Memory Network | BestHub

Youku Technology

Oct 28, 2022 · Artificial Intelligence

Enlarging Long‑time Dependencies via Reinforcement‑Learning‑Based Memory Network for Movie Affective Analysis

The authors introduce a reinforcement‑learning‑driven memory network that augments long‑range dependencies for continuous valence‑arousal emotion prediction in movies, integrating five multimodal features and a DDPG‑based update policy, which yields state‑of‑the‑art performance across multiple affective‑analysis and summarization benchmarks.

Memory NetworkVA affect modellong‑term dependencies

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Enlarging Long‑time Dependencies via Reinforcement‑Learning‑Based Memory Network for Movie Affective Analysis

Youku Technology

Jul 5, 2022 · Artificial Intelligence

Enlarging the Long-time Dependencies via RL-based Memory Network in Movie Affective Analysis

The paper introduces a reinforcement‑learning‑driven memory network that stores and updates historical video information via DDPG, overcoming LSTM/Transformer limitations on long‑duration movie sequences, and achieves state‑of‑the‑art affective prediction on LIRIS‑ACCEDE and related datasets, with real‑world deployments in AI content inspection and film‑element knowledge graphs.

Memory Networklong-term dependenciesmovie affective analysis

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Enlarging the Long-time Dependencies via RL-based Memory Network in Movie Affective Analysis

DataFunSummit

Aug 7, 2021 · Artificial Intelligence

Long-Term User Interest Modeling for Click-Through Rate Prediction in Alibaba's Advertising System

This article describes how Alibaba's advertising team tackled the challenges of modeling long‑term user interests for CTR prediction by co‑designing incremental computation services, introducing memory‑network‑based models (MIMN and HPMN), and achieving significant offline and online performance gains.

CTR predictionLong-Term InterestMachine Learning

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Long-Term User Interest Modeling for Click-Through Rate Prediction in Alibaba's Advertising System

DataFunTalk

May 11, 2020 · Artificial Intelligence

Advances in Click‑Through Rate Prediction: Deep Spatio‑Temporal Networks, Memory Networks, and Feature Expression Learning

This article reviews recent innovations in CTR prediction for an intelligent marketing platform, covering deep spatio‑temporal networks, deep memory networks, and a feature‑expression‑assisted learning framework, with system architecture details, experimental results, and references to KDD and IJCAI papers.

CTR predictionDeep LearningMemory Network

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Advances in Click‑Through Rate Prediction: Deep Spatio‑Temporal Networks, Memory Networks, and Feature Expression Learning

DataFunTalk

Apr 13, 2020 · Artificial Intelligence

Deep Spatio‑Temporal Neural Networks and Memory‑Augmented DNN for Click‑Through Rate Prediction

This article presents the design, challenges, and experimental evaluation of DSTN (with pooling, self‑attention, and interactive‑attention variants) and MA‑DNN models for CTR prediction, highlighting how temporal and contextual ad information improves accuracy and yields significant online gains in large‑scale advertising systems.

CTR predictionDeep LearningKDD

0 likes · 16 min read

Deep Spatio‑Temporal Neural Networks and Memory‑Augmented DNN for Click‑Through Rate Prediction

DataFunTalk

Dec 18, 2019 · Artificial Intelligence

Long-Term User Interest Modeling for Click‑Through Rate Prediction in Alibaba’s Advertising System

This article describes Alibaba‑Mama’s research on improving click‑through rate (CTR) prediction by modeling users’ long‑term interests with incremental computation services, memory‑network architectures (MIMN and HPMN), system redesign (UIC and RTP), and extensive offline and online experiments that demonstrate significant GAUC and CTR gains.

Long-Term InterestMemory Networkadvertising

0 likes · 16 min read