Interactivity, Fairness and Explanations in Recommendations

Giannopoulos, Giorgos; Papastefanatos, George; Sacharidis, Dimitris; Stefanidis, Kostas

doi:10.1145/3450614.3462238

Cited by 3 publications

References 29 publications

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Towards adaptive and transparent tourism recommendations: A survey

et al. 2023

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Crowdsourced data streams are popular and extremely valuable in several domains, namely in tourism. Tourism crowdsourcing platforms rely on past tourist and business inputs to provide tailored recommendations to current users in real time. The continuous, open, dynamic and non‐curated nature of the crowd‐originated data demands specific stream mining techniques to support online profiling, recommendation, change detection and adaptation, explanation and evaluation. The sought techniques must, not only, continuously improve and adapt profiles and models; but must also be transparent, overcome biases, prioritize preferences, master huge data volumes and all in real time. This article surveys the state‐of‐art of adaptive and explainable stream recommendation, extends the taxonomy of explainable recommendations from the offline to the stream‐based scenario, and identifies future research opportunities.

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Towards adaptive and transparent tourism recommendations: A survey

et al. 2023

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Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

Verma,

Boonsanong,

Hoang

et al. 2024

ACM Comput. Surv.

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Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders. Explaining, in a human-understandable way, the relationship between the input and output of machine learning models is essential to the development of trustworthy machine learning based systems. A burgeoning body of research seeks to define the goals and methods of explainability in machine learning. In this paper, we seek to review and categorize research on counterfactual explanations , a specific class of explanation that provides a link between what could have happened had input to a model been changed in a particular way. Modern approaches to counterfactual explainability in machine learning draw connections to the established legal doctrine in many countries, making them appealing to fielded systems in high-impact areas such as finance and healthcare. Thus, we design a rubric with desirable properties of counterfactual explanation algorithms and comprehensively evaluate all currently proposed algorithms against that rubric. Our rubric provides easy comparison and comprehension of the advantages and disadvantages of different approaches and serves as an introduction to major research themes in this field. We also identify gaps and discuss promising research directions in the space of counterfactual explainability.

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