Behavioral Data Mining to Produce Novel and Serendipitous Friend Recommendations in a Social...

BehavioralDataMiningtoProduceNovelandSerendipitousFriendRecommenda;onsinaSocial

BookmarkingSystem

BarcelonaOctober21st,2015

LudovicoBora8o SalvatoreCartaMa8eoManca

Introduction [email protected]

Social Media Systems (SMS)

Internet based applications: •  public or semi-public

profile; •  a list of other users with

whom they share a connection (Social Network);

•  view and traverse their

list of connections and those made by others within the system;

user-centered design

Allowuserstousekeywords(tags)todescribewebpagesthatareofinterestforthem

Helptoorganizeandsharetheresourceswithotherusersinthenetwork

Intheexample,177DelicioususerssavedthisbookmarkaMeritwassharedbythisuser


SocialBookmarkingSystems

User

Web page

Set of tags


Social Media Systems

user-centered design

Social Interaction Overload problem

h8p://www.personalizemedia.com/garys-social-media-count/


Social Interaction Overload

Social Interaction Overload

Social Recommender Systems: Information filtering in social media systems

Solution

Graph Analysis Content Mining


Social User Recommender Systems classification

+

State of the art

• Systems based on the analysis of social graphs (Example: “People you may know” on Facebook) • Systems that analyze the interactions of the users with the content of the system (Example: TF-IDF) [Chen et al., 2009]

• Hybrid Systems) [Hannon et al., 2010]


SRS - Information Filtering limitations

•  Scalability issues; •  Memory limitations (few features can be

exploited by graph algorithms)

•  Complex algorithms in this specific domain(for example TF-IDF vectors );

•  Problems to update the user preferences in order to produce up-to-date recommendations;

Graph Analysis

Content Mining



Serendipity Problem: Recommended items too similar to those already considered.

Items already considered by the user

Recommended item


follow


• Serendipity Problem • Scalability issues • Memory limitations (few features can be exploited by graph algorithms) • Complex algorithms (for example TF-IDF vectors); • Problems to update the user preferences in order to produce recommendations;

Mining of the user behavior

R1tag1

R2tag2

R3

like

R4

like

Userswithsimilarinterestsusesimilartagsandsavethesamebookmarks

Notehowuserswithsimilarinterestshavesimilartaggingbehaviors

Mineuserbehaviorandexploitthesephenomenaonalargescale,toconnectsimilarusers


IntuiWon

Example: 5 of the 363 users who saved the previous bookmark also saved the following one


Ourproposal• Friendrecommendersystemforthesocialbookmarkingdomain• Mainfeatures:

– UseofalimitedamountofinformaWon• Onlythebookmarkstaggedbyauser• Nosocialgraph

– Miningdonewithalgorithmsspecificallydesignedtooperateinasocialcontext

• Outcome:– ReducedcomputaWonalcost– ExploitthepossibilityofquicklyupdaWngtheinterestsoftheusersfrequently– ImprovedrecommendaWonaccuracy

Algorithm [email protected]

Algorithm

① Tag-baseduserprofiling

② Resource-baseduserprofiling;

③ Tag-basedsimilaritycomputaWon;

④ Userinterest(ui)computaWon;

⑤ RecommendaWonsSelecWon;


Algorithm

①  Tag-baseduserprofiling

②  Resource-baseduserprofiling;

③  Tag-basedsimilaritycomputaWon;

④  UserinterestcomputaWon;

⑤  RecommendaWonsSelecWon;


Vu1 Vu2 Vu3 .. Vun

apple iPhone

apple

tablet

tj = apple

vuj =25

apple

apple

iPad tablet

Tag based user profile

• Eachuserisrepresentedbyavector

• vuiistherelaWvefrequencyofeachtagti∈T(T=setoftagsintheSBS)


Algorithm







Vu1 Vu2 Vu3 .. Vun

vuj =10

!"#

$#

if the resource ri was bookmarked by u otherwise

iPhoneapple Resource r1

Resource r2

Vu1 = 1 Vu2 = 0

Resource based user profile

Each user is represented by a binary vector


Algorithm







vu1 vu2 vu3 vu4 … vun

User u

vm1 vm2 vm3 vm4 … vmn

User m

ts(u,m) =(vui − vu )(vmi − vm )i⊂Tum

∑(vui − vu )

2

i⊂Tum∑ (vmi − vm )

2

i⊂Tum∑

Tag-based user profiles

Pearson’s correlation

UserSimilarityComputaWonStep3–Tag-basedsimilaritycomputaWon


UserSimilarityComputaWonStep3–Tag-basedsimilaritycomputaWon

•  the average value has a strong influence (smallchanges do not influence the coefficient), so it canbecomputedoffline

•  Anefficientalgorithm thatexploitsa support-basedupperboundhasbeendeveloped[Xiongetal.,KDD04]

ts(u,m) =(vui − vu )(vmi − vm )i⊂Tum

∑(vui − vu )

2

i⊂Tum∑ (vmi − vm )

2

i⊂Tum∑


Algorithm







UserinterestComputaWonStep4–Resource-basedsimilaritycomputaWon

percentageofcommonbookmarksUserinterest(ui)towardsanotheruser

ui=%commonbookmarks!ui(u,m)≠ui(m,u)¤  ui(u,m)=(2/4)*100=50%¤  ui(m,u)=(2/2)*100=100%

User u User m b1

b2

b3

b4

b1

b2


Algorithm







Step5–RecommendaWonsselecWon

User User ts ui1 ui2

u1 u2 ts12 ui12 ui21

u1 … … … …

u1 un ts1n ui1n uin1u2 u3 ts23 ui23 ui32

… … … … …

Similarities between each pair of users

CS(ui) = set of users to recommend to ui

everyuj|ts(ui,uj)>αAND

(ui(ui,uj)>βORui(uj,ui)>β)

¨  CombinethepreviouslycomputedsimilariWesinaquickandefficientway¤ ArethesimilariWesaboveathreshold?¤ YesèRanktheuserspersimilarityandrecommend

Experimental Framework [email protected]

Dataset

¨  DeliciousdatasetdistributedfortheHetRec2011workshop

1867Users

69226URLs

7668bi-direcWonaluserrelaWons

53388tags

437593tagassignments[user,tag,URL]

104799bookmarks[user,URL]

¨  Preprocessing:removedusersthatusedlessthan5tagsandlessthen5URLs.


Behavioral analysis

•  the behavior of two users in a social bookmarking system is related both by the use of the tags and by the use of the resources;

•  the use of tags represents a stronger form of connection with respect to the amount of common resources between two users.

Tags

apple pizza mac tech nature

tablet screen

wine

apple

pasta mac car water smart screen

book

resources

R1 R2 R5 R6 R9 R9 R12 R13

R1

R3 R4 R7 R8 R10 R11 R14


Experiments ¨  EvaluaWonoftheaccuracy

¤  Comparisonwithastateoftheartapproach¤ Howmanyrecommenda6onsarecorrect?

¨  Evalua6onofthepercentageofsa6sfiedusers¨  Evalua6onofthenoveltyandserendipity

¤ Howmanyresourcesoftherecommendedusersarenovelorserendipitous?

User u User m


Experiments Evaluation of the accuracy

precision = true positivetrue positive+ false positive

True positive (Correct

recommendations)

¨  Precision: ratio of correct recommendations among all recommendations


Experiments:metricsEvalua6onofthesa6sfiedusers

PercentageofsaWsfiedusers:subsetofuserswhoreceivedacorrectrecommendaWonwithrespecttothesetofthosewhoreceivedarecommendaWon

¨  %saWsfiedusers=#Y/#X*100

X = set of users who received a

recommendation

Y = subset of users who received a

correct recommendation


Experiments Evaluation of the novelty and serendipity

novelty = #∪N(ut )#∪R(ut )

∀ut ∈ Y

R(ui) = Resources of the users ui recommended to ut

N(ut) = R(ui) \ R(ut)

R(ut) =Resources of the target user ut

¨  Novelty: How many recommended items were unknown for the target user that receives the recommendations;

Y is the subset of users for which a correct recommendation was produced


Experiments Evaluation of the novelty and serendipity

serendipity = #∪B(ut )#∪R(ut )

∀ut ∈ Y

R(ui) = Resources of the users ui recommended to ut

N(ut) = R(ui) \ R(ut)

R(ut) =Resources of the target user ut

¨  Serendipity: How surprising the successful recommendations are

Y is the subset of users for which a correct recommendation was produced

B(ut)

Serendipitous resources B(ut) = all resources ri such

that ∀rt ∈ R(ut), sim(ri, rt) < 0.5

Highlydissimilar


Strategy

Everyuj|tb(ui,uj)>αAND

(ui(ui,uj)>βORui(uj,ui)>β)

α= 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

β= 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Experimentsrepeatedwithdifferentvaluesofαandβ

Results [email protected]

Results Evaluation of the accuracy

•  HighersimilariWesleadstomoreaccuraterecommendaWons

•  Whenusershavesimilartaggingbehaviors(ts>0.4)precisionisalmost1,evenwhenuserinterestislow(ui>0.1)

•  Thisisconfirmedbytheexperimentinwhichweavoidconsideringthetag-basedsimilarity(ts=0),whereprecisiondrops

¨  Precisionvalueswithrespecttotheuserinterestui¤  Alineforeachvalueofthetag-

basedsimilarityts


ResultsEvalua6onoftherecommenda6ons

•  N.B:nocoupleofusershasauserinterestui>0.5– Usershavealmost50%ofbookmarksnotsavedbyanyotheruser

–  SpacetoproduceserendipitousfriendrecommendaWons(recommendaWonoffriendswithnovelandunexpectedbookmarks)

¨  Precisionvalueswithrespecttouserinterestui

¨  Alineforeachvalueofthetag-basedsimilarityts


ResultsEvalua6onoftherecommenda6ons

•  Thelineinwhichuserinterestisnotconsidered(ui=0)leadstoalowprecision

•  ThismeansthattheinterestofausertowardsanotheruserhasaninfluenceintheaccuracyoftherecommendaWons

•  EvenasmalluserinterestleadstoveryaccuraterecommendaWons

¨  Precisionvalueswithrespecttotag-basedsimilarityts

¨  Alineforeachvalueoftheuserinterestui


ResultsEvalua6onofthesa6sfiedusers

•  Dividedtheprecisionrangeinto0.1intervals

•  SelectedthevaluethatleadstothehighestpercentageofsaWsfiedusers

¨  Whenmovingfroma0.53toa0.65precision(0.08improvement),theamountofsaWsfiedusersincreasesofalmost20%

¨  Samehappensinthe0.75–0.8range

¨  Theseresultsareextremelyusefulinthedesignofasystem

¨  Takehomemessage:withasmallimprovementintermsofprecisionyoucanstronglyimproveusersaWsfacWon


Results Evaluation of the novelty and serendipity

Interval Precision Novelty Serendipity

[0,0–0,1) 0,03 0,96 0,92

[0,1–0,2) 0,12 0,93 0,81

[0,2–0,3) - - -

[0,3–0,4) 0,36 0,90 0,65

[0,4–0,5) - - -

[0,5–0,6) 0,53 0,89 0,54

[0,6–0,7) 0,65 0,83 0,69

[0,7–0,8) 0,75 0,74 0,59

[0,8–0,9) 0,88 0,79 0,61

[0,9–1) 0,97 0,79 0,53

[1] 1,00 0,67 0,47

¨  Novelty and Serendipity

Conclusions [email protected]

ConclusionsandfutureworkAfriendrecommendersystemforthesocialbookmarkingdomaintolinkuserswithsimilarinterests

reduceduseoftheavailableinformaWonnouseofcomplexalgorithms

Results:HighprecisionevenusingonlythetagsandthebookmarksusedbyusersUserswithsimilartaggingbehaviorsaregoodcandidatesforfriendrecommendaWonsEvenasmallpercentageofsharedbookmarks(userinterest)leadstoveryaccuraterecommendaWons

Futurework:Extendthestudywithotherdatasets

Bibliography [email protected]

Bibliography

•  [Facebook]F.RaWu,“Facebook:peopleyoumayknow,”May2008.[Online].Available:h8ps://blog.facebook.com/blog.php?post=15610312130

•  [TwiVer]J.L.PankajGupta,AshishGoel,A.Sharma,D.Wang,andR.Zadeh,“W�:Thewhotofollowserviceattwi8er,”inProceedingsofwww2013Conference,2013.

•  [Chenetal.,2009]J.Chen,W.Geyer,C.Dugan,M.Muller,andI.Guy,“Makenewfriends,butkeeptheold:recommendingpeopleonsocialnetworkingsites,”inProceedingsoftheSIGCHIConferenceonHumanFactorsinCompu6ngSystems,2009.

•  [Hannonetal.2010]J.Hannon,M.Benne8,andB.Smyth,“Recommendingtwi8eruserstofollowusingcontentandcollaboraWvefilteringapproaches,”inProceedingsofthefourthACMconferenceonRecommendersystems,2010.

•  [Saltonetal.,1975]G.Salton,A.Wong,andC.S.Yang.“Avectorspace•  modelforautomaWcindexing”.Commun.ACM,1975.•  [Xiongetal.,KDD04]Xiong,H.,Shekhar,S.,Tan,P.N.,&Kumar,V.(2004,August).

ExploiWngasupport-basedupperboundofPearson'scorrelaWoncoefficientforefficientlyidenWfyingstronglycorrelatedpairs.InProceedingsofthetenthACMSIGKDDinterna6onalconferenceonKnowledgediscoveryanddatamining.ACM.

Ques6ons?

BarcelonaOctober21st,2015

[email protected]

Thankyouforyoura8enWon!

[Published on Information System Frontiers journal (Springer US)]

BehavioralDataMiningtoProduceNovelandSerendipitousFriendRecommenda6onsinaSocialBookmarkingSystem

Date post:	22-Jan-2017
Category:	Social Media
Upload:	matteo-manca
View:	2,034 times
Download:	0 times