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

TL;DR

This paper introduces a cluster language model approach that enhances e-commerce product search accuracy by leveraging query clustering and fine-tuning, leading to more personalized and precise retrieval results.

Contribution

It presents a novel clustering and fine-tuning method that improves retrieval accuracy without significant additional training overhead.

Findings

Improved retrieval accuracy over bi-encoder models

Enhanced personalization in product search

Efficient local manifold learning in feature space

Abstract

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.