用神经网络提升偏好预测，突破传统线性假设限制。
ConjointNet: Enhancing Conjoint Analysis for Preference Prediction with Representation Learning
- 设计双神经架构，通过表征学习捕捉非线性偏好关系
- 在两个数据集上预测准确率比传统方法高5%以上
- 适合需要精准用户偏好建模的市场研究与产品设计
理解消费者偏好对产品设计和预测市场反应至关重要。基于选择的联合分析广泛用于通过调查中的选择行为建模用户偏好。然而，传统联合估计方法依赖简单线性模型，这一假设可能导致可预测性受限且对产品属性贡献的估计不准确，尤其在存在深层非线性关系的数据上。本文提出 ConjointNet，包含两种新型神经架构，用于预测用户偏好。实验表明，ConjointNet 在两个偏好数据集上的表现优于传统联合估计方法，提升超过5%，并揭示了非线性特征交互的内在机制。
原文摘要 · Abstract (English)
Understanding consumer preferences is essential to product design and predicting market response to these new products. Choice-based conjoint analysis is widely used to model user preferences using their choices in surveys. However, traditional conjoint estimation techniques assume simple linear models. This assumption may lead to limited predictability and inaccurate estimation of product attribute contributions, especially on data that has underlying non-linear relationships. In this work, we employ representation learning to efficiently alleviate this issue. We propose ConjointNet, which is composed of two novel neural architectures, to predict user preferences. We demonstrate that the proposed ConjointNet models outperform traditional conjoint estimate techniques on two preference datasets by over 5%, and offer insights into non-linear feature interactions.
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