Preferences Of Group Members Research Articles

A Fairness Group Recommendation Algorithm Based On User Activity

In the process of group recommendation, due to the different preferences of group members, the recommendation results cannot meet the needs of all users. How to maximize the fairness of group recommendation is still a challenge. Therefore, this paper proposes a group recommendation algorithm based on user activity. Firstly, a group discovery algorithm based on item cluster preference was used to mine potential groups. Secondly, considering the dynamic change of activity, a sliding time window is designed to investigate the recent activity of each member in the group at the time of subgroup division, and the group is divided into active subgroup and inactive subgroup. Finally, the group recommendation list was generated by aggregating the subgroup preferences by average consensus. Experimental results on the public dataset show that compared with the AGREE algorithm, the recommendation accuracy and coverage of the proposed algorithm are improved by 2.1% and 2.9%, respectively. By focusing on the preference needs of inactive users, the proposed algorithm effectively improves the recommendation satisfaction and group fairness.

Methods of preference aggregation in group recommender systems

The rapid growth of data volumes has led to information overload, which impedes informed decision-making. To solve this problem, recommender systems have emerged that analyze user preferences and offer relevant products on their own. One type of recommender system is group recommender systems, which are designed to facilitate collaborative decision-making, increase user engagement, and promote diversity and inclusion. However, these systems face challenges such as accommodating diverse group preferences and maintaining transparency in recommendation processes. In this study, we propose a method for aggregating preferences in group recommendation systems to retain as much information as possible from group members and improve the accuracy of recommendations. The proposed method provides recommendations to groups of users by avoiding the aggregation process in the first steps of recommendation, which preserves information throughout the group recommendation process and delays the aggregation step to provide accurate and diverse recommendations. When the object of a collaborative filtering-based recommender system is not a single user but a group of users, the strategy for calculating similarity between individual users to find similarity should be adapted to avoid aggregating the preferences of group members in the first step. In the proposed model, the nearest neighbors of a group of users are searched, so the method of finding neighbors is adapted to compare individual users with the group profile. An experimental study has shown that the proposed method achieves a satisfactory balance between accuracy and diversity. This makes it well suited for providing recommendations to large groups in situations where accuracy is more or less important compared to diversity. These results support the assumption that retaining all information from group members without using aggregation techniques can improve the performance of group recommender systems, taking into account various features.

A Point-of-Interest Recommender System for Tourist Groups Based on Cooperative Location Set Cover Problem

Trip recommendation for groups of tourists (TRGT) is a challenging task in tourism since many tourists travel in groups, inducing social interaction and bringing various social benefits. However, TRGT must address various real-life constraints such as limited time for touring, cost, etc. TRGT aims to design personalized tours that meet the preferences of all group members by addressing a variety of tourists’ requirements that may sometimes result in conflicts and stress for the group members. TRGT should satisfy that both the preferences of group members need to be achieved as much as possible and the preferences of group members need to be achieved as evenly as possible. In this paper, we present a methodology for tackling the TRGT problem by reducing it to the Cooperative Location Set Cover Problem (CLSCP), formulated as an integer linear program. The CLSCP aims to select a group of facilities that can satisfy, in aggregate, all demand points. To tackle the CLSCP, we present a new method based on detecting frequent patterns. We also demonstrate the efficiency of the proposed methodology by presenting extensive experimental tests.

Kişiselleştirilmiş yönlendirme için deneyime dayalı bir yöntem

Navigation devices that are tailored to the user's preferences offer personalized routes. When multiple users are involved, it can be hard to find a route that suits everyone's preferences and avoid conflicting interests. A decision support system can improve the quality of user decisions. Traditional systems typically consider only the predefined preferences of one user or a group with similar preferences. This study aims to develop a decision support system for a group of people with diverse preferences, using a method that considers their experiences regarding time and space. The method utilizes IoT, agent-based modeling, multi-objective optimization, and crowdsourced data to create a personalized navigation system for a group, such as a family car, that considers each group member's preferences. The study uses simulation to demonstrate how this method can be applied, and it is created using Grasshopper for Rhino and add-ons. The main original contribution of this research is to show how social aspects can be incorporated into personalized navigation systems for a heterogeneous group. The major challenge was the data-sharing policies.

Formation of a spectrum of system-information criteria for the consistency of expert opinions

The article is devoted to the development of a system-information methodology for establishing the consistency of generalized expert opinions. After all, in the process of conducting any research in which expert information is revealed, a paradoxical situation may arise when the indicator of a generalized group opinion, usually obtained based on the “group normalization effect”, is actually determined, but there is no individual opinion that would coincide with it. Moreover, in the context of this publication, we are talking about the consistency of group systems of specialists’ advantages. Under the system of benefits, we mean an ordered series of indicators, characteristics of professional activity or objects of expertise: from more acceptable (important, significant, etc.) to less acceptable. And it is clear that the inconsistency of opinions arises precisely when individual preference systems are aggregated into a group one. It is determined that the consistency of group opinions should be checked in the following areas: 1) for each individual alternative, which is ordered. It is advisable to implement the direction after the introduction into practice of expert research — the methodology and the differential method for establishing a part of the total significance of the compared alternatives; 2) coincidence/non-coincidence of individual systems of preferences of group members; 3) integrally — using the Kendall concordance coefficient; 4) complex. Based on the experience of using a multi-step technology for identifying and screening out marginal thoughts, eliminating the “systematic error of the survivor”, as well as building a “reference” group system of advantages, a set of known and new system-information criteria for the consistency of expert opinions is formulated. We are talking about the requirements for: the probability of the Kendall concordance coefficient, tested using the statistical criterion for testing hypotheses “хі-square”, and its minimum acceptable value, which should be equal to W≥0,7; the need for statistically probable matching of all individual preference systems with group thought in the final group preference system; the need to match the individual system of preferences with the majority opinions of group members etc. Moreover, the last two criteria are implemented based on the Spearman rank correlation coefficient, and their reliability is checked using Student’s t-test. Information about the effectiveness of these criteria is provided.

Gender, beliefs, and coordination with externalities

Groups such as committees or boards make many important decisions within organizations. Many of these decisions affect external parties. This paper uses an experimental approach to study how the gender composition of three-person groups affects choices and beliefs in a Coordination game with selfish and prosocial equilibria. We find that the social preferences of group members are a key determinant of the group’s coordination choice. Controlling for social preferences of the group, groups with more women are more likely to make choices that are kinder to external parties. Both men and women believe that women will make kinder choices more frequently. Groups comprised of all men are expected to make 18 percentage points fewer kind choices than groups of all women. Men are also expected to be 9 percentage points less kind than women overall. These results have implications for public policies intended to increase gender diversity and women’s representation on decision-making committees in the corporate sector, in politics, and in academia.

An enhanced hybridized approach for group recommendation via reliable ratings

A group recommender system aim's to provide relevant information to all members of the group. To determine group preferences, the majority of existing studies use aggregation approaches. An aggregation method is a strategy for recommending products to a group by combining the individual preferences of group members. So far, a slew of different types of aggregation algorithms has been discovered. However, they only aggregate one component of the offered ratings (e.g., counts, rankings, high averages), which limits their ability to capture group members' proclivities. This study proposes a novel aggregation method called weighted count that aggregates ratings by providing weights equal to the number of users who provide ratings to an item (location). In addition, the study proposes combining additive utilitarian and weighted count approaches to highlight popular locations on which group members agreed. Experiments on a benchmark check-in dataset demonstrated that the proposed blended technique surpasses the existing methods significantly.

Research on University Innovation and Entrepreneurship Resource Database System Based on SSH2

With the wide application of the Internet, the entrepreneurial resources of colleges and universities have grown at an exponential rate. With the rapid accumulation of this information, it is difficult for students to find what they are interested in from a large amount of information. To accurately recommend innovation and entrepreneurship resources, this paper proposes a recommendation algorithm based on user trust and a probability matrix. After obtaining the user trust data, the PMD (probability matrix decomposition) algorithm is used to complete the trust matrix and normalize it to get the similarity matrix. At the same time, the trust factor between users is added to the calculation process of the posterior probability, and the prediction score is obtained by maximizing the posterior probability. On this basis, the weights of users in the group are normalized, and the weighting strategy based on user interaction is used to integrate the preferences of group members to obtain the final recommendation results. When designing the recommendation system, the web system of the mainstream SSH2 framework is used to design, and the B/S structure of the entrepreneurial resource recommendation system platform is realized. Experimental results show that the proposed system has a higher recommendation quality compared with other recommended algorithms.

A GRASP to solve the multi-constraints multi-modal team orienteering problem with time windows for groups with heterogeneous preferences

Improving the travel experience is a goal of tourist destinations. Tourists demand information and services that help plan and organise the trips adapted to their preferences and resources. Intelligent systems, recommendation systems, and electronic tourist guides can play a decisive role in tourist satisfaction and shaping the offer at the destination. These systems must satisfy the interests of tourists which travel alone or as a group. However, the development of these tools to generate group tourism itineraries is still limited. Group itineraries must also consider individual preferences and the selection of transport modes. The problem associated with the construction of tourist routes is called the Tourist Trip Design Problem. In this work, an extension of the Team Orienteering Problem with Time Windows is developed to model tourism planning. The model considers the construction of group routes, a cost and time limit associated with each participant, the selection of the mode of transport to go from one location to another, and heterogeneous preferences in the group. The model aims to maximise the preferences of group members by considering real and challenging constraints that increase complexity and avoid solutions in polynomial time. This is a novel model, and we generate sets of instances to assess the accuracy of our solution approach. A Greedy Randomised Adaptive Search Procedure is proposed to solve the model. Evaluation criteria based on time, cost, and preferences are used to guide the candidate’s selection in the construction phase. The results provided by our meta-heuristic are compared with those obtained by solving the Mixed Integer Programming formulation with exact solver. Computational results show the effectiveness and efficiency of the proposed approach.

Aggregating user preferences in group recommender systems: A crowdsourcing approach

We present that group recommendations are similar to crowdsourcing, where the responses of different crowd workers are aggregated in the absence of ground truth. With this in mind, we mimic the use of the EM algorithm as in crowdsourcing to aggregate the preferences of group members to estimate group ratings and the expertise levels the group members. Moreover, for the first time in the literature, we cast the problem of estimating group rating as an ordinal classification problem relying on the natural ordering between the ratings, which allows us to define the expertise levels of the members in terms of sensitivity and specificity. In fact, we impose priors on the sensitivity and the specificity scores corresponding to the members, taking a Bayesian approach. We validate the effectiveness of the proposed aggregation method using the CAMRa2011 dataset, which consists of small and established groups, and the MovieLens dataset, which consists of large and random groups.

Predicting personalized grouping and consumption: A collaborative evolution model

With the prevalence of online social groups, the dynamic joint prediction of users’ grouping and consumption behaviors on social network platforms is critical for optimizing social link suggestions and product recommendations. The group influence theory indicates that group norms affect user preference and behavior; however, the individual preferences of group members can also alter group norms. Nevertheless, the problems of how to holistically model dynamic bidirectional influence, existing between individual preferences and group norms, and then, simultaneously predict the users’ grouping and consumption behaviors are still underexplored. In this study, we propose a collaborative evolution and prediction (CEP) model to address the above issues. We associate each social group with a latent group norm vector, and assign each user with a latent individual preference vector. The unobservable interplay between individual preferences and group norms is then modeled according to the underlying group influence theory. Based on these two latent vectors, we design a joint optimization function that incorporates the correlation between grouping and consumption behaviors, to enhance the prediction performance. Through extensive experiments and evolution analysis, we demonstrate the prediction effectiveness and the explanatory power of our CEP model.

Novel automatic group identification approaches for group recommendation

Group recommender systems are specialized in suggesting preferable products or services to a group of users rather than an individual by aggregating personal preferences of group members. In such expert systems, the initial task is to identify groups of similar users via clustering approaches as user groups are usually not predefined. However, clustering users into groups commonly suffer from sparsity, scalability, and complexity problems as the content in the domain proliferate. Moreover, group homogeneity and size are the critical parameters for organizing group members and enhancing their satisfaction. In this study, we propose novel automatic user grouping approaches by constructing a binary decision tree via bisecting k-means clustering for enhanced group formation and group size restriction. Furthermore, we propose adopting a genre-based mapping of user ratings into a tiny and dense vector to represent users, which both improves computation time for constructing the binary decision tree and enables eliminating adverse effects of sparsity. Finally, since the quality of group formation is not only dependent on conforming preferences but also to the demographic harmony among members, we further introduce utilizing similarities based on demographic characteristics along with the genre-based similarities. We propose applying two distinct strategies for small and large groups by decorating the genre-based similarities with demographic properties, which leads to a more homogeneous automatic group formation. Experiments performed on real-world benchmark datasets demonstrate that each proposed method outperforms its traditional rival significantly, and the final proposed method achieves significantly more qualified ranked recommendation lists than the state-of-the-art algorithm.

Softwarized Attention-Based Context-Aware Group Recommendation Technology in Event-Based Industrial Cyber-Physical Systems

Industrial cyber-physical systems are smart systems, which amalgamate the physical processes with computational capabilities to seamlessly capture, monitor and control the entities and scenarios in industrial environments. Among them, event-based industrial cyber-physical systems (EICPSs), such as Meetup and Plancast, have gained rapid developments. EICPSs provide event recommendation service for groups, which alleviates the information overload problem. However, existing group recommendation models in EICPSs focus on how to aggregate the preferences of group members, failing to model the complex and deep influence of contexts on groups. In this article, we propose an attention-based context-aware group event recommendation model (ACGER) in EICPSs. ACGER models the deep, nonlinear influence of contexts on users, groups, and events through multilayer neural networks. Especially, a novel attention mechanism is designed to enable the influence weights of contexts on users/groups change dynamically with the events concerned. Considering that groups may have completely different behavior patterns from group members, we acquire the preference of a group from two perspectives: indirect preference and direct preference. To obtain the indirect preference, we propose a method of aggregating preferences based on attention mechanism. Compared with existing predefined strategies, this method can flexibly adapt the strategy according to the events concerned by the group. To obtain the direct preference, we employ neural networks to learn it from group-event interactions. Furthermore, to make full use of rich user-event interactions in EICPSs, we integrate the context-aware individual recommendation task into ACGER, which enhances the accuracy of learning of user embeddings and event embeddings. Extensive experiments on three real datasets from Meetup and Douban event show that our model ACGER significantly outperforms the state-of-the-art models.

Decision framework of group consensus with hesitant fuzzy linguistic preference relations

Group decision making typically requires eliciting and coordinating the preferences of group members. In this study, the authors develop an overall framework of group consensus in context of hesitant fuzzy linguistic preference relations (HFLPRs). A new notion of distance measure between two HFLPRs is defined, based on which a consensual framework consists of two mechanisms, the automatic consensus reaching and the interactive consensus reaching is presented. Accordingly, they, respectively, design two algorithms to implement these mechanisms. Finally, a group decision problem of green supplier selection is provided to exemplify and verify both of the proposed methods, and a comparative analysis with some related approaches is performed.

Overcoming Data Sparsity in Group Recommendation

It has been an important task for recommender systems to suggest satisfying activities to a group of users in peoples daily social life. The major challenge in this task is how to aggregate personal preferences of group members to infer the decision of a group. In this paper, we propose a novel end-to-end group recommender system named CAGR (short for Centrality-Aware Group Recommender), which takes the Bipartite Graph Embedding Model (BGEM), the self-attention mechanism and Graph Convolutional Networks (GCNs) as basic building blocks to learn group and user representations in a unified way. Specifically, we first extend BGEM to model group-item interactions, and then in order to overcome the sparsity of the interaction data generated by occasional groups, we propose a self-attentive mechanism to represent groups based on the group members. To further alleviate the group data sparsity problem, we propose two model optimization approaches to exploit an and integrate the user-item interaction data. To overcome the sparsity issue of user-item interaction data, we extend GCNs to leverage the social network to enhance user representation learning. We create two large-scale benchmark datasets and conduct extensive experiments on them. The experimental results show the superiority of our proposed CAGR.

Fuzzy Group Decision Making for influence-aware recommendations

Group Recommender Systems are special kinds of Recommender Systems aimed at suggesting items to groups rather than individuals taking into account, at the same time, the preferences of all (or the majority of) members. Most existing models build recommendations for a group by aggregating the preferences for their members without taking into account social aspects like user personality and interpersonal trust, which are capable of affecting the item selection process during interactions. To consider such important factors, we propose in this paper a novel approach to group recommendations based on fuzzy influence-aware models for Group Decision Making. The proposed model calculates the influence strength between group members from the available information on their interpersonal trust and personality traits (possibly estimated from social networks). The estimated influence network is then used to complete and evolve the preferences of group members, initially calculated with standard recommendation algorithms, toward a shared set of group recommendations, simulating in this way the effects of influence on opinion change during social interactions. The proposed model has been experimented and compared with related works.

A Generative Model Approach for Geo-Social Group Recommendation

With the development and prevalence of online social networks, there is an obvious tendency that people are willing to attend and share group activities with friends or acquaintances. This motivates the study on group recommendation, which aims to meet the needs of a group of users, instead of only individual users. However, how to aggregate different preferences of different group members is still a challenging problem: 1) the choice of a member in a group is influenced by various factors, e.g., personal preference, group topic, and social relationship; 2) users have different influences when in different groups. In this paper, we propose a generative geo-social group recommendation model (GSGR) to recommend points of interest (POIs) for groups. Specifically, GSGR well models the personal preference impacted by geographical information, group topics, and social influence for recommendation. Moreover, when making recommendations, GSGR aggregates the preferences of group members with different weights to estimate the preference score of a group to a POI. Experimental results on two datasets show that GSGR is effective in group recommendation and outperforms the state-of-the-art methods.

An enhanced aggregation method considering deviations for a group recommendation

The goal of a group recommendation involves providing appropriate information for all members in a group. Most extant studies use aggregation methods to determine group preferences. An aggregation method is an approach that aggregates individual preferences of group members to recommend items to a group. Previous studies on aggregation methods only consider high averages, counts, and rankings to provide recommendations. However, the most important component of a group recommendation involves ensuring that majority of the members in a group are satisfied with the recommended results. Therefore, it is necessary to consider the deviation as an important element in aggregation methods. The present study involves proposing an upward leveling (UL) aggregation method that considers deviations for group recommendations. The UL recommends items with low deviations and high averages in conjunction with frequency of positive rating counts for group members. Furthermore, the effectiveness of the UL is validated to perform a comparative evaluation with existing aggregation methods by using the normalized discounted cumulative gain (NDCG) and diversity. The results indicate that the UL outperforms all the baselines and that the deviation plays an important role in the aggregation method.

The Role of Conversation in Developing Accurate Political Perceptions: A Multilevel Social Network Approach

We employed social network data from 25 randomly sampled voluntary associations to understand the factors associated with accurate perceptions of candidate preferences of group members. We analyzed relationships at the dyadic level, but also considered the overall accuracy of perceptions by each ego of all alters (“perceptiveness”) and the overall accuracy of perception by all alters of each ego (“explicitness”) regarding candidate preferences using multilevel modeling techniques. We found low levels of accuracy on average, but high variability and differential patterns of prediction of perceptiveness and explicitness. There was a strong, consistent link between the frequency of communication and accuracy both at the dyadic and aggregate levels which was moderated by the homophily of political preferences within the group.

Prioritising alternatives for maintenance of water distribution networks: A group decision approach

Difficulties related to the group decision-making process in the water supply sector, such as physical and economic losses, irrational use of water and maintenance costs, highlight the need to develop procedures to support decisions, reducing unnecessary water use and wastage. This study focused on the rational use of water resources and reduction of water losses, based on the assumption that it is far more economical to develop and improve existing systems rather than build new systems in parallel to the existing one. This study aimed to support a group decision-making process in the maintenance section of a water supply company. A model is proposed consisting of 2 phases, which aggregates individual preferences to achieve a group decision. The first phase is based on the ELECTRE II method, analysing individual preferences, while the second is based on the COPELAND method to aggregate individual preferences. From this model, we developed a software program to prioritise alternatives, simultaneously taking into account subjective and objective criteria, and thereby giving decision makers a clear and comprehensive overview of alternatives, indicating the most suitable alternative based on the preferences of group members from different areas.