Recommendations with Responsibility Constraints

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 761

Special Issue Editor


E-Mail Website
Guest Editor
Faculty of Information Technology and Communication Sciences (ITC), Tampere University, Kalevantie 4, 33100 Tampere, Finland
Interests: big data management; personalization; recommender systems; entity resolution; data exploration; data analytics; responsible data management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

AI promises to bring significant improvements in people’s lives, accelerating knowledge discovery and innovation. However, lately, there is an increasing concern regarding the lack of diversity (leading to exclusion), fairness (leading to discrimination), and transparency (leading to opacity) of decision-making algorithms, such as recommender systems (RS), raising a call for responsible design systems. This Special Issue on “Recommendations with Responsibility Constraints” focuses on advancing methods and algorithms that promote fairness and transparency in recommender systems. In general, fairness is a broad term, and typically means the fair (non-discriminating, equal, proportional, etc.) allocation of some resources (recommendation utility, exposure, etc.), while explanations make AI systems more transparent and trustworthy.

Topics of interest include, but are not limited to, the following topics related to algorithms: 

  • Fairness-aware recommendations;
  • Fairness-aware explanations for recommendations;
  • Unfairness discovery in recommender systems;
  • Fairness assessment in recommender systems;
  • Fairness correction in recommender systems;
  • Intent-aware recommendations and explanations;
  • Explanations of recommendations;
  • Counterfactual explanations for recommendations;
  • Explanation-based fairness audit for recommendations;
  • Explanation-driven fairness by design;
  • Interactive explanations for recommendations;
  • Guidelines for trustworthy, explainable recommender systems;
  • Auditing for fairness based on explanations in recommender systems;
  • Explanations via visualization in recommender systems;
  • Fairness for different stakeholders in recommender systems;
  • Interactive explanations for providing feedback on recommendations. 

Dr. Kostas Stefanidis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fairness-aware recommendations
  • fairness-aware explanations for recommendations
  • unfairness discovery in recommender systems
  • fairness assessment in recommender systems
  • fairness correction in recommender systems
  • intent-aware recommendations and explanations
  • explanations of recommendations
  • counterfactual explanations for recommendations
  • explanation-based fairness audit for recommendations
  • explanation-driven fairness by design
  • interactive explanations for recommendations
  • guidelines for trustworthy, explainable recommender systems
  • auditing for fairness based on explanations in recommender systems
  • explanations via visualization in recommender systems
  • fairness for different stakeholders in recommender systems
  • interactive explanations for providing feedback on recommendations

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 3670 KiB  
Article
Automated Recommendation of Aggregate Visualizations for Crowdfunding Data
by Mohamed A. Sharaf, Heba Helal, Nazar Zaki, Wadha Alketbi, Latifa Alkaabi, Sara Alshamsi and Fatmah Alhefeiti
Algorithms 2024, 17(6), 244; https://doi.org/10.3390/a17060244 - 6 Jun 2024
Viewed by 301
Abstract
Analyzing crowdfunding data has been the focus of many research efforts, where analysts typically explore this data to identify the main factors and characteristics of the lending process as well as to discover unique patterns and anomalies in loan distributions. However, the manual [...] Read more.
Analyzing crowdfunding data has been the focus of many research efforts, where analysts typically explore this data to identify the main factors and characteristics of the lending process as well as to discover unique patterns and anomalies in loan distributions. However, the manual exploration and visualization of such data is clearly an ad hoc, time-consuming, and labor-intensive process. Hence, in this work, we propose LoanVis, which is an automated solution for discovering and recommending those valuable and insightful visualizations. LoanVis is a data-driven system that utilizes objective metrics to quantify the “interestingness” of a visualization and employs such metrics in the recommendation process. We demonstrate the effectiveness of LoanVis in analyzing and exploring different aspects of the Kiva crowdfunding dataset. Full article
(This article belongs to the Special Issue Recommendations with Responsibility Constraints)
Show Figures

Figure 1

Back to TopTop