16 citations · 16 across the 6 of their papers we have counts for
7 papers
Seeing the Context: Enhancing Recommender Systems with Image-Derived Contextual Signals
Tal Cordova, Tomer Geva, Moshe Unger
Contextual information, capturing the circumstances of a user-item interaction, is central to recommender systems. Prior work draws context from location, time, or reviews, but not…
Breaking the Information Silo: Semantic Personas for Cross-Domain Recommendation
Jonathan Mayo, Moshe Unger, Konstantin Bauman
Digital platforms increasingly operate as isolated information silos, limiting their ability to construct comprehensive user representations across domains. Cross-domain recommende…
The Impact of LLM-Generated Reviews on Recommender Systems: Textual Shifts, Performance Effects, and Strategic Platform Control
Itzhak Ziv, Moshe Unger, Hilah Geva
The rise of generative AI technologies is reshaping content-based recommender systems (RSes), which increasingly encounter AI-generated content alongside human-authored content. Th…
Multi-View Graph Feature Propagation for Privacy Preservation and Feature Sparsity
Etzion Harari, Moshe Unger
Graph Neural Networks (GNNs) have demonstrated remarkable success in node classification tasks over relational data, yet their effectiveness often depends on the availability of co…
STARE: Predicting Decision Making Based on Spatio-Temporal Eye Movements
Moshe Unger, Alexander Tuzhilin, Michel Wedel
The present work proposes a Deep Learning architecture for the prediction of various consumer choice behaviors from time series of raw gaze or eye fixations on images of the decisi…
Sequence Preserving Network Traffic Generation
Sigal Shaked, Amos Zamir, Roman Vainshtein +4
We present the Network Traffic Generator (NTG), a framework for perturbing recorded network traffic with the purpose of generating diverse but realistic background traffic for netw…
Deep Context-Aware Recommender System Utilizing Sequential Latent Context
Amit Livne, Moshe Unger, Bracha Shapira +1
Context-aware recommender systems (CARSs) apply sensing and analysis of user context in order to provide personalized services. Adding context to a recommendation model is challeng…