Difference between revisions of "FriendBook"

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

Revision as of 17:51, 14 July 2012

Abstract


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.