Sep 3, 2023 · We explore the possibility of pre-trained recommender models that support building recommender systems in new domains, with minimal or no retraining.
Sep 4, 2024 · Inspired by the impact of pre-trained models, we explore the possibility of pre-trained recommender models that support building recommender ...
Instead, we develop a highly generalizable, pre-trained recommender framework that can zero-shot transfer to any new domain with just the user-item interaction ...
Oct 8, 2024 · This paper proposes a novel pre-trained framework for zero-shot cross-domain sequential recommendation without auxiliary information.
This paper proposes QRec, a zero-shot recommendation model which learns cross-domain user/item representations by leveraging the activity quantiles. The ...
Missing: Transferable | Show results with:Transferable
Oct 25, 2024 · We propose a new challenging setting for pre-trained sequential recommender systems: zero-shot cross-domain and cross-application transfer ...
This work explores the possibility of pre-trained recommender models that support building recommender systems in new domains, with minimal or no retraining.
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This paper proposes a novel pre-trained framework for zero-shot cross-domain sequential recommendation without auxiliary information.
This work shows that PrepRec, without any auxiliary information, can not only zero-shot transfer to a new domain, but achieve competitive performance ...
Sep 8, 2023 · This paper from UIUC explores the concept of pre-trained recommender models (PRMs) for enhancing the efficiency of neural collaborative filtering (NCF) ...