Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure

TL;DR

This paper introduces a value-aware product recommendation framework that leverages revenue-based customer segmentation and similarity measures to enhance profitability-focused recommendations.

Contribution

It proposes a novel high-dimensional similarity measure and three revenue-oriented recommendation strategies, validated through experiments and real-world data.

Findings

The revenue-based similarity measure improves segmentation accuracy.

The proposed strategies increase expected profit in recommendations.

Validation shows effectiveness on the UCI Online Retail dataset.

Abstract

This paper presents a novel value-aware approach to product recommendation that simultaneously addresses the high dimensionality and sparsity of user-item data while explicitly incorporating the contribution of each product and user to overall sales revenue. The proposed framework encodes revenue contributions in the user-item matrix and computes customer similarity directly on this basis using suitable distance measures. This enables the segmentation of users according to the revenue-based similarity of their purchase baskets and supports recommendations aligned with profitability objectives. We compare conventional similarity metrics with a novel alternative tailored to high-dimensional contexts and propose three recommendation strategies based on revenue share, product popularity, and expected profit generation. The effectiveness of the proposed method is validated through simulation experiments and a real-world application using the UCI Online Retail dataset.