Abstract
AbstractDue to the dynamic and heterogeneous nature of the Web, it is increasingly difficult for users to choose amongst large amounts of data. For this reason, the modelling of users and access to personalised information becomes essential. Recommendation systems stand out as systems that aim to provide the most appropriate and efficient services by offering personalised recommendations to users. Traditional recommendation systems provide suggestions to their users with static approaches and do not include user preferences that change over time in suggestion strategies. In this study, a comprehensive review and comparison of recommendation systems that can adaptively develop suggestion approaches according to changing user preferences and learn user preferences are presented.KeywordsCollaborative filteringClassification techniqueRecommendation systemSocial mediaUser preferences
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