May 18, 2021 · We develop an algorithm, dubbed ZEro-Shot Recommenders (ZESRec), that is trained on an old dataset and generalize to a new one where there are ...
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In this paper, we explore the possibility of zero-shot learning in RecSys, to enable generalization from an old dataset to an entirely new dataset. We develop, ...
May 15, 2023 · We show that LLMs have promising zero-shot ranking abilities but (1) struggle to perceive the order of historical interactions, and (2) can be biased by ...
Jan 28, 2022 · A novel hierarchical Bayesian model that performs zero-shot recommendation in a target domain where there are neither overlapping users nor overlapping items ...
This work explores the possibility of pre-trained recommender models that support building recommender systems in new domains, with minimal or no retraining.
Zero-shot learning (ZSL) and cold-start recommendation. (CSR) are two challenging problems in computer vision and recommender system, respectively.
In this paper, we explore the possibility of zero-shot learning in RecSys, to enable generalization from an old dataset to an entirely new dataset. We develop, ...
In this paper, we present ZCCR, a Zero-shot Content-based Crossmodal Recommendation System that leverages knowledge from large-scale pretrained Vision-Language ...
Mar 16, 2024 · This work mainly focuses on the ranking stage of recommender systems, since LLMs are more expensive to run on a large-scale candidate set.
Jul 11, 2023 · In this blog post, we'll explore the power of a zero-shot recommendation system that allows you to make quite accurate product recommendations with not that ...
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