Two‑Stage Constrained Actor‑Critic Reinforcement Learning for Short‑Video Recommendation and a Multi‑Task RL Framework

This article presents a two‑stage constrained actor‑critic reinforcement learning algorithm for short‑video recommendation, models the problem as a constrained MDP, details the algorithm’s stages, and reports extensive offline and online experiments showing superior watch‑time and interaction metrics, followed by a multi‑task RL framework and its evaluations.

Recommendation Systemsconstrained optimizationmulti‑task learning

0 likes · 16 min read

Two‑Stage Constrained Actor‑Critic Reinforcement Learning for Short‑Video Recommendation and a Multi‑Task RL Framework

DataFunSummit

Jul 14, 2023 · Artificial Intelligence

Iterative Evolution of JD Search EE System: Adaptive Exploration, Scenario Modeling, Scoring‑Insertion Consistency, and Context‑Aware Brand Store Detection

This article details the multi‑stage evolution of JD's search Explore‑Exploit (EE) system—covering an adaptive dynamic detection model, scenario‑modeling upgrades, end‑to‑end scoring and insertion consistency, and context‑aware brand/store dimension detection—demonstrating how each iteration improves result diversity, user experience, and key online metrics while maintaining search efficiency.

adaptive modelingexplore‑exploite‑commerce

0 likes · 24 min read

DataFunTalk

Jul 10, 2021 · Artificial Intelligence

Multi‑Business Ranking Modeling and Optimization in Meituan Search

This article presents Meituan's multi‑business search ranking system, describing the challenges of mixed‑business queries, the layered architecture, the evolution of multi‑business quota models (MQM‑V1/V2) and multi‑business ranking networks (MBN‑V1‑V4), experimental results, and future research directions.

Deep LearningMeituanmulti‑business modeling

0 likes · 16 min read

Multi‑Business Ranking Modeling and Optimization in Meituan Search

DataFunTalk

Jan 25, 2021 · Artificial Intelligence

Evolution of Zhihu Search Ranking Models: From GBDT to DNN, Multi‑Goal and Context‑Aware LTR

This article reviews the development of Zhihu's search system, describing the transition from early GBDT ranking to deep neural networks, the introduction of multi‑objective and position‑bias‑aware learning‑to‑rank methods, context‑aware techniques, end‑to‑end training, personalization, and future research directions.

DNNDeep LearningGBDT

0 likes · 17 min read