Present-day web browsers possess several features that facilitate browsing tasks. Among these features, one of the most useful is the possibility of using tabs. Nowadays, it is very common for web users to use several tabs and to switch from one to another while navigating. Taking into account parallel browsing is thus becoming very important in the frame of web usage mining. Although many studies about web users' navigational behavior have been conducted, few of these studies deal with parallel browsing. This paper is dedicated to such a study. Taking into account parallel browsing involves to have some information about when tab switches are performed in user sessions. However, current browsers do not allow to explicitly acquire such an information, and the data available for web usage mining is usually made of raw navigation logs in which parallel sessions are mixed. Therefore, we propose to get this information in an implicit way. We thus propose the TABAKO model, which is able to detect tab switches in raw navigation logs and to benefit from such a knowledge in order to improve the quality of web recommendations. Experimental studies are performed on an open browsing dataset. Results validate the ability of our algorithm to detect parallel sessions, and to exploit them to enhance the results compared to a state-of-the-art recommendation model.