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.

语音助手在电子设备中广泛使用，但如何让语音助手主动向用户提供建议尚未得到充分关注。本文针对电子商务领域中的购物产品问题进行了研究，通过识别用户的购物需求，语音助手能够提供产品或交易建议，提升购物体验。通过使用用户的购物历史数据，我们提出了一种基于 Mixture-of-Experts (MoE) 模型的新型特征，能够准确识别购物产品问题，取得了较高的 F1 得分。在实际用户中进行在线评估，我们能够实时识别购物产品问题并向用户推荐购物动作，提高用户加入购物清单的比率。