Textual semantics and machine learning methods for data product pricing

TL;DR

This paper explores how textual features influence data product pricing using various text representations and machine learning models, revealing that semantic features impact prices differently across categories and improving model interpretability.

Contribution

It introduces a comprehensive framework combining text representations, machine learning, and interpretability techniques to analyze data product pricing based on descriptive text.

Findings

Word2Vec representations outperform others for continuous price prediction.

Bag-of-Words and TF-IDF excel in price-tier classification.

Semantic features related to healthcare and demographics increase prices.

Abstract

Reasonable pricing of data products enables data trading platforms to maximize revenue and foster the growth of the data trading market. The textual semantics of data products are vital for pricing and contain significant value that remains largely underexplored. Therefore, to investigate how textual features influence data product pricing, we employ five prevalent text representation techniques to encode the descriptive text of data products. And then, we employ six machine learning methods to predict data product prices, including linear regression, neural networks, decision trees, support vector machines, random forests, and XGBoost. Our empirical design consists of two tasks: a regression task that predicts the continuous price of data products, and a classification task that discretizes price into ordered categories. Furthermore, we conduct feature importance analysis by the mRMR feature selection method and SHAP-based interpretability techniques. Based on empirical data from the AWA Data Exchange, we find that for predicting continuous prices, Word2Vec text representations capturing semantic similarity yield superior performance. In contrast, for price-tier classification tasks, simpler representations that do not rely on semantic similarity, such as Bag-of-Words and TF-IDF, perform better. SHAP analysis reveals that semantic features related to healthcare and demographics tend to increase prices, whereas those associated with weather and environmental topics are linked to lower prices. This analytical framework significantly enhances the interpretability of pricing models.