The web has evolved enormously in the past few years resulting in a rapid increase in the number of web users and web pages. Web personalisation has become a challenging task for e-commerce-based companies due to information overload on the web and increase in the number of web users. Recommendation systems generate recommendations to users for various products as per their requirements. Recommendation systems have been widely used in e-commerce websites and have become an integral part of the ecommerce business process. Developers try to customise websites to suit the needs of specific users utilising the knowledge acquired from users' navigational behaviour. The current study focuses on development of a framework for a web recommendation system on the basis of sequential information present in web logs. Rough set-based clustering using similarity upper approximation has been utilised to generate soft clusters to capture the multiple interests of users. The proposed recommendation system can predict the next possible page visit of the user based on the sequential information present in their navigation pattern.