This thesis was to fulfill my MPhil degree requirement. It contains a quick review of current click modeling techniques and provides new perspectives: (1) expanding query-document relevance score with a user dimension, hence personalized click models capturing user intrinsic preferences by matrix and tensor factorization; and (2) using previous click models as a micro layer for each user click out of a macro click chain, which includes search click logs for every click-able block on a whole search result page….
We put forward a novel personalized click model to describe user-oriented click preferences, which applies and extends matrix / tensor factorization from the view of collaborative filtering to connect users, queries and documents together. Our model serves as a generalized personification framework that can be incorporated to the previously proposed click models and perhaps to their future expansions. A delightful bonus is the model’s ability to get insights of queries and documents through latent feature vectors, and hence to handle rare and even new query-document pairs, that preceding click models could only take an average value….
When searching for information on a search result page, one is often interacting with an entire page instead of a single block (ads block or organic search block). 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. WPC can achieve significant gain in interpreting the advertisement data….