Personalized Product Search Ranking: A Multi-Task Learning Approach with Tabular and Non-Tabular Data

TL;DR

This paper introduces a multi-task learning model that combines tabular and non-tabular data, including semantic embeddings from TinyBERT, to improve personalized product search ranking performance.

Contribution

It presents a novel architecture integrating diverse data types with a scalable relevance labeling mechanism, advancing personalized ranking methods.

Findings

Enhanced ranking accuracy with combined data and embeddings.

Relevance labels improve model performance.

Fine-tuning TinyBERT boosts semantic understanding.

Abstract

In this paper, we present a novel model architecture for optimizing personalized product search ranking using a multi-task learning (MTL) framework. Our approach uniquely integrates tabular and non-tabular data, leveraging a pre-trained TinyBERT model for semantic embeddings and a novel sampling technique to capture diverse customer behaviors. We evaluate our model against several baselines, including XGBoost, TabNet, FT-Transformer, DCN-V2, and MMoE, focusing on their ability to handle mixed data types and optimize personalized ranking. Additionally, we propose a scalable relevance labeling mechanism based on click-through rates, click positions, and semantic similarity, offering an alternative to traditional human-annotated labels. Experimental results show that combining non-tabular data with advanced embedding techniques in multi-task learning paradigm significantly enhances model performance. Ablation studies further underscore the benefits of incorporating relevance labels, fine-tuning TinyBERT layers, and TinyBERT query-product embedding interactions. These results demonstrate the effectiveness of our approach in achieving improved personalized product search ranking.