1 citations · 1 across the 3 of their papers we have counts for
4 papers
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 Long Tail of Context: Does it Exist and Matter?
Konstantin Bauman, Alexey Vasilev, Alexander Tuzhilin
Context has been an important topic in recommender systems over the past two decades. A standard representational approach to context assumes that contextual variables and their st…
Discovering the Graph Structure in the Clustering Results
Evgeny Bauman, Konstantin Bauman
In a standard cluster analysis, such as k-means, in addition to clusters locations and distances between them, it's important to know if they are connected or well separated from e…
One-Class Semi-Supervised Learning: Detecting Linearly Separable Class by its Mean
Evgeny Bauman, Konstantin Bauman
In this paper, we presented a novel semi-supervised one-class classification algorithm which assumes that class is linearly separable from other elements. We proved theoretically t…