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Recommendation systems aim to make valuable suggestions to users, by taking into consideration their profile, preferences and/or actions throughout interaction with an application or website. A number of studies have presented the use of sequence mining from the web usage dataset that plays an important role for generating web page recommendation to web users. However, it is a big challenge to select effective sequential mining algorithms for discovering the web usage knowledge. The paper presents taxonomy of the existing sequential rule mining algorithms and compares them in tabular form based on the different key features. This paper makes an attempt to give a comparative performance analysis of two excellent algorithms, i.e., RuleGrowth and RuleGen. A web page recommendation system based on sequential rule mining discovered from web usage data has also been presented along with a detailed comparative evaluation. For a given user’s web navigation sequence, the web page recommendation system provides recommendations on the basis of the generated sequential rules. These results are used to pick a suitable sequence mining algorithms for web page recommendation system developers.