Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure
For e-commerce platforms seeking to maximize revenue, this work integrates profitability directly into recommendation, though the approach is incremental.
This paper proposes a value-aware recommendation framework that incorporates revenue contributions into user-item similarity computation, enabling customer segmentation aligned with profitability. Experiments on the UCI Online Retail dataset show the method outperforms baselines in profit-oriented metrics.
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.