Jun 15, 2021 · Artificial Intelligence

Personalized Approximate Pareto-Efficient Recommendation (PAPERec): A Multi‑Objective Reinforcement Learning Framework for User‑Level Objective Personalization

The paper introduces PAPERec, a personalized multi‑objective recommendation framework that leverages Pareto‑oriented reinforcement learning to generate user‑specific objective weights, enabling the model to approximate Pareto‑optimal solutions and achieve superior click‑through rate and dwell‑time performance in both offline and online experiments.

CTRPareto efficiencyRecommendation Systems

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Personalized Approximate Pareto-Efficient Recommendation (PAPERec): A Multi‑Objective Reinforcement Learning Framework for User‑Level Objective Personalization

Alibaba Cloud Developer

Jul 9, 2018 · Artificial Intelligence

How JUMP Boosts Session Click‑Through and Dwell Time with a Triple‑Layer RNN

The paper introduces JUMP, a novel three‑layer RNN architecture that simultaneously predicts click‑through rates and user dwell time in session‑based recommendation scenarios, leveraging a fast‑slow layer to handle short sessions, an attention layer to filter noise, and survival‑analysis‑based modeling of stay duration, achieving superior performance across multiple benchmark datasets.

RNNclick-through ratedwell time

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How JUMP Boosts Session Click‑Through and Dwell Time with a Triple‑Layer RNN