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cs.IR2026
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…
cs.IR2026
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…
cs.IR2026
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…