A Whole Page Click Model to Better Interpret Search Click Data
Authors: Weizhu Chen, Zhanglong Ji, Si Shen, and Qiang Yang
In Proceedings of the 25th AAAI Conference on Artificial Intelligence, San Francisco, CA, USA, 2011.
Recent advances in click modeling have established it as an attractive approach to interpret search click data. These advances characterize users’ search behavior either in advertisement blocks, or within an organic search block through probabilistic models. Yet, when searching for information on a search result page, one is often interacting with the search engine via an entire page instead of a single block. Consequently, previous works that exclusively modeled user behavior in a single block may sacrifice much useful user behavior information embedded in other blocks.
To solve this problem, in this paper, we put forward a novel Whole Page Click (WPC) Model to characterize user behavior in multiple blocks. Specifically, WPC uses a Markov chain to learn the user transition probabilities among different blocks in the whole page. To compare our model with the best alternatives in the Web-Search literature, we run a large-scale experiment on a real dataset and demonstrate the advantage of the WPC model in terms of both the whole page and each block in the page. Especially, we find that WPC can achieve significant gain in interpreting the advertisement data, despite of the sparsity of the advertisement click data.