Alexander Felfernig | Graz University of Technology (original) (raw)

Papers by Alexander Felfernig

Group decision-making processes can be supported by group recommender systems that help groups of... more Group decision-making processes can be supported by group recommender systems that help groups of users obtain satisfying decision outcomes. These systems integrate a consensus-achieving process, allowing group members to discuss with each other on the potential items, adapt their opinions accordingly, and achieve an agreement on a selected item. Such a process, therefore, helps to generate group recommendations with a high satisfaction level of group members. Our article provides a rigorous review of the existing consensus approaches to group decision-making. These approaches are classified depending on the applied consensus models such as reference domain where a set of group members or items is selected for calculating consensus measures, coincidence method that calculates the consensus degree between group members depending on the coincidence concept, operators that aggregate user preferences, guidance measures where the consensus-achieving process is guided by different consensus measures, and recommendation generation and individual centrality that enhance the role of a moderator or a leader in the consensus-achieving process. Further consensus techniques for group decision-making in heterogeneous and large-scale groups are also discussed in this article. Besides, to provide an overall landscape of consensus approaches, we also discuss new consensus models in group recommender systems. These models attempt to improve basic aggregation strategies, further consider social relationship interactions, and provide group members with intuitive descriptions regarding the cur

Frontiers in Big Data

Sustainability development goals (SDGs) are regarded as a universal call to action with the overa... more Sustainability development goals (SDGs) are regarded as a universal call to action with the overall objectives of planet protection, ending of poverty, and ensuring peace and prosperity for all people. In order to achieve these objectives, different AI technologies play a major role. Specifically, recommender systems can provide support for organizations and individuals to achieve the defined goals. Recommender systems integrate AI technologies such as machine learning, explainable AI (XAI), case-based reasoning, and constraint solving in order to find and explain user-relevant alternatives from a potentially large set of options. In this article, we summarize the state of the art in applying recommender systems to support the achievement of sustainability development goals. In this context, we discuss open issues for future research.

As an interactive intelligent system, recommender systems are developed to give predictions that ... more As an interactive intelligent system, recommender systems are developed to give predictions that match users preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from the end-user perspective. The field has reached a point where it is ready to look beyond algorithms, into users interactions, decision-making processes and overall experience. Accordingly, the goals of this workshop (IntRS@RecSys) are to explore the human aspects of recommender systems, with a particular focus on the impact of interfaces and interaction design on decision-making and user experiences with recommender systems, and to explore methodologies to evaluate these human aspects of the recommendation process that go beyond traditional automated approaches.

Proceedings of the 16th ACM Conference on Recommender Systems

The FinRec workshop series offers a central forum for the study and discussion of the domain-spec... more The FinRec workshop series offers a central forum for the study and discussion of the domain-specific aspects, challenges, and opportunities of RecSys and other related technologies in the financial services domain. Six years after the second edition of the workshop, the recent advances in the area of personalization and recommendation in financial services fostered the need for a new workshop aiming at bringing together researchers and practitioners working in financial services-related areas. Accordingly, the third edition of the event aims to: (1) understand and discuss open research challenges, (2) provide an overview of existing technologies using recommender systems in the financial services domain, and (3) provide an interactive platform for information exchange between industry and academia.

Proceedings of the 17th ACM Conference on Recommender Systems

Recommender systems (RSs) have undoubtedly played a significant role in addressing the informatio... more Recommender systems (RSs) have undoubtedly played a significant role in addressing the information overload problem by efficiently filtering and suggesting relevant items to users. These systems use both explicit and implicit user preferences to filter available data and suggest items that might align with the user's interests. This can range from recommending movies on a streaming platform based on previous views to suggesting products for purchase based on browsing history. In their early stages, RSs focused on enhancing their algorithmic capabilities to provide accurate recommendations. However, the overemphasis on algorithms resulted in neglecting the human aspect of the user experience. Recognizing this limitation, recent trends in RSs have started to shift their attention toward incorporating Symbiotic Human-Machines Decision Making models. These models aim to provide users with dynamic and persuasive interfaces that empower them to understand and engage better with the recommendations. This shift represents an essential step in creating recommender systems that truly resonate with users and create a more enjoyable, trustable, and user-friendly experience. A crucial aspect of recommender systems' evolution lies in their proactive nature. Early works focused on designing systems that could proactively anticipate user preferences and needs. While this remains a valuable trait, modern RSs also recognize the importance of giving users control and transparency over their recommendations. Striking the right balance between proactivity and user control ensures that the system supports users without being overly intrusive, thus enhancing their overall satisfaction. These aspects are the main discussion topics of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

Fourteenth ACM Conference on Recommender Systems, 2020

As an interactive intelligent system, recommender systems are developed to give recommendations t... more As an interactive intelligent system, recommender systems are developed to give recommendations that match users’ preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from users’ perspectives. The field has reached a point where it is ready to look beyond algorithms, into users’ interactions, decision making processes, and overall experience. The series of workshops on Interfaces and Human Decision Making for Recommender Systems focuses on the ”human side” of recommender systems. The goal of the research stream featured at the workshop is to improve users’ overall experience with recommender systems by integrating different theories of human decision making into the construction of recommender systems and exploring better interfaces for recommender systems. In this summary, we introduce 7th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at RecSys’20, review its history, and discuss most important topics considered at the workshop.

arXiv (Cornell University), Dec 5, 2023

Sports recommender systems receive an increasing attention due to their potential of fostering he... more Sports recommender systems receive an increasing attention due to their potential of fostering healthy living, improving personal well-being, and increasing performances in sport. These systems support people in sports, for example, by the recommendation of healthy and performance-boosting food items, the recommendation of training practices, talent and team recommendation, and the recommendation of specific tactics in competitions. With applications in the virtual world, for example, the recommendation of maps or opponents in e-sports, these systems already transcend conventional sports scenarios where physical presence is needed. On the basis of different working examples, we present an overview of sports recommender systems applications and techniques. Overall, we analyze the related state-of-the-art and discuss open research issues.

Lecture Notes in Computer Science, 2019

Constraint-based recommender systems help users to identify useful objects and services based on ... more Constraint-based recommender systems help users to identify useful objects and services based on a given set of constraints. These decision support systems are often applied in complex domains where millions of possible recommendations exist. One major challenge of constraint-based recommenders is the identification of recommendations which are similar to the user’s requirements. Especially, in cases where the user requirements are inconsistent with the underlying constraint set, constraint-based recommender systems have to identify and apply the most suitable diagnosis in order to identify a recommendation and to increase the user’s satisfaction with the recommendation. Given this motivation, we developed two different approaches which provide similar recommendations to users based on their requirements even when the user’s preferences are inconsistent with the underlying constraint set. We tested our approaches with two real-world datasets and evaluated them with respect to the runtime performance and the degree of similarity between the original requirements and the identified recommendation. The results of our evaluation show that both approaches are able to identify recommendations of similar solutions in a highly efficient manner.

Springer eBooks, 2021

Feature model-based configuration involves selecting desired features from a collection of featur... more Feature model-based configuration involves selecting desired features from a collection of features (called a feature model) that satisfy pre-defined constraints. Configurator development can be performed by different stakeholders with distinct skills and interests, who could also be non-IT domain experts with limited technical understanding and programming experience. In this context, a simple configuration framework is required to facilitate non-IT stakeholders' participation in configurator development processes. In this paper, we develop a so-called tool FM2EXCONF that enables stakeholders to represent configuration knowledge as an executable representation in Microsoft Excel. Our tool supports the conversion of a feature model into an Excel-based configurator, which is performed in two steps. In the first step, the tool checks the consistency and anomalies of a feature model. If the feature model is consistent, then it is converted into a corresponding Excel-based configurator. Otherwise, the tool provides corrective explanations that help stakeholders to resolve anomalies before performing the conversion. Besides, in the second step, another type of explanation (which is included in the Excel-based configurator) is provided to help non-IT stakeholders to fix inconsistencies in the configuration phase.

International Conference on User Modeling, Adaptation, and Personalization, 2015

Constraint-based recommenders support customers in identifying relevant items from complex item a... more Constraint-based recommenders support customers in identifying relevant items from complex item assortments. In this paper we present WeeVis, a constraint-based environment that can be applied in different scenarios in the e-government domain. WeeVis supports collaborative knowledge acquisition for recommender applications in a MediaWiki-based context. This paper shows how Wiki pages can be extended with recommender applications and how the environment uses intelligent mechanisms to support users in identifying the optimal solutions to their needs. An evaluation shows a performance overview with different knowledge bases.

arXiv (Cornell University), Feb 11, 2021

Variability models (e.g., feature models) are a common way for the representation of variabilitie... more Variability models (e.g., feature models) are a common way for the representation of variabilities and commonalities of software artifacts. Such models can be translated to a logical representation and thus allow different operations for quality assurance and other types of model property analysis. Specifically, complex and often large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we introduce DIRECTDEBUG which is a direct diagnosis approach to the automated testing and debugging of variability models. The algorithm helps software engineers by supporting an automated identification of faulty constraints responsible for an unintended behavior of a variability model. This approach can significantly decrease development and maintenance efforts for such models.

Smart innovation, systems and technologies, 2013

The paper presents an overview of the field of recommender systems and describes the current gene... more The paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. The paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multi-criteria ratings, and provision of more flexible and less intrusive types of recommendations.

In group recommender systems,decision manipulation refers to an attack in which a group member ma... more In group recommender systems,decision manipulation refers to an attack in which a group member makes attempts to push his/her favorite options. In this paper, we propose user interfaces to counteract decision manipulation in group recommender systems. The proposed user interfaces visualize information dimensions regarding rating adaptations of group members at different transparency levels. The results show that the user interface at the highest transparency level best helps to discourage users from decision manipulation. Besides, the ability of the user interfaces to counteract decision manipulation differs depending on the dimensions represented in the user interfaces. The information dimensions regarding "\textititem ratings " and "\textitgroup recommendations " have the strongest impacts on preventing users from decision manipulation.

Explanations help users to better understand why a set of items has been recommended. Compared to... more Explanations help users to better understand why a set of items has been recommended. Compared to single user recommender systems, explanations in group recommender systems have further goals. Examples thereof are fairness which helps to take into account as much as possible group members' preferences and consensus which persuades group members to agree on a decision. This paper proposes different explanation types and investigates which explanation best helps to increase the fairness perception, consensus perception, and satisfaction of group members with regard to group recommendations. We conducted a user study to evaluate the proposed explanations. The results show that explanations which take into account preferences of all or the majority of group members achieve the best results in terms of the mentioned aspects. Moreover, there exist positive correlations among these aspects, i.e., as the perceived fairness (or the perceived consensus) of explanations increases, so does the satisfaction of users with regard to group recommendations. In addition, in the context of repeated decisions, the inclusion of group members' satisfaction from previous decisions in the explanations helps to improve the fairness perception of users with regard to group recommendations.

International Joint Conference on Artificial Intelligence, Jul 27, 2015

Constraint-based recommendation systems are well-established in several domains like cars, comput... more Constraint-based recommendation systems are well-established in several domains like cars, computers, and financial services. Such recommendation tasks are based on sets of product constraints and customer preferences. Customer preferences reduce the number of products which are relevant for the customer. In scenarios like that it may happen that the set of customer preferences is inconsistent with the set of constraints in the recommendation system. In order to repair an inconsistency, the customer is informed about possible ways to adapt his/her preferences. There are different possibilities to present this information to the customer: a) via preferred diagnoses, b) via preferred conflicts, and c) via similar products. On the basis of the results of an empirical study we show that diagnoses, conflicts, and similar products are evaluated differently by users in terms of understandability, user satisfaction, and conflict resolution effort.