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User Perceived Qualities and Acceptance of Recommender Systems

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Design choices for Central Bank Digital Currency

Bryan Alexander Ford, Fan Zhang, Srdan Capkun, Ari Juels

Central banks around the world are exploring and in some cases even piloting central bank digital currencies (CBDCs). CBDCs promise to realize a broad range of new capabilities, including direct government disbursements to citizens, frictionless consumer p ...

Brookings Institution2020

A Framework for Adaptive User Interface Generation based on User Behavioural Patterns

The concept of adaptivity is crucial in enterprise software systems with a large user base. Adaptive user interfaces (AUI) is an emerging research area that enables customized user experience based on user activities. Most of the existing studies that are ...

IEEE2019

Modeling Multimodal-Multiuser Interactions in Declarative Multimedia Languages

Roberto Gerson De Albuquerque Azevedo

Recent advances in hardware and software technologies have given rise to a new class of human-computer interfaces that both explores multiple modalities and allows for multiple collaborating users. When compared to the development of traditional single-use ...

ASSOC COMPUTING MACHINERY2019

Utility and privacy effects of a user action in a communication system

Rachid Guerraoui, Mahsa Taziki

The present invention concerns a method of determining an effect of a user action taken with respect to an item in a communication network. The item is associated with data content in the communication network. The method comprises: (a) determining (31) pu ...

2019

Unified and Scalable Incremental Recommenders with Consumed Item Packs

Rachid Guerraoui, Rhicheek Patra

Recommenders personalize the web content using collaborative filtering to relate users (or items). This work proposes to unify user-based, item-based and neural word embeddings types of recommenders under a single abstraction for their input, we name Consu ...

Springer2019

Towards Scalable Personalization

Rhicheek Patra

The ever-growing amount of online information calls for Personalization. Among the various personalization systems, recommenders have become increasingly popular in recent years. Recommenders typically use collaborative filtering to suggest the most releva ...

EPFL2018

Can Who-Edits-What Predict Edit Survival?

Matthias Grossglauser, Lucas Maystre, Victor Kristof

As the number of contributors to online peer-production systems grows, it becomes increasingly important to predict whether the edits that users make will eventually be beneficial to the project. Existing solutions either rely on a user reputation system o ...

ACM2018

Diversifying Group Recommendation

Quoc Viet Hung Nguyen, Thành Tâm Nguyên

Recommender-systems has been a significant research direction in both literature and practice. The core of recommender systems are the recommendation mechanisms, which suggest to a user a selected set of items supposed to match user true intent, based on e ...

2018

Investigating users' eye movement behavior in critiquing-based recommender systems

Pearl Pu Faltings, Li Chen, Feng Wang

Recommender systems have increasingly become popular in various web environments (such as e-commerce and social media) for automatically generating items that match to individual users' personal interests. Among different types of recommender systems that ...

Ios Press2017

Learning adaptive dressing assistance from human demonstration

Sylvain Calinon, Emmanuel Pignat

For tasks such as dressing assistance, robots should be able to adapt to different user morphologies, preferences and requirements. We propose a programming by demonstration method to efficiently learn and adapt such skills. Our method encodes sensory info ...

2017