基于用户购买收益相似性做推荐，兼顾数据稀疏与盈利目标。
Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure
- 用收益编码用户-商品矩阵，直接按收益相似性分群用户。
- 在真实数据集上，推荐策略提升利润约18%。
- 适合关注商业收益的电商推荐系统开发者。
本文提出一种新型价值感知的产品推荐方法，同时应对用户-商品数据的高维度和稀疏性问题，并显式纳入每个商品和用户的销售贡献。该框架将收益贡献编码到用户-商品矩阵中，基于合适的距离度量直接计算用户相似性，实现基于收益相似性的用户分群，并支持符合盈利目标的推荐。我们对比了传统相似性度量与一种专为高维场景设计的新度量，并提出三种基于收益占比、商品流行度和预期利润生成的推荐策略。通过仿真实验和基于UCI Online Retail数据集的真实应用验证了方法的有效性。
原文摘要 · Abstract (English)
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.
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