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Friend Recommendations in Social Networks using Genetic Algorithms and Network Topology

Friend Recommendations in Social Networks using Genetic Algorithms and Network Topology. Jeff Naruchitparames , Mehmet Gunes , Sushil J. Louis University of Nevada, Reno Evolutionary Computing Systems Lab (ECSL (excel)) http://ecsl.cse.unr.edu (jnaru@cse.unr.edu). Outline.

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Friend Recommendations in Social Networks using Genetic Algorithms and Network Topology

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  1. Friend Recommendations in Social Networks using Genetic Algorithms and Network Topology Jeff Naruchitparames, MehmetGunes, Sushil J. Louis University of Nevada, Reno Evolutionary Computing Systems Lab (ECSL (excel)) http://ecsl.cse.unr.edu (jnaru@cse.unr.edu)

  2. Outline • Social Networks • Recommend facebook friends • Approach • Method • Results • Future Work

  3. What is the problem? • Recommend friends on facebook • Customized to each user • Use • Friends of friends • Degree centrality • Pareto Optimal GA • GA identifies useful “social” features • Feature selection • How do we figure out if we are making progress?

  4. Prior Work • Facebook seems to use a friend-of-friends approach. • Analyze friend graphs to find cliques or communities (Kuan) • Filter: GA used to optimize 3 parameters derived from structure of social network. Then filter based on these parameters (Last CEC, Silva) • …more • We also use a filtering approach based on features identified by a pareto-GA

  5. Jeff’s Friends

  6. Approach – Successive filtering • Consider friends of friends (fof) • Add users who have high degree centrality • Degree centrality = deg(v)/n-1 • N is number of vertices • Personalize recommendations based on N social features • Which M features from these N? • N == 10 in this paper • GA chooses M

  7. Ten Features (1/2) • Number of Shared Friends • Number of friends in town • Age Range • General Interests • Number of shared likes, music • Common photos • Number of shared photo tags

  8. Ten Features (2/2) • Number of shared events • Number of shared groups • Number of liked movies • Education • Same school with two year overlap • Number of same: Religion and Politics

  9. Caveats • Preliminary work • 10 features  10 bits  1024 points in search space. That’s easy for exhaustive search! • But we want to • Test approach on a small problem first • Then expand to N >> 10 features

  10. Methodology • Representation • Genetic Algorithm • Selects features to use for filtering • Pareto optimality principles to compare feature sets. Pareto front tells you which feature sets work well • Best combination of features for each central person through Pareto optimality Feature 1  Present, 0 Absent

  11. Pareto Genetic Algorithm • Chromosome fitness is inverse pareto rank times number of friends • Elitist GA, tournament selection • Single point crossover (0.92) • High mutation probability (0.89) • Populations size = 20 • Number of generations = 30 • Results averaged over 3 runs on 100 users

  12. Performance comparison method • 100 users • Remove 10 friends • See if system recommends those 10 • Track number of friends correctly recommended

  13. Results

  14. Conclusions and Future Work • Pareto GA seems to help • Pareto friendships seem promising as a representation • Performance metric • Lots of work left to do • Experiment with GA • Do we really need Pareto GA? • More features • Combinations with other approaches

  15. While you ask Questions? http://ecsl.cse.unr.edu CI in RTS games: Research Assistantships

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