In product search, negation is frequently used to articulate unwanted product features or components. Modern search engines often struggle to comprehend negations, resulting in suboptimal user experiences. While various methods have been proposed to tackle negations in search, none of them took the vocabulary gap between query keywords and product text into consideration. In this work, we introduced a query rewriting approach to enhance the performance of product search engines when dealing with queries with negations. First, we introduced a data generation workflow that leverages large language models (LLMs) to extract query rewrites from product text. Subsequently, we trained a Seq2Seq model to generate query rewrite for unseen queries. Our experiments demonstrated that query rewriting yields a 3.17% precision@30 improvement for queries with negations. The promising results pave the way for further research on enhancing the search performance of queries with negations.

In contrast to traditional exhaustive search, selective search first clusters documents into several groups before all the documents are searched exhaustively by a query, to limit the search executed within one group or only a few groups. Selective search is designed to reduce the latency and computation in modern large-scale search systems. In this study, we propose MICO, a Mutual Information CO-training framework for selective search with minimal supervision using the search logs. After training, MICO does not only cluster the documents, but also routes unseen queries to the relevant clusters for efficient retrieval. In our empirical experiments, MICO significantly improves the performance on multiple metrics of selective search and outperforms a number of existing competitive baselines.