Cross‑Session Aware Temporal Convolutional Network (CA‑TCN) for Session‑Based Recommendation

The article introduces the CA‑TCN model, which combines cross‑session item graphs, a temporal convolutional network, and a session‑context graph to capture both item‑level and session‑level cross‑session influences, achieving state‑of‑the‑art performance on benchmark session‑based recommendation datasets.

Graph Neural NetworkTemporal Convolutional Networkcross-session modeling

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Cross‑Session Aware Temporal Convolutional Network (CA‑TCN) for Session‑Based Recommendation

Meituan Technology Team

Dec 10, 2020 · Artificial Intelligence

Cross‑Session Aware Temporal Convolutional Network (CA‑TCN) for Session‑Based Recommendation

The Cross‑Session Aware Temporal Convolutional Network (CA‑TCN) combines a cross‑session item graph, a dilated temporal convolutional network, and a session‑context graph to capture both global cross‑session signals and positional order, achieving state‑of‑the‑art recommendation performance on benchmarks and slated for deployment in Meituan’s e‑commerce platforms.

Graph Neural NetworkTemporal Convolutional Networkcross-session modeling

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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

21CTO

Feb 24, 2018 · Artificial Intelligence

Why Deep Learning Is Revolutionizing Recommendation Systems

This article explores how deep learning techniques such as item embeddings, autoencoders, Word2Vec, and session‑based neural models are applied to recommendation systems, highlighting their advantages, key architectures, and recent advances from industry and research.

AIRecommendation Systemsautoencoders

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Why Deep Learning Is Revolutionizing Recommendation Systems

Architecture Digest

Feb 22, 2018 · Artificial Intelligence

Deep Learning Applications in Recommendation Systems

This article explains why deep learning has become essential for modern recommendation systems, describing its advantages such as automatic feature extraction, noise robustness, sequential modeling with RNNs, and improved user‑item representation, and reviews major deep‑learning‑based recommendation models and techniques.

Recommendation SystemsWord2Vecautoencoders

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Deep Learning Applications in Recommendation Systems