# Identifying Shopping Intent in Product QA for Proactive Recommendations

arXiv: https://arxiv.org/abs/2404.06017 · HTML: https://mlsanity.com/paper/2404.06017/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 58 references that are in the map.
- First posted on arXiv 9 Apr 2024.

## Builds on (most cited first, 25 of 25)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations](https://mlsanity.com/paper/1706.03847/) (2017)
- [Joint Deep Modeling of Users and Items Using Reviews for Recommendation](https://mlsanity.com/paper/1701.04783/) (2017)
- [Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation](https://mlsanity.com/paper/1607.07326/) (2016)
- [Addressing Complex and Subjective Product-Related Queries with Customer Reviews](https://mlsanity.com/paper/1512.06863/) (2015)
- [Product-Aware Answer Generation in E-Commerce Question-Answering](https://mlsanity.com/paper/1901.07696/) (2019)
- [NoiseQA: Challenge Set Evaluation for User-Centric Question Answering](https://mlsanity.com/paper/2102.08345/) (2021)
- [Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation](https://mlsanity.com/paper/2006.06922/) (2020)
- [Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products](https://mlsanity.com/paper/2105.08956/) (2021)
- [Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa](https://mlsanity.com/paper/1803.04223/) (2018)
- [Review-Driven Answer Generation for Product-Related Questions in E-Commerce](https://mlsanity.com/paper/1905.01994/) (2019)
- [Learning a Fine-Grained Review-based Transformer Model for Personalized Product Search](https://mlsanity.com/paper/2004.09424/) (2020)
- [JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search](https://mlsanity.com/paper/2005.13783/) (2020)
- [Dual Attention Network for Product Compatibility and Function Satisfiability Analysis](https://mlsanity.com/paper/1712.02016/) (2017)
- [The Technological Gap Between Virtual Assistants and Recommendation Systems](https://mlsanity.com/paper/1901.00431/) (2018)
- [Supervised Transfer Learning for Product Information Question Answering](https://mlsanity.com/paper/1901.02539/) (2019)
- [Answering Unanswered Questions through Semantic Reformulations in Spoken QA](https://mlsanity.com/paper/2305.17393/) (2023)
- [Follow-on Question Suggestion via Voice Hints for Voice Assistants](https://mlsanity.com/paper/2310.17034/) (2023)
- [Fast Intent Classification for Spoken Language Understanding](https://mlsanity.com/paper/1912.01728/) (2019)
- [CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment](https://mlsanity.com/paper/2208.13480/) (2022)

## Abstract (arXiv)

Voice assistants have become ubiquitous in smart devices allowing users to instantly access information via voice questions. While extensive research has been conducted in question answering for voice search, little attention has been paid on how to enable proactive recommendations from a voice assistant to its users. This is a highly challenging problem that often leads to user friction, mainly due to recommendations provided to the users at the wrong time. We focus on the domain of e-commerce, namely in identifying Shopping Product Questions (SPQs), where the user asking a product-related question may have an underlying shopping need. Identifying a user's shopping need allows voice assistants to enhance shopping experience by determining when to provide recommendations, such as product or deal recommendations, or proactive shopping actions recommendation. Identifying SPQs is a challenging problem and cannot be done from question text alone, and thus requires to infer latent user behavior patterns inferred from user's past shopping history. We propose features that capture the user's latent shopping behavior from their purchase history, and combine them using a novel Mixture-of-Experts (MoE) model. Our evaluation shows that the proposed approach is able to identify SPQs with a high score of F1=0.91. Furthermore, based on an online evaluation with real voice assistant users, we identify SPQs in real-time and recommend shopping actions to users to add the queried product into their shopping list. We demonstrate that we are able to accurately identify SPQs, as indicated by the significantly higher rate of added products to users' shopping lists when being prompted after SPQs vs random PQs.