Search Results for author: Tsvi Kuflik

Found 5 papers, 0 papers with code

Exploring Values in Museum Artifacts in the SPICE project: a Preliminary Study

no code implementations13 Nov 2023 Nele Kadastik, Thomas A. Pederson, Luis Emilio Bruni, Rossana Damiano, Antonio Lieto, Manuel Striani, Tsvi Kuflik, Alan Wecker

This document describes the rationale, the implementation and a preliminary evaluation of a semantic reasoning tool developed in the EU H2020 SPICE project to enhance the diversity of perspectives experienced by museum visitors.

Addressing the cold start problem in privacy preserving content-based recommender systems using hypercube graphs

no code implementations13 Oct 2023 Noa Tuval, Alain Hertz, Tsvi Kuflik

The initial interaction of a user with a recommender system is problematic because, in such a so-called cold start situation, the recommender system has very little information about the user, if any.

Collaborative Filtering Privacy Preserving +1

Person Entity Profiling Framework: Identifying, Integrating and Visualizing Online Freely Available Entity-Related Information

no code implementations2 Oct 2021 Saeed Amal, Einat Minkov, Tsvi Kuflik

Hence, we can view any CV as a graph of interlinked entities, where nodes are entities and edges are relations between them.

Relation

Towards Algorithmic Transparency: A Diversity Perspective

no code implementations12 Apr 2021 Fausto Giunchiglia, Jahna Otterbacher, Styliani Kleanthous, Khuyagbaatar Batsuren, Veronika Bogin, Tsvi Kuflik, Avital Shulner Tal

As the role of algorithmic systems and processes increases in society, so does the risk of bias, which can result in discrimination against individuals and social groups.

Fairness Management

Graph Based Recommendations: From Data Representation to Feature Extraction and Application

no code implementations5 Jul 2017 Amit Tiroshi, Tsvi Kuflik, Shlomo Berkovsky, Mohamed Ali Kaafar

The proposed approach is domain-independent (demonstrated on data from movies, music, and business recommender systems), and is evaluated using several state-of-the-art machine learning methods, on different recommendation tasks, and using different evaluation metrics.

Recommendation Systems

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