Abstract In recent years, recommender systems have been employed in domains like e-commerce, tourism, and multimedia streaming, where personalising users’ experience based on their interactions is a fundamental aspect to consider. Recent recommender system developments have also focused on well-being, yet existing solutions have been entirely designed considering one single well-being aspect in isolation, such as a healthy diet or an active lifestyle. This research introduces EvoRecSys, a novel recommendation framework that proposes evolutionary algorithms as the main recommendation engine, thereby modelling the problem of generating personalised well-being recommendations as a multi-objective optimisation problem. EvoRecSys captures the interrelation between multiple aspects of well-being by constructing configurable recommendations in the form of bundled items with dynamic properties. The preferences and a predefined well-being goal by the user are jointly considered. By instantiating the framework into an implemented model, we illustrate the use of a genetic algorithm as the recommendation engine. Finally, this implementation has been deployed as a Web application in order to conduct a users’ study.

This article provides a brief tour through the main fuzzy and linguistic decision-making trends, studies, methodologies, and models developed in the last 50 years. Fuzzy and linguistic decision-making approaches allow to address complex real-world decision problems where humans exhibit vagueness, imprecision, and/or use natural language to assess decision alternatives, criteria, etc. The aim of this article is threefold. First, the main fuzzy set theory and computing with words-based representation paradigms of decision information, with their different levels of expressive richness and complexity, are reviewed. Second, three core decision-making frameworks are examined: 1) multicriteria decision making; 2) group consensus-driven decision making; and 3) multiperson multicriteria decision making. Third, the article discusses new complex decision-making frameworks that have emerged in recent years, where decisions are guided by the “wisdom of the crowd”: their associated challenges are highlighted and considerations on much needed key guidelines for future research in the field are provided.

Reciprocal recommender systems, which recommend users to each other, have gained significant importance in various Internet services for connecting people in a personalized manner, such as: online dating, recruitment, socializing, learning, or skill-sharing. Unlike classical item-to-user recommenders, a fundamental requirement in reciprocal recommendation is that both parties, namely the requester user and the recommended user, must be satisfied with the "user match" recommendation in order to deem it as successful. Therefore, bidirectional preferences indicating mutual compatibility between pairs of users need to be estimated predicated on information fusion. This tutorial introduces the emerging and novel topic of reciprocal recommender systems, by analyzing their information retrieval, data-driven preference modelling and integration mechanisms for predicting suitable user matches. The tutorial will also discuss the current trends, practical use, impact and challenges of reciprocal recommenders in different application domains.

Managing comparative linguistic expressions (CLEs) information is a key issue in group decision-making (GDM). A transformation approach has been previously defined to convert CLEs into hesitant fuzzy linguistic terms sets (HFLTSs). However, it is noted that the occurring possibilities of the linguistic terms in the HFLTSs are assumed equal. This assumption might sometimes not capture the real opinions of the decision makers. Linguistic distribution assessments (LDAs) are an effective way to deal with this issue. This paper develops a linguistic distribution-based optimization approach for converting CLEs into LDAs, in which we assume that decision makers provide their opinions using preference relations with CLEs. Particularly, the proposed optimization approach is based on the use of a consistency-driven methodology, which seeks to minimize the inconsistency level of LDA preference relations obtained by transforming the original CLE preference relations elicited from decision makers. The linguistic distribution-based optimization approach is further developed to transform CLEs into interval LDAs to increase their flexibility. Moreover, society and technology trends make it possible to involve and manage large groups of decision makers in GDM environment. Therefore, a large-scale GDM framework with CLE information is designed based on the linguistic distribution-based optimization approach. To justify the effectiveness and applicability of the proposed methodology, it is applied to solve a real large-scale GDM problem, pertaining the selection of the best sustainable disinfection technique for wastewater reuse projects. A comparison against a baseline method is likewise provided to highlight the advantages and innovations of our proposal.

Online dating platforms help to connect people who might potentially be a good match for each other. They have exerted a significant societal impact over the last decade, such that about one third of new relationships in the US are now started online, for instance. Recommender Systems are widely utilized in online platforms that connect people to people in e.g. online dating and recruitment sites. These recommender approaches are fundamentally different from traditional user-item approaches (such as those operating on movie and shopping sites), in that they must consider the interests of both parties jointly. Latent factor models have been notably successful in the area of user-item recommendation, however they have not been investigated within user-to-user domains as of yet. In this study, we present a novel method for reciprocal recommendation using latent factor models. We also provide a first analysis of the use of different preference aggregation strategies, thereby demonstrating that the aggregation function used to combine user preference scores has a significant impact on the outcome of the recommender system. Our evaluation results report significant improvements over previous nearest-neighbour and content-based methods for reciprocal recommendation, and show that the latent factor model can be used effectively on much larger datasets than previous state-of-the-art reciprocal recommender systems.

Recommender systems have been one of the main methods to overcome the information overload problem in many domains including tourism. A solo traveller may easily create an itinerary according to his or her taste. However, when it comes to a group of people, it is often difficult to find the most suitable places to please everyone's preferences, not only because of lack of prior information about a place but also owing to the difficulty to take influence among group members into account. In our work, we introduce a model for Group Decision Making that uses YouTube API to gather rich video contents associated with a finite set of alternatives, e.g. travel destinations. Instead of eliciting subjective opinions on alternatives directly, preferences are built upon a Collaborative Filtering approach, based on each participant's watch history and interaction with items. Randomly chosen videos from different group members are also recommended to each target user in order to infer trust information within the group. We then use trust information to obtain an aggregated group preference for determining places to visit. An application example based on YouTube API shows that a higher degree of interaction consolidates a trust network, resulting in informed decision results.

Reciprocal Recommender Systems are a class of Recommender System for recommending people to people, where the preference relationship is bidirectional and both sides must be considered in the recommendation process. Reciprocal recommender systems often generate preference values using similar algorithms to traditional recommender systems, and then they combine two preference scores into a single indicator of the compatability between the two users. None of the few existing approaches for reciprocal recommendation have investigated the influence of different aggregation functions in these systems as of yet. Our work concentrates on exploring diverse approaches for aggregating bidirectional preference relations, and presents the results of a comparative study.

Failure mode and effect analysis (FMEA) is an effective risk-management tool, which has been extensively utilized to manage failure modes (FMs) of products, processes, systems, and services. Almost all FMEA models are concerned with how to get a complete risk order of FMs from highest to lowest risk. However, in many situations, it may be sufficient to classify the FMs into several ordinal risk classes. Meanwhile, generating a consensual decision is crucial for the FMEA problem because 1) reaching consensus will enhance the connections among FMEA participants, and 2) a highly accepted group solution to the FMEA problem can be generated. Thus, this study proposes a consensus-based group decision-making framework for FMEA with the aim of classifying FMs into several ordinal risk classes in which we assumed that FMEA participants provide their preferences in a linguistic way using possibilistic hesitant fuzzy linguistic information. In the FMEA framework, a consensus-driven methodology is presented to generate the weights of risk factors. Following this, an optimization-based consensus rule guided by a minimum adjustment distance policy is devised, and an interactive model for reaching consensus is developed to generate consensual FM risk classes. In order to justify its validity of the proposal, our framework is applied for the risk evaluation of proton beam radiotherapy.

Online dating constitutes one out of myriad popular services that can be accessed via the Internet nowadays. This paper introduces a novel detection system for identifying dubious users, i.e. users who utilize a Japanese online dating service for purposes besides dating. Examples of such purposes include sales and multi-level marketing, amongst others. More specifically, the proposed detection is characterized by simultaneously analyzing: (i) user profile data; (ii) user actions over their first few hours; and (iii) data retrieved from Facebook in order to find the likelihood that the user is a spammer. The resulting system successfully detects a number of spammers every day, thereby becoming a valuable tool for the customer service team in Eureka Inc, where it has been deployed.

This paper investigates the problem of measuring consensus in multi-perspective Multi-Criteria Group Decision Making (MCGDM) problems, in which participants have individual views on the relative importance of different evaluation criteria. A novel dual consensus measure for multi-perspective MCGDM problems is introduced. The proposed measure determines the level of agreement between participants' opinions based on: (i) the global performance or satisfaction of alternatives, (ii) their partial performances of alternatives under each criterion, and (iii) the similarity between the perspectives of participants regarding criteria weights. Preliminary experiments are conducted for an example multi-perspective MCGDM scenario. The degree to which global and partial performance information are jointly taken into account - together with the actual pairwise distances between the opinions of participants - are shown to directly affect the overall measurement of consensus in the group. An application example is introduced in a MCGDM problem on selecting the safest logistic route to transport hazardous materials.

Planning is an essential process in teams of multiple agents pursuing a common goal. When the effects of actions undertaken by agents are uncertain, evaluating the potential risk of such actions alongside their utility might lead to more rational decisions upon planning. This challenge has been recently tackled for single agent settings, yet domains with multiple agents that present diverse viewpoints towards risk still necessitate comprehensive decision making mechanisms that balance the utility and risk of actions. In this work, we propose a novel collaborative multi-agent planning framework that integrates (i) a team-level online planner under uncertainty that extends the classical UCT approximate algorithm, and (ii) a preference modeling and multicriteria group decision making approach that allows agents to find accepted and rational solutions for planning problems, predicated on the attitude each agent adopts towards risk. When utilised in risk-pervaded scenarios, the proposed framework can reduce the cost of reaching the common goal sought and increase effectiveness, before making collective decisions by appropriately balancing risk and utility of actions.

Recommender systems aim at filtering large amounts of information for users, providing them with those pieces of information which better meet their preferences or needs. Such systems have been traditionally used in diverse areas, such as e-commerce or tourism. Within this context, group recommender systems address the problem of generating recommendations for groups of users who might have different interests. Although different aggregation processes have been extensively utilized in real-life applications to generate group recommendations, such processes do not guarantee that the list of products recommended to the group reflect a high agreement level among its members' individual preferences. Given the need for considering the added value of obtaining group recommendations under a high agreement level, this paper presents a novel group recommender system methodology that attempts to reach a high level of consensus among individual recommendations of group members. To do this, and inspired by existing group decision-making approaches in the literature, a consensus reaching process is carried out to bring such individual recommendations closer to each other before delivering the group recommendations.

Group decision making problems are characterized by the participation of multiple experts with different points of view, who attempt to find a common solution to a problem composed by a set of alternatives. Such problems are often defined in environments of uncertainty caused by the imprecision and vagueness of information, therefore experts must utilize appropriate information domains to deal with such uncertainty when expressing their preferences, e.g. linguistic information. Usually, in group decision making problems it is necessary to apply a consensus reaching process, in which experts discuss and make their opinions closer to each other, in order to achieve a high level of agreement before making the decision. Nevertheless, in large-scale group decision making problems, where a large group of individuals take part, it is more frequent the existence of certain subgroups with a non-cooperative behavior towards consensus reaching. For this reason, it would be convenient to identify such subgroups and deal with them, so that their behavior does not affect the consensus reaching process negatively. In this contribution, we present an approach based on computing with words and fuzzy set theory, to study the behavior of experts in consensus reaching processes, with the aim of identifying and penalizing the importance weights of those experts whose behavior does not contribute to reach a collective agreement.

Consensus reaching processes as part of solving group decision-making problems attempt to reach a mutual agreement in the group before making a decision. Most consensus models and consensus support systems that are proposed in the literature present some noticeable drawbacks: the need for constant human supervision by experts to guarantee an effective process and the difficulty to manage large groups of experts, which are increasingly common in current decisions and may imply a higher cost and complexity to carry out such processes. In order to overcome these problems, this paper presents a novel consensus support system based on the multiagent system paradigm, which automates and supports consensus reaching processes by providing agents with the necessary degree of autonomy to conduct discussion processes by themselves, with a semisupervised methodology. The main novelty of such a system is the agent semisupervised autonomy approach it incorporates, which lets agents conduct most of the discussion process by themselves and allows them to interact with their corresponding human experts under certain circumstances in which human supervision might be convenient and necessary.

Consensus reaching processes in group decision making attempt to reach a mutual agreement among a group of decision makers before making a common decision. Different consensus models have been proposed by different authors in the literature to facilitate consensus reaching processes. Classical models focus on solving group decision making problems where few decision makers participate. However, nowadays, societal and technological trends that demand the management of larger scales of decision makers, such as e-democracy and social networks, add a new requirement to the solution of consensus-based group decision making problems. Dealing with such large groups implies the need for mechanisms to detect decision makers' noncooperative behaviors in consensus, which might bias the consensus reaching process. This paper presents a consensus model suitable to manage large scales of decision makers, which incorporates a fuzzy clustering-based scheme to detect and manage individual and subgroup noncooperative behaviors. The model is complemented with a visual analysis tool of the overall consensus reaching process based on self-organizing maps, which facilitates the monitoring of the process performance across the time. The consensus model presented is aimed to the solution of consensus processes involving large groups.

In group decision making problems is common the necessity of achieving a consensus before making a decision. Many consensus reaching processes have been introduced in the literature but not many intelligent systems have finally been implemented to deal with such processes. In this contribution an initial prototype of a consensus support system supported on a multi-agent paradigm is presented, showing the system architecture (set of agents, behaviours and relationships) and a preliminary ontology to represent the problem knowledge and used for the agent communication.