Incorporating revisiting behaviors into click models

Click-through behaviors are treated as invaluable sources of user feedback and they have been leveraged in several commercial search engines in recent years. However, estimating unbiased relevance is always a challenging task because of position bias. To solve this problem, many researchers have proposed a variety of assumptions to model click-through behaviors. Most of these models share a common examination hypothesis, which is that users examine search results from the top to the bottom. Nevertheless, this model cannot draw a complete picture of information-seeking behaviors. Many eye-tracking studies find that user interactions are not sequential but contain revisiting patterns. If a user clicks on a higher ranked document after having clicked on a lower-ranked one, we call this scenario a revisiting pattern, and we believe that the revisiting patterns are important signals regarding a user's click preferences. This paper incorporates revisiting behaviors into click models and introduces a novel click model named Temporal Hidden Click Model (THCM). This model dynamically models users' click behaviors with a temporal order. In our experiment, we collect over 115 million query sessions from a widely-used commercial search engine and then conduct a comparative analysis between our model and several state-of-the-art click models. The experimental results show that the THCM model achieves a significant improvement in the Normalized Discounted Cumulative Gain (NDCG), the click perplexity and click distributions metrics.