Tell Me What You Know

Krebs, Luciana Monteiro; Alvarado, Oscar; Dewitte, Pierre; Ausloos, Jef; Geerts, David; Naudts, Laurens; Verbert, Katrien

doi:10.1145/3290607.3312808

Cited by 13 publications

(1 citation statement)

References 16 publications

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“…As privacy awareness grows and stricter regulations are introduced, user expectations regarding data privacy are changing. For instance, the European General Data Protection Regulation (GDPR) has implications on how recommender systems collect and handle user data [1]. Future recommender systems will need to prioritize privacy policies to remain legally compliant.…”

Section: Introductionmentioning

confidence: 99%

One-Shot Federated Group Collaborative Filtering

Eren

Bhattarai

Solovyev

et al. 2022

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)

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Non-negative matrix factorization (NMF) with missing-value completion is a well-known effective Collaborative Filtering (CF) method used to provide personalized user recommendations. However, traditional CF relies on a privacy-invasive collection of user data to build a central recommender model. One-shot federated learning has recently emerged as a method to mitigate the privacy problem while addressing the traditional communication bottleneck of federated learning. In this paper, we present the first one-shot federated CF implementation, named One-FedCF, for groups of users or collaborating organizations. In our solution, the clients first apply local CF in-parallel to build distinct, client-specific recommenders. Then, the privacypreserving local item patterns and biases from each client are shared with the processor to perform joint factorization in order to extract the global item patterns. Extracted patterns are then aggregated to each client to build the local models via information retrieval transfer. In our experiments, we demonstrate our approach with two MovieLens datasets and show results competitive with the state-of-the-art federated recommender systems at a substantial decrease in the number of communications.

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Section: Introductionmentioning

confidence: 99%

One-Shot Federated Group Collaborative Filtering

Eren

Bhattarai

Solovyev

et al. 2022

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)

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Personalising Explainable Recommendations: Literature and Conceptualisation

Naiseh

Jiang

et al. 2020

Advances in Intelligent Systems and Computing

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Explanations in intelligent systems aim to enhance a users' understandability of their reasoning process and the resulted decisions and recommendations. Explanations typically increase trust, user acceptance and retention. The need for explanations is on the rise due to the increasing public concerns about AI and the emergence of new laws, such as the General Data Protection Regulation (GDPR) in Europe. However, users are different in their needs for explanations, and such needs can depend on their dynamic context. Explanations suffer the risk of being seen as information overload, and this makes personalisation more needed. In this paper, we review literature around personalising explanations in intelligent systems. We synthesise a conceptualisation that puts together various aspects being considered important for the personalisation needs and implementation. Moreover, we identify several challenges which would need more research, including the frequency of explanation and their evolution in tandem with the ongoing user experience.

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What Are People Doing About XAI User Experience? A Survey on AI Explainability Research and Practice

Ferreira

Monteiro

2020

Lecture Notes in Computer Science

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Tell Me What You Know

Cited by 13 publications

References 16 publications

One-Shot Federated Group Collaborative Filtering

One-Shot Federated Group Collaborative Filtering

Personalising Explainable Recommendations: Literature and Conceptualisation

What Are People Doing About XAI User Experience? A Survey on AI Explainability Research and Practice

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