Modeling Dwell Time to Predict Click-level Satsifaction
The 7th Annual International ACM Conference on Web Search and Data Mining (WSDM 2014) |
Published by ACM
Clicks on search results are the most widely used behavioral signals for predicting search satisfaction. Even though clicks are correlated with satisfaction, they can also be noisy. Previous work has shown that clicks are affected by position bias, caption bias, and other factors. A popular heuristic for reducing this noise is to only consider clicks with long dwell time, usually equaling or exceeding 30 seconds. The rationale is that the more time a searcher spends on a page, the more likely they are to be satisfied with its contents. However, having a single threshold value assumes that users need a fixed amount of time to be satisfied with any result click, irrespective of the page chosen. In reality, clicked pages can differ significantly. Pages have different topics, readability levels, content lengths, etc. All of these factors may affect the amount of time spent by the user on the page. In this paper, we study the effect of different page characteristics on the time needed to achieve search satisfaction. We show that the topic of the page, its length and its readability level are critical in determining the amount of dwell time needed to predict whether any click is associated with satisfaction. We propose a method to model and provide a better understanding of click dwell time. We estimate click dwell time distributions for SAT (satisfied) or DSAT (dissatisfied) clicks for different click segments and use them to derive features to train a click-level satisfaction model. We compare the proposed model to baseline methods that use dwell time and other search performance predictors as features, and demonstrate that the proposed model achieves significant improvements.