Web page recommendation aims to predict the user’s navigation through the help of web usage mining techniques. Currently, researchers focus their attention to develop a web page recommendation algorithm using the well known pattern mining techniques. Here, we have presented a web page recommendation algorithm using weighted sequential patterns and markov model. To mine the weighted sequential pattern, we have modified the prefixspan algorithm incorporating the weightage constraints such as, spending time and recent visiting. Then, the weighted sequential patterns are utilized to construct the recommendation model using the Patricia trie-based tree structure. Finally, the recommendation of the current users is done with the help of markov model that is the probability theory enabling the reasoning and computation as intractable. For experimentation, the synthetic dataset is utilized to analyze the performance of W-Prefixspan algorithm as well as web page recommendation algorithm. From the results, the memory required for the W-prefixSpan algorithm is less than 50% of memory needed for PrefixSpan algorithm.