“Make New Friends,but Keep the Old”-Recommending People on Social Networking Sites Jilin...
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![Page 1: “Make New Friends,but Keep the Old”-Recommending People on Social Networking Sites Jilin Chen,Werner Geyer,Casey Dugan,Michael Muller,Ido Guy CHI 2009.](https://reader031.fdocuments.net/reader031/viewer/2022032521/56649d5d5503460f94a3ce68/html5/thumbnails/1.jpg)
“Make New Friends ,but Keep the Old”-Recommending People on
Social Networking Sites
Jilin Chen ,Werner Geyer ,Casey Dugan ,Michael Muller ,Ido Guy
CHI 2009
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Outline
• Introduction• Data Set• Algorithm • Experiment– Personalized survey – Controlled field study
• Discussion & Conclusion
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Introduction
• Users in online social network site has two type of friends – Already known offline– New friends they discover on the site
• There are many personalized-recommended algorithms , but the effective of those approach is not available
• It is different from traditional recommendations of books, movie, restaurants, etc.
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Introduction
• Goal– Effectiveness of different algorithms– The characteristics of recommending known
versus unknown people– If the recommender system effectively increase
the number of friends a user has– Overall impact of a recommender system on the
site
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Data Set
• online social network site : Beehive within IBM• Start time: July 2008• Network situation in experiment: 38000 users,
average of 8.2 friends per user.• Friend type: Non-reciprocal friendship
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Data Set(Beehive)
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Algorithms
• People recommendation algorithms– Content matching• Explanation: common keywords
– Content-plus-link(CplusL)• Explanation: common keywords & directional links
– Friend-of-Friend(FoF)• Explanation: common friend list
– SONAR • Explanation: all relation in database of IBM
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Algorithm-Content matching
• Motivation : If we both post content on similar topics, we might be interested in getting to know each other.
• Formulation(similarity of two users) :
• Relationship explanation : show up 10 highest scores words.
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Algorithm-Content plus link
• Motivation: By disclosing a network path to a weak tie or unknown person, recipient may be more likely to accept it.
• Link rule(3 and 4 path):
• Similarity scores: if valid link exits ,boost 50% • Relationship explanation : show up 10 highest
scores plus valid links if it exits.
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Algorithm-Friend of friend
• Motivation : If many of my friends consider Alice a friend, perhaps Alice could be my friend too.
• Formulation:
• Score : Number of Mutual friends.• Relationship explanation : show up all mutual
friends.
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Algorithm-SONAR
• SONAR system : Aggregates social relationship information from public data sources within IBM– Organization chart – Publication database– Patent database– Friending system – People tagging system– Project wiki– Blogging system
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Experiment :Personalized survey
• Methodology:– 500 active users – Every user was exposed to all four algorithms
• Top 10 recommendations of four algorithms
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Experiment :Personalized survey
• For each recommendation , we show a photo, the job title and the work location ,as well as the explanation generated by a algorithm.
• User answer following Question for the test.
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Experiment :Personalized survey
• User also answer more general questions like their interest in meeting people on the site.
• 415 logged in and 230 valid survey form.• Results-Understand user’s need– 95% of the user considered people
recommendations to be useful and would like to see them as a feature on the site.
– 61.6% said they are interested in meeting new people , 31% said maybe and 7.4% say no.
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Experiment :Personalized survey
– What may make people to connect to unknown person : 75.2% chose common friends , 74.4% said common content, 39.2% indicated geographical location of the person, 27% said the division within IBM, and 14.5% chose “other”.
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Experiment :Personalized survey
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Experiment :Personalized survey
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Experiment :Controlled field study
• Methodology:– 3000 users– Divide into 5 groups, each with 600 users.4
experiment with one algorithm, 1 control group that did not get any recommendations.
– In experiment group ,show one recommendation a time, starting from the highest ranked ones.
– In control group, we advertised various friending features and actions.
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Experiment :Controlled field study
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Experiment :Controlled field study
• Valid users: 122 from content matching group, 131 from the content-plus-link group , 157 from the friend-of-friend group, and 210 from the SONAR group.
• Test situation:
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Experiment :Controlled field study
• In contrast to survey, the introduction response is less than 1%– “what is this” let the users feel bothered and
ignore the feature• Impact of people recommendations– In experiment group viewed 13.7% more page
compared to previous time– In control group viewed 24.4% less page
compared to previous time
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Experiment :Controlled field study
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Discussion and conclusion
• The result can show the four algorithm are effective in making people recommendation and increase the number of friends.
• Relationship-based algorithms are better at finding known one ,whereas content similarity algorithms are better at new friends
• To combine the strengths of both type of algorithms, we can initially use R-B algo ,complement them with C-S algo latter.