Core Concepts
Within-basket recommendation involves complex user behaviors, addressed by the Neural Pattern Associator model.
Abstract
The content discusses the challenges of within-basket recommendation and introduces the Neural Pattern Associator (NPA) model to address them. It highlights the importance of modeling user intentions and combination patterns in shopping baskets. The NPA model utilizes Vector Quantized Attention (VQA) modules to identify common user intentions and make coherent recommendations. Evaluation results show significant performance improvement over existing solutions.
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Introduction
- Defines within-basket recommendation as completing a shopping basket with relevant items.
- Early solutions faced scalability issues with growing e-commerce inventories.
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Machine-learning Solutions
- ML-based approaches improved scalability by parameterizing item compatibility.
- Graph-convolution-based models mine complex associations among items.
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User Intentions Modeling
- Reflects multiple shared user intentions in shopping baskets.
- Overlooks complexity of user behaviors like interleaving intents.
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Neural Pattern Associator (NPA)
- Explicitly models combination patterns using VQA modules inspired by vector quantization.
- Achieves weak order sensitivity to model interleaving behavior during shopping sessions.
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Evaluation
- NPA outperforms baseline models on e-commerce and entertainment datasets by 5%-25%.
- Offers self-interpretability in recommendations without post-processing efforts.
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Vector Quantized Attention (VQA)
- Models set-expansion task through conditional distribution for item recommendations.
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Combination Pattern Identification
- Utilizes attention mechanism and VQ learning for stable inference logic.
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Context Estimation
- Estimates context based on identified combination pattern to predict missing components in a basket.
Stats
複数の拡張データセットで提案されたNPAモデルは、既存のWBRソリューションよりも顕著な性能向上を示しています。
Quotes
"The NPA model significantly outperforms a wide range of existing WBR solutions."
"Recent work has shifted its focus toward more practical settings, specifically by incorporating user intentions into the modeling process."