Beyond clicks: Measuring attractiveness and satisfaction in e-commerce using Bayesian models with conversion signals

Understanding user behavior is essential for improving user experience and maximizing conversion rates on e-commerce platforms. To more accurately capture user satisfaction, the iLab Click and Conversion Dynamic Bayesian Network (iCCDBN) is introduced, a novel click model that jointly incorporates click and post-click conversion signals. iCCDBN employs separate satisfaction parameters for clicks and conversions, enhancing interpretability while maintaining computational efficiency. The probabilistic formulation of the model is derived, and parameter estimation is carried out using the Expectation-Maximization (EM) algorithm. For evaluation, iCCDBN is compared with established click models on large-scale interaction logs from a real estate marketplace. Results show that iCCDBN, together with strong baselines, achieves the lowest click-through rate prediction errors, with optimal performance observed when (query, item) pairs have at least 60 historical sessions. In satisfaction prediction, iCCDBN surpasses the Dynamic Bayesian Network (DBN) with a lower mean squared error (0.1927 vs. 0.2313). KL divergence analysis further demonstrates that iCCDBN achieves an 8.6% reduction in KL divergence when evaluated on raw prediction scores. When score ranges are normalized via min-max scaling, thereby emphasizing distributional shape rather than scale, the relative improvement increases to 17.4%. These findings highlight the benefits of integrating conversion data and refined behavioral structures into click models, offering a more faithful representation of user satisfaction.