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