Click models aim at extracting intrinsic relevance of documents to queries from biased user clicks. One basic modeling assumption made in existing work is to treat such intrinsic relevance as an atomic query-document-specific parameter, which is solely estimated from historical clicks without using any content information about a document or relationship among the clicked/skipped documents under the same query. Due to this overly simplified assumption, existing click models can neither fully explore the information about a document's relevance quality nor make predictions of relevance for any unseen documents. In this work, we proposed a novel Bayesian Sequential State model for modeling the user click behaviors, where the document content and dependencies among the sequential click events within a query are characterized by a set of descriptive features via a probabilistic graphical model. By applying the posterior regularized Expectation Maximization algorithm for parameter learning, we tailor the model to meet specific ranking-oriented properties, e.g., pairwise click preferences, so as to exploit richer information buried in the user clicks. Experiment results on a large set of real click logs demonstrate the effectiveness of the proposed model compared with several state-of-the-art click models. Copyright is held by the International World Wide Web Conference Committee (IW3C2).