DIN: Deep Interest Network for Click-Through Rate Prediction
Learning Target Item Aware User Interest Representation for CTR Prediction
Latest articles
AttriBERT - Session-based Product Attribute Recommendation with BERT
Personalized Refinement or Filter Recommendation
Interpretable Attribute-based Action-aware Bandits for Within-Session Personalization in E-commerce
Authors propose Online Personalized Attribute-based Re-ranker (OPAR), a light-weight, within-session personalization approach using multi-arm bandits (MAB). Each arm of the MAB represents an attribute. As users interact with products, the bandit lear...
Luong Attention
Luong Attention is proposed in the paper Effective Approaches to Attention-based Neural Machine Translation with two variants Global Attention Local Attention The proposed NMT model architecture consists of LSTM encoder to encode input sequences ...
Hard, Soft and Doubly Stochastic Attention
For the image captioning task, the paper Show, Attend and Tell: Neural Image Caption Generation with Visual Attention proposes three different attention mechanisms, Stochastic Hard Attention Deterministic Soft Attention Doubly Stochastic Attention...
Bahdanau Attention
The paper Neural Machine Translation by Jointly Learning to Align and Translate formally introduced the concept of attention for the first time. One of the illustrations (shown below) presented in the paper Learning Phrase Representations using RNN E...
ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems
ReLoop
AITM: Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising
ESMM et al. - Part 4
ESM2: Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction
ESMM et al. - Part 3
