“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

Outline Introduction Data Set Algorithm Experiment – Personalized survey – Controlled field study Discussion & Conclusion

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.

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

Data Set online social network site : Beehive within IBM Start time: July 2008 Network situation in experiment: users, average of 8.2 friends per user. Friend type: Non-reciprocal friendship

Data Set(Beehive)

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

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.

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.

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.

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

Experiment :Personalized survey Methodology: – 500 active users – Every user was exposed to all four algorithms Top 10 recommendations of four algorithms

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.

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.

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”.

Experiment :Personalized survey

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.

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:

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

Experiment :Controlled field study

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.