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

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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
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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

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  • 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)

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18 pages, 3670 KiB  
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; - 6 Jun 2024
Viewed by 301
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)
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