Query Classification with Multi-objective Backoff Optimization

Abstract

E-commerce platforms greatly benefit from high-quality search that retrieves relevant search results in response to search terms. For the sake of search relevance, Query Classification (QC) has been widely adopted to make search engines robust against low text quality and complex category hierarchy. Generally, QC solutions categorize search queries and direct users to the suggested categories whereby the search results are then retrieved. In this way, the search scope is contextually constrained to increase search relevance. However, such operations might risk deteriorating e-commerce metrics when irrelevant categories are suggested. Thus, QC solutions are expected to demonstrate high accuracy. Unfortunately, existing QC methods mainly focus on the intrinsic performance of classifiers whereas fail to consider post-inference optimization that could further improve reliability. To fill up the research gap, we propose the Query Classification with Multi-objective Backoff (QCMB). The proposed solution consists of two steps: 1) hierarchical text classification that classifies search queries into multi-level categories; and 2) multi-objective backoff that substitutes potentially misclassified leaf categories with appropriate ancestors that optimize the trade-off between accuracy and depth. The proposed QCMB is evaluated using the real-world search data of Trade Me that is the largest e-commerce platform in New Zealand. Compared with the benchmarks, QCMB delivers superior solutions with flexible tuning to satisfy different users' demands. To the best of our knowledge, this work is the first attempt to enhance QC with multi-objective optimization.