11 research outputs found
The Seven Layers of Complexity of Recommender Systems for Children in Educational Contexts
Recommender systems (RS) in their majority focus on an average target user: adults. We argue that for non-traditional populations in specific contexts, the task is not as straightforward–we must look beyond existing recommendation algorithms, premises for interface design, and standard evaluation metrics and frameworks. We explore the complexity of RS in an educational context for which young children are the target audience. The aim of this position paper is to spell out, label, and organize the specific layers of complexity observed in this context
Retrieving and Recommending for the Classroom: Stakeholders, Objectives, Resources, and Users
In this paper, we consider the promise and challenges of deploying recommendation and information retrieval technology to help teachers locate resources for use in classroom instruction. The classroom setting is a complex environment presenting a number of challenges for recommendation, due to its inherent multi-stakeholder nature, the multiple objectives that quality educational resources and experiences must simultaneously satisfy, and potential disconnect between the direct user of the system and the end users of the resources it provides. In this paper, we outline these challenges, highlight opportunities for new research, and describe our work in progress in this area including insights from interviews with working teachers
A Stakeholder-Centered View on Fairness in Music Recommender Systems
Our narrative literature review acknowledges that, although there is an increasing interest in recommender system fairness in general, the music domain has received relatively little attention in this regard. However, addressing fairness of music recommender systems (MRSs) is highly important because the performance of these systems considerably impacts both the users of music streaming platforms and the artists providing music to those platforms. The distinct needs that these stakeholder groups may have, and the different aspects of fairness that therefore should be considered, make for a challenging research field with ample opportunities for improvement. The review first outlines current literature on MRS fairness from the perspective of each stakeholder and the stakeholders combined, and then identifies promising directions for future research. The two open questions arising from the review are as follows: (i) In the MRS field, only limited data is publicly available to conduct fairness research; most datasets either originate from the same source or are proprietary (and, thus, not widely accessible). How can we address this limited data availability? (ii) Overall, the review shows that the large majority of works analyze the current situation of MRS fairness, whereas only few works propose approaches to improve it. How can we move forward to a focus on improving fairness aspects in these recommender systems? At FAccTRec '22, we emphasize the specifics of addressing RS fairness in the music domain
Fairness in music recommender systems: a stakeholder-centered mini review
The performance of recommender systems highly impacts both music streaming platform users and the artists providing music. As fairness is a fundamental value of human life, there is increasing pressure for these algorithmic decision-making processes to be fair as well. However, many factors make recommender systems prone to biases, resulting in unfair outcomes. Furthermore, several stakeholders are involved, who may all have distinct needs requiring different fairness considerations. While there is an increasing interest in research on recommender system fairness in general, the music domain has received relatively little attention. This mini review, therefore, outlines current literature on music recommender system fairness from the perspective of each relevant stakeholder and the stakeholders combined. For instance, various works address gender fairness: one line of research compares differences in recommendation quality across user gender groups, and another line focuses on the imbalanced representation of artist gender in the recommendations. In addition to gender, popularity bias is frequently addressed; yet, primarily from the user perspective and rarely addressing how it impacts the representation of artists. Overall, this narrative literature review shows that the large majority of works analyze the current situation of fairness in music recommender systems, whereas only a few works propose approaches to improve it. This is, thus, a promising direction for future research
Evaluating Recommender Systems: Survey and Framework
The comprehensive evaluation of the performance of a recommender system is a complex endeavor: many facets need to be considered in configuring an adequate and effective evaluation setting. Such facets include, for instance, defining the specific goals of the evaluation, choosing an evaluation method, underlying data, and suitable evaluation metrics. In this paper, we consolidate and systematically organize this dispersed knowledge on recommender systems evaluation. We introduce the “Framework for EValuating Recommender systems” (FEVR) that we derive from the discourse on recommender systems evaluation. In FEVR, we categorize the evaluation space of recommender systems evaluation. We postulate that the comprehensive evaluation of a recommender system frequently requires considering multiple facets and perspectives in the evaluation. The FEVR framework provides a structured foundation to adopt adequate evaluation configurations that encompass this required multi-facettedness and provides the basis to advance in the field. We outline and discuss the challenges of a comprehensive evaluation of recommender systems, and provide an outlook on what we need to embrace and do to move forward as a research community
Recommended from our members
Innovative food recommendation systems: a machine learning approach
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonRecommendation systems employ users history data records to predict their preference,
and have been widely used in diverse fields including biology, e-commerce, and healthcare.
Traditional recommendation techniques include content-based, collaborative-based and
hybrid methods but not all real-world problems can be best addressed by these classical
recommendation techniques. Food recommendation is one such challenging problem where
there is an urgent need to use novel recommendation systems in assisting people to select
healthy, balanced and personalized food plans. In this thesis, we make several advances in
food recommendation systems using innovative machine learning methods. First, a novel
recommendation approach is proposed by transforming an original recommendation problem
into a many-objective optimisation one that contains several different objectives resulting in
more balanced recommendations. Second, a unified approach to designing sequence-based
personalised food recommendation systems is investigated to accommodate dynamic user
behaviours. Third, a new food recommendation approach is developed with a temporal
dependent graph neural network and data augmentation techniques leading to more accurate
and robust recommendations. The experimental results show that these proposed approaches
have not only provided a more balanced and accurate way of recommending food than the
traditional methods but also led to promising areas for future research
Artificial Intelligence for Online Review Platforms - Data Understanding, Enhanced Approaches and Explanations in Recommender Systems and Aspect-based Sentiment Analysis
The epoch-making and ever faster technological progress provokes disruptive changes and poses pivotal challenges for individuals and organizations. In particular, artificial intelligence (AI) is a disruptive technology that offers tremendous potential for many fields such as information systems and electronic commerce. Therefore, this dissertation contributes to AI for online review platforms aiming at enabling the future for consumers, businesses and platforms by unveiling the potential of AI. To achieve this goal, the dissertation investigates six major research questions embedded in the triad of data understanding of online consumer reviews, enhanced approaches and explanations in recommender systems and aspect-based sentiment analysis
The Challenges of Big Data - Contributions in the Field of Data Quality and Artificial Intelligence Applications
The term "big data" has been characterized by challenges regarding data volume, velocity, variety and veracity. Solving these challenges requires research effort that fits the needs of big data. Therefore, this cumulative dissertation contains five paper aiming at developing and applying AI approaches within the field of big data as well as managing data quality in big data