4 citations · 5 across the 5 of their papers we have counts for
5 papers
Oracle Agent Memory as an Enterprise Memory Substrate for Long-Horizon AI Agents
Richmond Alake, Cesare Bernardis, Paul Cayet +10
Agent memory is a systems problem for long-horizon agents. Practical deployments require retention of task state across extended conversations, recovery of user-specific facts and…
Open Agent Specification (Agent Spec): A Unified Representation for AI Agents
Soufiane Amini, Yassine Benajiba, Cesare Bernardis +16
The proliferation of agent frameworks has led to fragmentation in how agents are defined, executed, and evaluated. Existing systems differ in their abstractions, data flow semantic…
Multi-stage Ensemble Model for Cross-market Recommendation
Cesare Bernardis
This paper describes the solution of our team PolimiRank for the WSDM Cup 2022 on cross-market recommendation. The goal of the competition is to effectively exploit the information…
On the instability of embeddings for recommender systems: the case of Matrix Factorization
Giovanni Gabbolini, Edoardo D'Amico, Cesare Bernardis +1
Most state-of-the-art top-N collaborative recommender systems work by learning embeddings to jointly represent users and items. Learned embeddings are considered to be effective to…
A novel graph-based model for hybrid recommendations in cold-start scenarios
Cesare Bernardis, Maurizio Ferrari Dacrema, Paolo Cremonesi
Cold-start is a very common and still open problem in the Recommender Systems literature. Since cold start items do not have any interaction, collaborative algorithms are not appli…