Learning Personalized Page Content Ranking Using Customer Representation

TL;DR

This paper presents a deep learning-based causal bandit algorithm that personalizes e-commerce content ranking by leveraging historical customer behavior, significantly improving relevance metrics like nDCG.

Contribution

It introduces a novel deep learning bandit model that incorporates individual shopping history and latent goals for personalized content ranking.

Findings

Achieved 12.08% nDCG lift in content relevance

Utilized customer behavior features for improved personalization

Demonstrated effectiveness over traditional models

Abstract

On E-commerce stores, there are rich recommendation content to help shoppers shopping more efficiently. However given numerous products, it's crucial to select most relevant content to reduce the burden of information overload. We introduced a content ranking service powered by a linear causal bandit algorithm to rank and select content for each shopper under each context. The algorithm mainly leverages aggregated customer behavior features, and ignores single shopper level past activities. We study the problem of inferring shoppers interest from historical activities. We propose a deep learning based bandit algorithm that incorporates historical shopping behavior, customer latent shopping goals, and the correlation between customers and content categories. This model produces more personalized content ranking measured by 12.08% nDCG lift.