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Existing social networking services recommend potential friends to users based on their social graphs and web actions. This mechanism, however, may not be the most appropri ate to reflect a user’s preferences on friend selection in real life. In this paper, we present Friendbook, a semantic-based friend recommendation system for social networks. By ex ploiting recent sociology findings, Friendbook identifies and recommends users with similar life styles. Specifically, tak ing the advantage of developments in text mining, Friend book models a user’s daily life as life documents with the frequency of activity information, or bag-of-activity. Friend book then extracts the life style distributions of users from their life documents using the Latent Dirichlet Allocation (LDA) algorithm. Based on these distributions, Friendbook constructs a friend-matching graph that represents users’ life style similarities. When users send queries to Friendbook for friend recommendations, the Friendbook server analyzes the friend-matching graphs, ranks users according to their impact, and sends a list of potential friends in response to the query. To further improve the accuracy of recommen dations, Friendbook integrates a feedback mechanism that takes inputs from users, and dynamically adjusts internal parameters to optimize online performance. We have im plemented Friendbook based on the Android-based Nexus S mobile phones, and evaluated its performance based on data collected from 8 users for a period of three months. The re- sults show that the recommendations accurately reflect the preferences of users in choosing friends.