Query Rewriting for Voice Shopping Null Queries

Abstract

Voice shopping using natural language introduces new challenges related to customer queries, like handling mispronounced, misexpressed, and misunderstood queries. Voice null queries, which result in no offers, have negative impact on customers shopping experience. Query rewriting (QR) attempts to automatically replace null queries with alternatives that lead to relevant results. We present a new approach for pre-retrieval QR of voice shopping null queries. Our proposed QR framework first generates alternative queries using a search index-based approach that targets different potential failures in voice queries. Then, a machine-learning component ranks these alternatives, and the original query is amended by the selected alternative. We provide an experimental evaluation of our approach based on data logs of a commercial voice assistant and an e-commerce website, demonstrating that it outperforms several baselines by more than $22%$. Our evaluation also highlights an interesting phenomenon, showing that web shopping null queries are considerably different, and apparently easier to fix, than voice queries. This further substantiates the use of specialized mechanisms for the voice domain. We believe that our proposed framework, mapping tail queries to head queries, is of independent interest since it can be extended and applied to other domains.