# Learning to Personalize Recommendation based on Customers' Shopping Intents

arXiv: https://arxiv.org/abs/2305.05279 · HTML: https://mlsanity.com/paper/2305.05279/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method](https://mlsanity.com/paper/1402.3722/) (2014)
- [OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]](https://mlsanity.com/paper/1806.01264/) (2018)
- [Semantic Product Search](https://mlsanity.com/paper/1907.00937/) (2019)
- [Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation](https://mlsanity.com/paper/2103.03578/) (2021)
- [AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types](https://mlsanity.com/paper/2006.13473/) (2020)
- [Extreme Multi-label Learning for Semantic Matching in Product Search](https://mlsanity.com/paper/2106.12657/) (2021)
- [OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision](https://mlsanity.com/paper/2204.13874/) (2022)
- [PAM: Understanding Product Images in Cross Product Category Attribute Extraction](https://mlsanity.com/paper/2106.04630/) (2021)
- [Rethinking Personalized Ranking at Pinterest: An End-to-End Approach](https://mlsanity.com/paper/2209.08435/) (2022)
- [Coarse-to-Fine Sparse Sequential Recommendation](https://mlsanity.com/paper/2204.01839/) (2022)
- [Density-Based Dynamic Curriculum Learning for Intent Detection](https://mlsanity.com/paper/2108.10674/) (2021)
- [PGE: Robust Product Graph Embedding Learning for Error Detection](https://mlsanity.com/paper/2202.09747/) (2022)

## Built on by (most cited first, 1 of 1)

- [TRACE: Transformer-based user Representations from Attributed Clickstream Event sequences](https://mlsanity.com/paper/2409.12972/) (2024)

## Abstract (arXiv)

Understanding the customers' high level shopping intent, such as their desire to go camping or hold a birthday party, is critically important for an E-commerce platform; it can help boost the quality of shopping experience by enabling provision of more relevant, explainable, and diversified recommendations. However, such high level shopping intent has been overlooked in the industry due to practical challenges. In this work, we introduce Amazon's new system that explicitly identifies and utilizes each customer's high level shopping intents for personalizing recommendations. We develop a novel technique that automatically identifies various high level goals being pursued by the Amazon customers, such as "go camping", and "preparing for a beach party". Our solution is in a scalable fashion (in 14 languages across 21 countries). Then a deep learning model maps each customer's online behavior, e.g. product search and individual item engagements, into a subset of high level shopping intents. Finally, a realtime ranker considers both the identified intents as well as the granular engagements to present personalized intent-aware recommendations. Extensive offline analysis ensures accuracy and relevance of the new recommendations and we further observe an 10% improvement in the business metrics. This system is currently serving online traffic at amazon.com, powering several production features, driving significant business impacts