Web search is an interactive process that involves actions from Web search users and responses from the search engine. Many research efforts have been made to address the problem of understanding search behavior in general. Some of this work focused on predicting whether a particular user has succeeded in achieving her search goal or not. Most of these studies have faced the problem of the lack of reliable labeled data to learn from. Unlike labeled data, unlabeled data recording behavioral signals in Web search is widely available in search logs. In this work, we study the plausibility of using labeled and unlabeled data to learn better models of user behavior that can be used to predict search success more effectively. We present a semi-supervised approach to modeling Web search satisfaction. The proposed approach can use either labeled data only or both labeled and unlabeled data. We show that the proposed model out-
performs previous methods for modeling search success using labeled data. We also show that adding unlabeled data improves the effectiveness of the proposed models and that the proposed method outperforms other strong semi-supervised baselines.