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Friendbook: A Semantic-based Friend Recommendation System for Social Networks

Friendbook: A Semantic-based Friend Recommendation System for Social Networks

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Friendbook: A Semantic-based Friend Recommendation System for Social Networks

†Existing social networking services recommend friends to users based on their social graphs, which may not be the most appropriate to reflect a userís preferences on friend selection in real life. In this paper, we present Friendbook, a novel semantic-based friend recommendation system for social networks, which recommends friends to users based on their life styles instead of social graphs. By taking advantage of sensor-rich smartphones, Friendbook discovers life styles of users from user-centric sensor data, measures the similarity of life styles between users, and recommends friends to users if their life styles have high similarity. Inspired by text mining, we model a userís daily life as life documents, from which his/her life styles are extracted by using the Latent Dirichlet Allocation algorithm. We further propose a similarity metric to measure the similarity of life styles between users, and calculate usersí impact in terms of life styles with a friend-matching graph. Upon receiving a request, Friendbook returns a list of people with highest Recommendation scores to the query user. Finally, Friendbook integrates a feedback mechanism to further improve the recommendation accuracy. We have implemented Friendbook on the Android-based smartphones, and evaluated its performance on both small-scale experiments and large-scale simulations. The results show that the recommendations accurately re?ect the preferences of users in choosing friends.



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  • Model: PROJ5711
  • 999 Units in Stock
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This product was added to our catalog on Tuesday 27 September, 2016.

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