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Baobao Algorithm Notes
Baobao Algorithm Notes
Oct 13, 2024 · Artificial Intelligence

Can Hierarchical LLMs Transform Sequential Recommendation? A Deep Dive

This article provides a comprehensive analysis of the HLLM paper, detailing its hierarchical LLM architecture for item and user modeling, the training objectives, fusion strategies, extensive offline and online experiments, scaling behavior, ablation studies, and practical deployment insights in large‑scale recommendation systems.

Industrial DeploymentLLMRecommendation
0 likes · 12 min read
Can Hierarchical LLMs Transform Sequential Recommendation? A Deep Dive
Meituan Technology Team
Meituan Technology Team
Aug 12, 2021 · Artificial Intelligence

Adaptive Information Transfer Multi-task (AITM) Framework for Sequential User Conversion Modeling in Targeted Display Advertising

The Adaptive Information Transfer Multi‑task (AITM) framework integrates multi‑task learning with an attention‑based information‑transfer module to jointly model the sequential conversion chain in targeted display ads, mitigating class imbalance and boosting end‑to‑end user acquisition rates, as demonstrated by offline and online experiments.

AITMMulti-Task LearningSequential Modeling
0 likes · 16 min read
Adaptive Information Transfer Multi-task (AITM) Framework for Sequential User Conversion Modeling in Targeted Display Advertising
DataFunTalk
DataFunTalk
Dec 16, 2019 · Artificial Intelligence

A Comprehensive Overview of Sequential Recommendation Models and Techniques

This article provides an in-depth overview of sequential recommendation, defining the problem, discussing data preparation, and reviewing various neural architectures—including MLP, CNN, RNN, Temporal CNN, self‑attention, and reinforcement‑learning approaches—while offering practical guidance on model selection and implementation.

AttentionCNNRNN
0 likes · 36 min read
A Comprehensive Overview of Sequential Recommendation Models and Techniques