Towards Amortized Ranking-Critical Training for Collaborative Filtering | AISC

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Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Published on ● Video Link: https://www.youtube.com/watch?v=cEecLq0EM0g


Duration: 58:42
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Speaker(s): Sam Lobel
Facilitator(s): Susan Shu Chang, Omar Nada

Find the recording, slides, and more info at https://ai.science/e/towards-amortized-ranking-critical-training-for-collaborative-filtering--Vsue1qHMyVeqNEckRcCJ

Motivation / Abstract
Collaborative filtering is widely used in modern recommender systems. In this paper we investigate new methods for training collaborative filtering models based on actor-critic reinforcement learning, to directly optimize the non-differentiable quality metrics of interest. Empirically, we show that the proposed methods outperform several state-of-the-art baselines, including recently-proposed deep learning approaches, on three large-scale real-world datasets.

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