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Dec 9, 2025 · Product Management

Why AI Product Managers Struggle with Planning: Insights from Real Interviews

The article reveals that many AI product managers can talk about AIGC and agents but stumble when asked to design a rigorous evaluation system, illustrating the problem with a chatbot case study and presenting a detailed 1+3 multi‑dimensional framework to guide product definition, development, and iteration.

AI product evaluationHuman-in-the-Loopadversarial testing

0 likes · 18 min read

Why AI Product Managers Struggle with Planning: Insights from Real Interviews

Ele.me Technology

Oct 27, 2025 · Artificial Intelligence

How IAK Transforms Multi‑Domain Recommendation with Pre‑Training and Fine‑Tuning

This paper introduces IAK, a unified multi‑domain recommendation paradigm that treats the system as a large model, leveraging pre‑training and fine‑tuning with an information‑aware adaptive kernel to capture rapid user interest shifts while reducing training costs and improving online performance.

Recommendation Systemsfine‑tuninginformation bottleneck

0 likes · 18 min read

How IAK Transforms Multi‑Domain Recommendation with Pre‑Training and Fine‑Tuning

Kuaishou Large Model

Sep 24, 2025 · Artificial Intelligence

How Generative Reinforcement Learning is Revolutionizing Real-Time Bidding

The article explains the core challenges of real‑time bidding, reviews Kuaishou's evolution from PID to MPC to reinforcement learning, and introduces generative reinforcement‑learning methods (GAVE and CBD) that combine decision transformers or diffusion models with value‑guided exploration and score‑based RTG, achieving significant offline and online performance gains.

advertising algorithmsdiffusion modelsgenerative reinforcement learning

0 likes · 15 min read

How Generative Reinforcement Learning is Revolutionizing Real-Time Bidding

DataFunSummit

Jan 5, 2025 · Artificial Intelligence

Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)

The article presents a CIKM‑2024 paper that introduces MODRL‑TA, a multi‑objective deep reinforcement learning system combining multi‑objective Q‑learning, a cross‑entropy‑based decision‑fusion algorithm, and a progressive data‑augmentation pipeline to dynamically allocate search traffic on JD.com, with both offline and online experiments showing substantial gains in CTR, CVR, and overall platform performance.

Deep Learningcross-entropy methode‑commerce

0 likes · 14 min read

Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)

DataFunSummit

Dec 27, 2023 · Artificial Intelligence

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

This article presents a two‑stage constrained actor‑critic (TSCAC) algorithm that models short‑video recommendation as a constrained reinforcement‑learning problem, details its theoretical formulation and optimization loss, and validates its superiority through extensive offline and online experiments, followed by a multi‑task reinforcement‑learning framework (RMTL) that further improves multi‑objective recommendation performance.

Recommendation Systemsconstrained optimizationmulti-task learning

0 likes · 16 min read

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

Kuaishou Tech

Apr 27, 2023 · Artificial Intelligence

Two-Stage Constrained Actor‑Critic (TSCAC) for Short‑Video Recommendation

The paper models short‑video recommendation as a constrained Markov decision process and introduces a two‑stage constrained actor‑critic algorithm that jointly maximizes watch time while satisfying multiple interaction constraints, demonstrating superior offline and online performance on the KuaiRand dataset and Kuaishou app.

actor-criticconstrained optimizationoffline evaluation

0 likes · 7 min read

Two-Stage Constrained Actor‑Critic (TSCAC) for Short‑Video Recommendation

Meituan Technology Team

Mar 23, 2023 · Artificial Intelligence

HiNet: A Hierarchical Information Extraction Network for Multi-Scenario Multi-Task Learning in Recommendation Systems

HiNet, a hierarchical information extraction network combining a scenario‑aware attentive layer and a customized gate‑control layer, jointly learns shared and scenario‑specific representations for multiple recommendation tasks, delivering consistently higher CTR and CTCVR performance across six Meituan restaurant scenarios than strong baselines in both offline and online evaluations.

hierarchical networkmulti-task learningonline A/B testing

0 likes · 19 min read

HiNet: A Hierarchical Information Extraction Network for Multi-Scenario Multi-Task Learning in Recommendation Systems

Xiaohongshu Tech REDtech

May 18, 2022 · Artificial Intelligence

Sliding Spectrum Decomposition for Diversified Recommendation in Feed Systems

The paper introduces Sliding Spectrum Decomposition (SSD), a tensor‑based method that quantifies feed diversity through singular‑value volume within sliding windows, integrates quality‑exploration trade‑offs, and employs a hybrid CB2CF model for item embeddings, achieving superior offline and online performance versus DPP in Xiaohongshu’s feed.

DiversityRecommendation Systemsonline A/B testing

0 likes · 10 min read