaining the weights of the features by means of entropy and coefficients of dependency.

A Knowledge Based Recommender System Based on Consistent Preference Relations

E-commerce companies have developed many methods and tools in order to personalize their web sites and services according to users’ necessities and tastes. The most successful and widespread are the recommender systems. The aim of these systems is to lead people to interesting items through recommendations. Sometimes, these systems face situations in which there is a lack of information and this implies unsuccessful results. In this chapter we propose a knowledge based recommender system designed to overcome these situations. The proposed system is able to compute recommendations from scarce information. Our proposal will consist in gathering user’s preference information over several examples using an incomplete preference relation. The system will complete this relation and exploit it in order to obtain a user profile that will be utilized to generate good recommendations.

A method for weighting multi-valued features in content-based filtering

Content-based recommender systems (CBRS) and collaborative filtering are the type of recommender systems most spread in the e-commerce arena. A CBRS works with two sets of information: (i) a set of features that describe the items to be recommended and (ii) a users profile built from past choices that the user made over a subset of items. Based on these sets and on weighting items features the CBRS is able to recommend those items that better fits the user profile. Commonly, a CBRS deals with simple item features such as key words extracted from the item description applying a simple feature weighting model, based on the TF-IDF. However, this method does not obtain good results when features are assessed in multiple values and or domains. In this contribution we propose a higher level feature weighting method based on entropy and coefficients of correlation and contingency in order to improve the content-based filtering in settings with multi-valued features.

Group Recommendations Based on Hesitant Fuzzy Sets

Group recommender systems (GRSs) recommend items that are used by groups of people because certain activities, such as listening to music, watching a movie, dining in a restaurant, etc., are social events performed by groups of people sharing their tastes, and their choices affect all of them. GRSs help groups of people making choices in overloaded search spaces according to all group members preferences. A common GRS scheme aggregates users preferences to generate a group preference profile. However, the aggregation process may imply a loss of information, negatively affecting different properties of the GRS such as diversity of group recommendations, which is an important quality factor because of such recommendations are targeted to groups formed by users with individual and possibly conflicting preferences. To avoid and manage the loss of information caused by aggregation, this paper proposes to keep all group members preferences by using hesitant fuzzy sets (HFSs) and interpreting such information like the group hesitation about their preferences that will be used in the group recommendation process. To evaluate the performance and rank quality of the HFS GRS proposal, a case study is carried out.

Academic Orientation Supported by Hybrid Intelligent Decision Support System

It is common that in all academic systems the students must make decisions about the future by choosing among different alternatives that include professional profiles or modalities, elective subjects or optional, etc. These decisions can have a very important influence in the academic journey of the students because sometimes a wrong decision can lead to academic failure or its correction implies a time cost for the students. It is remarkable that in many academic systems these important decisions have to be made in early stages in which the students do not have enough maturity or knowledge to be conscious about the consequences in their future if a wrong decision is made. Ideally these multiple choices, offered to the students, want to facilitate the acquirement of some professional and valuable competences to obtain a job. Taking into account that the suitability of people in jobs or studies is not only restricted to their taste or preferences, but also other factors involved in the process of acquiring maturity as an adult who develops any function. These factors such as capacities, skills, attitudes concerning the task, social attitudes, taste, preferences, etc. (Brinol 2007, Robertson 1993, Salgado 1996, Shevin 2004), must be taken into account in such processes. Initially these decision making processes were made by the students themselves or with their parents support according to different criteria such as, preferences, future job market, even randomly, etc. Therefore, in order to improve this situation different countries introduced one figure, so-called advisor, whose role is to guide the students in their decision making situations regarding their academic future. The academic orientation process carried out by these advisors imply the review of different information regarding the students in order to report which academic alternatives suits better their skills, competences and needs. In most of academic institutions the advisor deals yearly with several hundreds of students with different skills, personalities, etc. To make an idea, in Spain, and depending on the high school, advisors can manage from 200 to 800 students. This number of students implies a big search space to find the relevant information that can facilitate the orientation for each one in a right way, making very hard to perform successfully the academic orientation process. Hence it seems suitable the development of automated tools that support the accomplishment of the different processes of academic orientation to improve the success of advisors’ tasks. An overview of different problems in the real world that deal with search spaces drove us to pay attention to the situation raised some years ago with the advent of Internet and the

Dealing with diversity and novelty in group recommendations using Hesitant fuzzy sets

Diversity and novelty are appreciated features by users of recommender systems, which alleviate the information overload problem. These features are more important in recommendation to groups because members interests and needs differ from each other or are even in conflict. Various techniques have been used to recommend to groups. However, these techniques apply an aggregation step that imply a loss of information, which negatively affect the recommendation. We aim at avoiding the negative influence of the aggregation step considering the various interests and needs of the group members as the group hesitation, thus, our proposal uses Hesitant Fuzzy Sets to model the group information. A case study is performed to evaluate the proposal, whose results show its performance regarding recommendation diversity, novelty and accuracy.