Semantic De-boosting in e-commerce Query Autocomplete

Abstract: In ecommerce search, query autocomplete plays a critical role to help users in their shopping journey. Often times, query autocomplete presents users with semantically similar queries, which can impede the user's ability to find diverse and relevant results. This paper proposes a novel strategy to enhance this service by refining the presentation of typeahead suggestions based on their semantic similarity. Our solution uniquely demotes semantically equivalent queries using an embedding similarity of query suggestions at runtime. This strategy ensures only distinct and varied queries are prioritized, thereby promoting more diverse suggestions for users. To maintain comprehensive query coverage, we incorporate this deduplication process within the query suggestion reranking step. This approach ensures that the broad spectrum of possible queries remains available to users, while eliminating the redundancy and repetitiveness in the suggestion list. In extending this work, we propose using the distance between query embeddings to offer even more diverse suggestions to users using an algorithm similar to maximal marginal relevance. This approach will further ensure the delivery of non-redundant, unique, and pertinent suggestions to users, thus enriching their search experience. We evaluated our method through rigorous AB testing, demonstrating substantial improvements in key metrics. Notably, we observed a statistically significant rise in the search Add-to-Cart rate, signifying an enhanced user engagement and conversion rate. Furthermore, we observed a statistically significant decrease in clicks to ATC, implying that the feature improved the efficiency of the customer's product search journey. Finally, we also noticed a marked reduction in the null page view rate, indicating the increased pertinence and efficiency of user search sessions.