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The need to identify an approach that recommends items that match users' preferences within social networks has grown in tandem with the increasing number of items appearing within these networks. This research presents a novel technique for item recommendation within social networks that matches user and group interests over time. Users often tag items in social networks with words and phrases that reflect their preferred "vocabulary." As such, these tags provide succinct descriptions of the resource, implicitly reveal user preferences, and, as the tag vocabulary of users tends to change over time, reflect the dynamics of user preferences. Based on evaluation of user and group interests over time, we present a recommendation system employing a modified latent Dirichlet allocation (LDA) model in which users and tags associated with an item are represented and clustered by topics, and the topic-based representation is combined with the item's timestamp to show time-based topic distribution. By representing users via topics, the model can cluster users to reveal the group interests. Based on this model, we developed a recommendation system that reflects user as well as group interests in a dynamic manner that accounts for time, allowing it to perform in a manner superior to that of static recommendation systems in terms of precision rate.