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This paper proposes a novel method to improve the accuracy of product searchin e-commerce by utilizing a cluster language model. The method aims to addressthe limitations of the bi-encoder architecture while maintaining a minimaladditional training burden. The approach involves labeling top products foreach query, generating semantically similar query clusters using the K-Meansclustering algorithm, and fine-tuning a global language model into clusterlanguage models on individual clusters. The parameters of each cluster languagemodel are fine-tuned to learn local manifolds in the feature space efficiently,capturing the nuances of various query types within each cluster. The inferenceis performed by assigning a new query to its respective cluster and utilizingthe corresponding cluster language model for retrieval. The proposed methodresults in more accurate and personalized retrieval results, offering asuperior alternative to the popular bi-encoder based retrieval models insemantic search.

Cluster Language Model for Improved E-Commerce Retrieval and Ranking: Leveraging Query Similarity and Fine-Tuning for Personalized Results