- grained analysis of user interactions and activities
DESCRIPTION
- grained Analysis of User Interactions and Activities. Suvash Sedhain , Scott Sanner , Lexing Xie , Riley Kidd, Khoi -Nguyen Tran, Peter Christen Australian National University NICTA. Social Recommendation: Problem Setting. U. Like/Dislike?. URL. Friends. Liked U’s Video. - PowerPoint PPT PresentationTRANSCRIPT
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-grained Analysis of User Interactions and Activities
Suvash Sedhain, Scott Sanner, Lexing Xie, Riley Kidd, Khoi-Nguyen Tran, Peter Christen
Australian National University NICTA
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Like/Dislike?
FriendsLiked U’s Video
Justin Bieber Fan
U
URL
Social Recommendation: Problem Setting
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Nearest Neighbor(N
N)
Matrix Factorization (MF)
Social MF
In Reality
Motivation
Social Similarity
Friends
Liked U’s videos
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Key Question
• Can we do better social recommendation via fine-grained analysis of different interactions?
YES !!
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Outline
• Motivation• Rich social features– Facebook interactions and activities– Social affinity features
• Experiment• Results and discussion• Summary
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Facebook Interactions
URL
Photo
Video
Post
Like
Comment
Tag
ContentsFriends
{link, post, photo, video} × {like, tag, comment} × {incoming, outgoing}
Outgoing Incoming
23 interactions
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Facebook Activities
Groups3,469
Pages10,771
Favourites4,284
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(Pages)
Social Affinity Features
Train
Test
{u2, u
7, u
9}
{u2, u
5, u
11 …
.}
Social Affinity Filtering(SAF)• Naïve Bayes• Logistic Regression• SVM
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Data DescriptionLinkR: Link Recommender App
119 users and 37,872 friends
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Experiment Setup
• Baselines– Non- Social Methods• Nearest Neighbors(NN)• Matchbox (MF)
– Social Methods• Social Matchbox (SMB) [Noel et al. WWW 2012]
• Social Affinity Filtering– Interactions
• Naïve Bayes (NB-ISAF)• Logistic Regression (LR-ISAF)• SVM (SVM-ISAF)
– Activities• Naïve Bayes (NB-ASAF)• Logistic Regression (LR-ASAF)• SVM (SVM-ASAF)
Reported results are based on 10 fold cross-validation
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SAF Accuracy
BaselinesSocial Affinity Filtering
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Outline
• Motivation• Rich social features• Experiment• Discussion– Interaction Analysis– Activity Analysis
• Summary
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Are all Interactions Equally Informative? Conditional Entropy as a measure of informativeness
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Are large groups more informative than small groups?
Large group tend not to be predictive
Most predictive group were small in size
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Are all favourites equally informative?
• Majority of them are less informative • Very Informative outliers
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Most and Median Informative Favourites
• Median favorites were generic• Most informative were specialized
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SAF for User Cold startAc
cura
cy
• User cold start : new user problem• Cold-Start Predictor: Held out test users from training dataset• Non Cold-Start : Train on full training dataset
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Is having more social activity better?
<10 10-50 >50 <10 10-50 >50 <10 10-50 >50
Number of groups joined Number of page liked Number of favourites
Groups Pages Favourites
Accu
racy
• More activity is better for Social Affinity Filtering
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Power of page likes
• Relates to the recent work– Page likes help to predict gender, relationship status,
religion etc.• Michal Kosinskia, David Stillwella, and Thore Graepel, Private traits
and attributes are predictable from digital records of human behavior, PNAS 2013
– Page likes help to predict user purchase behavior in ebay• Yongzheng Zhang and Marco Pennacchiotti, Predicting purchase
behaviors from social media, WWW '13
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Summary• Social Affinity Filtering (SAF)
– Novel social recommendation– scalable
• All Interactions and activities are not equally predictive– Interactions in videos are more predictive than other modalities– Small sized activities tends to be more predictive
• Future work– Predict with only likes (no dislikes)– SAF + MF/NN
• If you are building social recommender – Ask for Facebook page likes– Use SAF to build scalable state-of-the-art recommender system
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Thanks!!!