A distance-based aggregation method for finding consensus in preference-approvals

Point of Interest (POI) recommender systems help provide their users with a location or place that they might be interested in visiting. When combined with Location-Based Social Networks (LBSNs), POI recommender systems can be restructured to recommend POI for groups of users and not just individuals. The research focused on Group Recommender Systems (GRSs) and specifically, POI GRSs are scarce when compared to recommender systems for individuals. There are two main techniques that are used for POI GRSs, the Group Profiling methods and the Users Score Aggregation methods. Both methods have their drawbacks, as the Group Profiling methods do not recommend well for new groups and the Users Score Aggregation methods generally do not perform as well as the Group Profiling methods for established groups. In this paper, we propose new result aggregation methods that use both the Group Profiling method and the Users Score Aggregation method's results to provide the best POI group recommendations without the aforementioned drawbacks.

Comparing aggregation methods in large-scale group AHP: Time for the shift to distance-based aggregation

Recommender systems are increasingly used in various domains like movies, travel, music, etc. The rise in social activities has increased the usage of recommender systems in general and group recommender systems in particular. A group recommender system is a system that recommends items to a group of users collectively, given their preferences. In addition to the user preferences, using social and behavioural aspects of group members to generate group recommendations will increase the quality of the content recommended in heterogeneous groups. Group recommender systems also address the cold start problem that arises in an individual recommendation system. This paper presents a survey on the state-of-the-art in group recommender systems concerning various domains. We discussed existing systems with respect to their aggregation and user preference models. This organisation is very useful to understand the intricacies with respect to each domain.

Reproducibility is a challenging aspect that considerably affects the quality of most scientific papers. To deal with this, many open frameworks allow to build, test, and benchmark recommender systems for single users. Group recommender systems involve additional tasks w.r.t. those for single users, such as the identification of the groups, or their modeling. While this clearly amplifies the possible reproducibility issues, to date, no framework to benchmark group recommender systems exists. In this work, we enable reproducibility in group recommender systems by extending the LibRec library, which stands out as one of the richest, with more than 70 different recommender algorithms, good performance and several evaluation metrics. Specifically, we include several approaches for all the stages of group recommender systems: group formation, group modeling strategies, and evaluation. To validate our framework, we consider a use-case that compares several group building, recommendation, and group modeling approaches.

This article presents a comprehensive and objective systematic review of existing research on recommendation systems with regards to core theory, latest studies, various applications, current attitudes, and potential future applications. The research is mainly based on exploring professional peer-reviewed studies and articles and using their abstracts to create a comprehensive and unbiased review of existing research. The following search terms were used to identify articles and studies for the research: recommendation systems; recommender systems; core theory of recommender systems; current attitudes towards recommendation systems; latest studies on recommendation systems; applications of recommendation systems; potential studies on recommendation systems; and future potential applications of recommendation systems. The research also used the advanced search filter to locate recent studies for comparison by limiting the search by year to find studies published from 2021 onwards. Most literature on this area highlights the importance of recommendation systems in almost all aspects of modern life. Specifically, recommendation systems have become critical components in business, health care, education, marketing, and social networking domains. Additionally, most studies identified reinforcement of learning and deep learning techniques as significant developments in the field. These techniques form the backbone of most modern recommendation systems. The primary concern that could hinder further evolution systems is their consequent filter bubble effects which many studies showed to be problematic. Healthcare is a central area that shows tremendous potential for these systems. Although recommender systems have been implemented in this domain, there remains a lot of untapped potential that, if unleashed, could revolutionize medicine and healthcare. But the problems facing these systems have to be tackled first to establish trust. Keywords: Recommendation systems, Recommender systems, Deep learning, Reinforcement learning DOI: 10.7176/CEIS/13-4-04 Publication date: August 31 st 2022

Although researchers have proposed various recommendation systems, most recommendation approaches are for single users and there are only a small number of recommendation approaches for groups.However, TV programs or movies are most often viewed by groups rather than by single users.Most recommendation approaches for groups assume that single users' profiles are known and that group profiles consist of the single users' profiles.However, because it is difficult to obtain group profiles, researchers have only used synthetic or limited datasets.In this paper, we report on various group recommendation approaches to a real largescale dataset in a TV domain, and evaluate the various group recommendation approaches.In addition, we provide some guidelines for group recommendation systems, focusing on home group users in a TV domain.

Hybrid POI group recommender system based on group type in LBSN

The problem ofjustifying democracy arises when a society sees the need for cooperative, collective action. Collective action may be needed to solve coordination problems, public goods problems, Prisoner's Dilemmas, and other structural problems of human interaction or perhaps to realize common ideals of justice in concrete political institutions. Once the need for collective action is established, one must ask how the goals of a cooperative endeavor are to be secured. Cooperation requires that individuals, at least over a specified range of activity, pursue a joint strategy. To assure voluntary compliance in a joint venture, cooperation must be to each person's advantage. However, each person may have an incentive to induce others to cooperate and to defect from the joint strategy in the hope of enjoying the fruits of cooperation without incurring the opportunity costs of compliance. This is the essence of the Prisoner's Dilemma, and considerations of this sort suggest that if collective action is to succeed, policies or strategies formed collectively must be coercively enforceable. Coercion, however, requires justification. Because solving the problem of rational noncompliance requires that collective decisions be coercively enforceable, the rules by which collective decisions are reached require justification. Douglas Rae neatly puts the problem of justification that emerges when political solutions are coercively enforceable as follows: "Once a political community has decided which of its members are to participate directly in the making of collective policy, an important question remains: 'How many of them must agree before a policy is imposed on the community?"" This is essentially the question to which the principle of democratic rule provides an answer: by what process are collective decisions to be made? Answering Rae's question requires a normative framework. We could say that a procedure for making collective decisions is justified if and

We propose a group recommender system considering the recommendation quantity and repeat purchasing by using the existing collaborative filtering algorithm in order to optimize the offline physical store inventories. This research is the first of its kind to consider recommendation quantity and repetitive recommendations when creating group recommender systems. In offline stores, physical limitations result in the ability to display only a limited number of items. Quantity and selection of the item is an important decision for offline stores. In this paper, we suggest applying the user-based recommender system, which is capable of determining the best suited recommendation items for each store. This model is evaluated by the MAE, precision, recall, and F1 measures, and shows higher performance than the baseline model. A new performance evaluation measure is also suggested in this research. New quantity precision, quantity recall, and quantity F1 measures consider a penalty for a shortage or excess of the recommendation quantity. Novelty is defined as the proportion of items that the consumer may not have experienced in the recommendation list. Through the use of this novelty measure, we assess the new profit creation effect of the suggested model. Finally, previous research focused on recommendations for online customers, however, we expanded the recommender system to incorporate offline stores. This research is not only an academic contribution to the marketing field, but also practical contribution to offline stores through the usability of a developed offline shopping algorithm.

This chapter presents a brief and systematic overview of four major advanced recommender systems: group recommender systems, context-aware recommender systems, multi-criteria recommender systems, and cross-domain recommender systems. These advanced recommendations are characterized and compared in a unifying model as extensions of basic recommender systems. Future research topics and directions in the area of advanced personalized recommendations are discussed. Advanced recommender technologies will continue to advance.

This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Politics. Please check back later for the full article.Narrowly understood, social choice theory is a specialized branch of applied logic and mathematics that analyzes abstract objects called preference aggregation functions, social welfare functions, and social choice functions. But more broadly, social choice theory identifies, analyzes, and evaluates rules that may be used to make collective decisions. So understood, social choice is a subfield of the social sciences that examines what may be called “voting rules” of various sorts. While social choice theory typically assumes a finite set of alternatives over which voter preferences are unrestricted, the spatial model of social choice assumes that policy alternatives can be represented by points in a space of one or more dimensions, and that voters have preferences that are plausibly shaped by this spatial structure.Social choice theory has considerable relevance for the study of legislative (as well as electoral) institutions. The concepts and tools of social choice theory make possible formal descriptions of legislative institutions such as bicameralism, parliamentary voting procedures, effects of decision rules (e.g., supramajority vs. simple majority rule and executive veto rules), sincere vs. strategic voting by legislators, agenda control, and other parliamentary maneuvers. Spatial models of social choice further enrich this analysis and raise additional questions regarding policy stability and change. Spatial models are used increasingly to guide empirical research on legislative institutions and processes.

The introduction of cutting-edge technologies has brought about a lot of changes in the healthcare industry. The application of intelligent recommender systems to improve healthcare services is one of the noteworthy developments. In order to provide cutting-edge healthcare services, the research investigates the architecture, application, and possible effects of an Intelligent Healthcare Recommender System (IHRS). The purpose of IHRS development, its architecture, features, difficulties, and potential are all covered in the paper. It is clear from a thorough review that IHRS has enormous potential to transform healthcare delivery by offering effective and individualized suggestions to healthcare practitioners as well as patients. Recommender systems have drawn a lot of interest recently from a variety of industries, including healthcare. Healthcare providers may find it easier to make wise decisions, improve patient outcomes, and provide higher-quality treatment overall with the help of recommender systems. This paper discusses the applications, difficulties, and potential future directions of recommender systems in the healthcare industry. Research examines various recommender system types utilized in the healthcare industry, including hybrid, content-based, and collaborative filtering methods. The research also goes into the significance of ethical, security, and data privacy concerns in the creation and application of healthcare recommender systems. Lastly, it outlines possible future possibilities for this field’s developments and study.

Recommender systems suggest items that might be interesting to a user. To achieve this, rating prediction is the main form of information processing that these systems perform. This article tackles the problem of predicting ratings in a group recommender system by analyzing how system accuracy is influenced by the choice of prediction approach and by a solution that employs the predicted values to avoid data sparsity. The results of more than 100 experiments show that by predicting the ratings for individual users instead of predicting them for groups, and by using these predictions in a system's group detection task, accuracy increases and problems caused by data sparsity are reduced.

In an open source software development environment, it is hard to decide the number of group members required for resolving software issues. Developers generally reply to issues based totally on their domain knowledge and interest, and there are no predetermined groups. The developers openly collaborate on resolving the issues based on many factors, such as their interest, domain expertise, and availability. This study compares eight different algorithms employing machine learning and deep learning, namely—Convolutional Neural Network, Multilayer Perceptron, Classification and Regression Trees, Generalized Linear Model, Bayesian Additive Regression Trees, Gaussian Process, Random Forest and Conditional Inference Tree for predicting group size in five open source software projects developed and managed using an open source development framework GitHub. The social information foraging model has also been extended to predict group size in software issues, and its results compared to those obtained using machine learning and deep learning algorithms. The prediction results suggest that deep learning and machine learning models predict better than the extended social information foraging model, while the best-ranked model is a deep multilayer perceptron((R.M.S.E. sequelize—1.21, opencv—1.17, bitcoin—1.05, aseprite—1.01, electron—1.16). Also it was observed that issue labels helped improve the prediction performance of the machine learning and deep learning models. The prediction results of these models have been used to build an Issue Group Recommendation System as an Internet of Things application that recommends and alerts additional developers to help resolve an open issue.

Investigating and counteracting popularity bias in group recommendations