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Ranking as a key functionality of Web search engines, is a user-centric process. However, click-through data, which is the source of implicit feedback of users, are not included in almost all of datasets published for the task of ranking. This limitation is also observable in the majority of benchmark datasets prepared for the learning to rank which is a new and promising trend in the information retrieval literature. In this paper, inspiring from the click-through data concept, the notion of click-through features is introduced. Click-through features could be derived from the given primitive dataset even in the absence of click-through data in the utilized benchmark dataset. These features are categorized into three different categories and are either related to the users’ queries, results of searches or clicks of users. With the use of click-through features, in this research, a novel learning to rank algorithm is proposed. By taking …