Exploratory testing (ET) is a software testing approach that complements automated testing by leveraging business expertise. It has gained momentum over the last decades as it appeals testers to exploit their business knowledge to stress the system under test (SUT). Exploratory tests, unlike automated tests, are defined and executed on-the-fly by testers. Testers who perform exploratory tests may be biased by their past experience and therefore may miss anomalies or unusual interactions proposed by the SUT. This is even more complex in the context of web applications, which typically expose a huge number of interaction paths to their users. As testers of these applications cannot remember all the sequences of interactions they performed, they may fail to deeply explore the application scope. This paper therefore introduces a new approach to assist testers in widely exploring any web application. In particular, our approach monitors the online interactions performed by the testers to suggest in real-time the probabilities of performing next interactions. Looking at these probabilities, we claim that the testers who favour interactions that have a low probability (because they were rarely performed), will increase the diversity of their explorations. Our approach defines a prediction model, based on n-grams, that encodes the history of past interactions and that supports the estimation of the probabilities. Integrated within a web browser extension, it automatically and transparently injects feedback within the application itself. We conduct a controlled experiment and a qualitative study to assess our approach. Results show that it prevents testers to be trapped in already tested loops, and succeeds to assist them in performing deeper explorations of the SUT.