Friend Recommendations in Social Networks using Genetic Algorithms

and Network Topology

Jeff Naruchitparames, Mehmet Gunes, Sushil J. Louis

University of Nevada, RenoEvolutionary Computing Systems Lab (ECSL (excel))

http://ecsl.cse.unr.edu (jnaru@cse.unr.edu)

Outline

• Social Networks– Recommend facebook friends

• Approach• Method• Results• Future Work

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?

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

Jeff’s Friends

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

Ten Features (1/2)

1. Number of Shared Friends2. Number of friends in town3. Age Range4. General Interests

1. Number of shared likes, music

5. Common photos1. Number of shared photo tags

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

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

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

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

Performance comparison method

• 100 users• Remove 10 friends• See if system recommends those 10• Track number of friends correctly

recommended

Results

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

While you ask Questions?

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