An Ensemble Click Model for Web Document Ranking

Abstract

Annually, web search engine providers spend a lot of money on re-ranking documents in search engine result pages (SERP). Click models provide advantageous information for re-ranking documents in SERPs through modeling interactions among users and search engines. Here, three modules are employed to predict users' clicks on SERPs simultaneously, the first module tries to predict users' click behaviors using <em>Probabilistic Graphical Models</em>, the second module is a <em>Time-series Deep Neural Click Model </em>which predicts users' clicks on documents and finally, the third module is a similarity-based measure which creates a graph of document-query relations and uses <em>SimRank</em> <em>Algorithm</em> to predict the similarity. After running these three simultaneous processes, three click probability values are fed to an MLP classifier as inputs. The MLP classifier learns to decide on top of the three preceding modules, then it predicts a probability value which shows how probable a document is to be clicked by a user. The proposed system is evaluated on the Yandex dataset as a standard click log dataset. The results demonstrate the superiority of our model over the well-known click models in terms of perplexity.