Social Recommender Systems Tutorial - WWW 2011

I
idoguy
••
Social Recommender Systems Ido Guy, David Carmel IBM Research-Haifa, Israel WWW 2011, March 28 th -April 1 st , Hyderabad, India
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
Web 2.0 and Social Media ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Top 10 Website (Alexa.com, 3/2011) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Social Overload ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Social Overload ,[object Object],[object Object]
Social Recommender Systems ,[object Object],[object Object],[object Object],[object Object]
Recommender Systems and Social Media ,[object Object],[object Object],[object Object],Recommender Systems Social Media Social media introduces new types of data and metadata that can be leveraged by RS (tags, comments, votes, explicit social relationships) RS can significantly impact the success of social media, ensuring each user is present with the most relevant items that suits her personal needs
Real-World Examples
Real-World Examples
Real-World Examples
Real-World Examples
Real-World Examples
Real-World Examples ,[object Object],[object Object]
Fundamental Recommendation Approaches IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
Fundamental Recommendation approaches ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommendation techniques ( Burke,2002 ) Generate a classifier based on u ’s ratings, use it to classify new items Rating from u to items Features of items Content-based Infer a match between items and u ’s needs User needs Features of items Knowledge-based Identify similar users, extrapolate from their rating Demographic information about u Demographic information about users Demographic Identify similar users, extrapolate from their rating Rating from u to items User-item matrix CF Process Input Background Technique
Collaborative Filtering ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
User based CF algorithm ,[object Object],Jon’s taste is similar to both Chris and Alice tastes  Do not recommend Superman to Jon Shall we recommend Superman for John? ? Like Like Jon Dislike Like Like Chris Like Dislike ? Bob Dislike Like Like Alice Superman Snow-white Shrek
User based CF algorithm (cont) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Cosine based similarity between users Pearson based similarity between users
CF - Practical challenges ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Item-Based Nearest Neighbor Algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Bob dislikes Snow-white (which is similar to Shrek)  do not recommend Shrek to Bob ? Like Like Jon Dislike Like Like Chris Like Dislike ? Bob Dislike Like Like Alice Superman Snow-white Shrek
Dimensionality Reduction Algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Singular Value Decomposition (SVD) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Hybrid recommendation methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object]
Content Recommendation IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
Video Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Video Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
News Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
News Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object]
News Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Enhancing CF with Friends ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Blog Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Mixed Social Media Item Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object],Personalized Recommendation of Social Software Items based on Social Relations [Guy et al., RecSys ’09]
Mixed Social Media Item Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object]
Mixed Social Media Item Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object]
Mixed Social Media Item Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Tag-based Movie Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary of Key Points ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object]
Tag Recommendation IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
Tag Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
Tag Recommendation Approaches ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Flickr’s tag recommender (Sigurbjornsson WWW2008 )
Content-based tag recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Graph based approaches ,[object Object],[object Object],[object Object],[object Object],[object Object],w – a weight vector over nodes A – a row-stochastic matrix of the graph p - preference vector over the nodes ,[object Object]
Tag Recommendations in Folksonomies, Jaeschke07 ,[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
People Recommendation IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
Social Matching ,[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending People to Connect with ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending People to Connect with ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending People to Connect with ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],4 Algorithms Compared
Recommending People to Connect with ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending People to Connect with ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],degree distribution Betweenness delta
Recommending People to Connect with ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending People to Follow ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending People to Follow ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending People to Follow ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Expertise Location ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Community Recommendations IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
Recommending Similar Communities ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],at time of experiments L1-Norm L2-Norm Log-Odds Salton (IDF) Pointwise Mutual-Info: Pos. Correlations Pointwise Mutual-Info: Pos. and Neg. Correlations
Recommending Similar Communities ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Personalized Community Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Personalized Community Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Personalized Community Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object]
Personalized Community Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Personalized Community Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommendations for Groups IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
Recommendation for a group rather than individuals ,[object Object],[object Object],[object Object],[object Object]
Issues in recommendation for groups How to support the process of arriving at a final decision Negotiation may be required. Members decide which recommendation (if any) to accept Aggregation methods Aggregating preferences/results must be applied System generates recommendations Benefits/drawback for the group/system Members can examine each other Members specify their preferences General issues Differences from individuals Phase of recommendation
Explicit specification of preferences: MusicFX ( McCarthy, CSCW 2000) Hate Don’t mind Love Alternative rock : 1 2 3 4 5 Hot country: 1 2 3 4 5 50’oldies 1 2 3 4 5 …90 more genres
Collaborative speciation of preferences in the Travel Decision Forum [Jameson 04]
Collaborative specification - Advantages ,[object Object],[object Object],[object Object],[object Object],[object Object]
Aggregating Preferences ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Aggregation Methods [Berkovsky, RecSys ’10]
Group recommendations .. Baltrunas et a.l, RecSys2010 ,[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Cold Start Problem IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
The Cold Start Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],CF CB CF CB
The Cold Start Problem of New Items ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Recommend Y to B Recommend Y to A CB CF+CB User A Item X Item Y User B Similar Likes Similar User A Item X Item Y Likes Similar
The Cold Start Problem of New Items ,[object Object],[object Object]
The Cold Start Problem for New Users ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Cold Start Problem for New Users ,[object Object],[object Object],[object Object],[object Object]
The Cold Start Problem for New Users ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Cold Start for Tag-based Recommenders ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object]
Trust and Distrust IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
Social relations based recommendation ,[object Object],[object Object],[object Object],[object Object]
 
Trust Enhanced Recommendation ,[object Object],[object Object],[object Object]
Social trust graph User-item rating matrix Problem Definition
TidalTrust: Accumulate recommendation from trusted people only (Golbeck06)
Integrating Trust with CF ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Inferring Trust The Goal: Select two individuals - the source and sink - and recommend to the source how much to trust the sink . Sink Source
Trust computations ,[object Object],[object Object]
Reputation ,[object Object],[object Object],[object Object]
What about Distrust? (Victor 2009) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Trust in Recommendation (by explanations) Good explanations could help inspire user trust and loyalty, increase satisfaction, make it quicker and easier for users to find what they want, and persuade them to try or purchase a recommended item MoviExplain: A Recommender System with Explanations (Symeonidis09)
Explanation Aims: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Explanation types ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
TagSplanations (Vig09) ,[object Object],[object Object],[object Object],Rushmore Rear Window
Social-based Explanation (Guy09)
References ,[object Object],[object Object],[object Object],[object Object],[object Object]
Social Recommender Systems in the Enterprise IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
Social Media in the Enterprise ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
IBM Lotus Connections – Enterprise Social Software
Social Recommenders in the Enterprise ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending Content to Create ,[object Object],[object Object],[object Object],[object Object],[object Object]
Increasing Engagement in Enterprise Social Media ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Stranger Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Stranger Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object]
Temporal Aspects in Social Recommendation IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
The Time Factor in RS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Temporal CF ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Temporal Diversity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Temporal Diversity ,[object Object],[object Object],[object Object],[object Object],[object Object]
Temporal User Profile ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Time-based Evaluation in SRS Google Reader News Recommendation Network effects of people recommendation Increasing engagement of new users
Future Directions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object]
Social Recommendation over Activity Streams IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
Activity streams and the Real-time Web ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],External (aggregated) Hetrogenous Asymmetric (follow) FriendFeed Internal Hetrogenous Symmetric (friends) Facebook Internal Homogenous (status updates) Asymmetric (follow) Twitter
Utilizing the Stream for Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Enhancing News Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Enhancing Movie Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object]
Personalized Filtering of the Stream ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending Twitter URLs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending Twitter URLs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Topic-based Twitter Filtering ,[object Object]
Topic-based Twitter Filtering ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object]
Evaluation Methods IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
Evaluation goals ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Offline Evaluation ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Netflix Challenge (Bernett 07) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Evaluating Prediction Accuracy – the user opinions/ratings over items Root Mean Squared Error (RMSE) the most popular metric used in evaluating accuracy of predicted ratings A popular alternative: Mean Absolute Error (MAE) ,[object Object],[object Object]
Measuring Ranking Accuracy ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Evaluating top-N recommendation (Cremonesi, RecSys2010) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Off-line evaluation of a Tag Recommendation System using social bookmarks ( Carmel, CIKM’09) ,[object Object],[object Object],[object Object],[object Object],[object Object],(d1,u1): t1, t2 , t3, t4 , t5 ,t6, t7, t8, t 9 , t10 … (d2,u2): t1 , t2, t3 , t4 , t5 , t6 , t7, t8, t9, t10 … (d3,u3): t1, t2, t3, t4, t5 ,t6, t7, t8, t9 , t10 …
User Studies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Questions to measure subjective constructs
User studies – Pros and Cons ,[object Object],[object Object],[object Object],[object Object],[object Object]
Online Evaluation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object]
Open Issues and Research Challenges IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
Open Issues and Research challenges ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Questions? ,[object Object],[object Object]
1 of 156

Social Recommender Systems Tutorial - WWW 2011

••
TechnologyBusiness
I
idoguy

Recommended

Recommender system introductionRecommender system introduction
Recommender system introductionLiang Xiang
12.2K views•39 slides
Recommender SystemsRecommender Systems
Recommender SystemsCarlos Castillo (ChaTo)
2.8K views•67 slides
Recommender SystemsRecommender Systems
Recommender SystemsFrancesco Casalegno
2.3K views•35 slides
Social Recommender SystemsSocial Recommender Systems
Social Recommender Systemsguest77b0cd12
3.5K views•40 slides
An introduction to Recommender SystemsAn introduction to Recommender Systems
An introduction to Recommender SystemsDavid Zibriczky
2.3K views•105 slides
Recommender systems: Content-based and collaborative filteringRecommender systems: Content-based and collaborative filtering
Recommender systems: Content-based and collaborative filteringViet-Trung TRAN
11.4K views•42 slides

More Related Content

What's hot

What's hot(20)

Matrix Factorization In Recommender SystemsMatrix Factorization In Recommender Systems
Matrix Factorization In Recommender Systems
YONG ZHENG•26.1K views
Music Recommendations at Scale with SparkMusic Recommendations at Scale with Spark
Music Recommendations at Scale with Spark
Chris Johnson•58.6K views
Recommendation SystemRecommendation System
Recommendation System
Anamta Sayyed•472 views
Movie lens movie recommendation systemMovie lens movie recommendation system
Movie lens movie recommendation system
Gaurav Sawant•602 views
Recommendation systemRecommendation system
Recommendation system
PratisthaSingh5•152 views
Recommender systems using collaborative filteringRecommender systems using collaborative filtering
Recommender systems using collaborative filtering
D Yogendra Rao•3.4K views
Matrix Factorization Techniques For Recommender SystemsMatrix Factorization Techniques For Recommender Systems
Matrix Factorization Techniques For Recommender Systems
Lei Guo•46.7K views
Recommendation Systems BasicsRecommendation Systems Basics
Recommendation Systems Basics
Jarin Tasnim Khan•3K views
Recommendation system Recommendation system
Recommendation system
Vikrant Arya•54K views
Recommender SystemsRecommender Systems
Recommender Systems
Federico Cargnelutti•1.9K views
Collaborative Filtering Recommendation SystemCollaborative Filtering Recommendation System
Collaborative Filtering Recommendation System
Milind Gokhale•10.9K views
Recommender systemRecommender system
Recommender system
Nilotpal Pramanik•658 views
Recommendation System ExplainedRecommendation System Explained
Recommendation System Explained
Crossing Minds•10.6K views
Movie recommendation system using collaborative filtering system Movie recommendation system using collaborative filtering system
Movie recommendation system using collaborative filtering system
Mauryasuraj98•1K views
A Hybrid Recommendation systemA Hybrid Recommendation system
A Hybrid Recommendation system
Pranav Prakash•2.7K views
Data Mining and Recommendation SystemsData Mining and Recommendation Systems
Data Mining and Recommendation Systems
Salil Navgire•9.6K views
Recommendation systemRecommendation system
Recommendation system
SAIFUR RAHMAN•211 views
Recommender SystemsRecommender Systems
Recommender Systems
Girish Khanzode•8.6K views
Amazon Item-to-Item RecommendationsAmazon Item-to-Item Recommendations
Amazon Item-to-Item Recommendations
Roger Chen•11.7K views
Movie Recommender System Using Artificial Intelligence Movie Recommender System Using Artificial Intelligence
Movie Recommender System Using Artificial Intelligence
Shrutika Oswal•434 views

Viewers also liked

Viewers also liked(19)

Recommender Engines Seminar PaperRecommender Engines Seminar Paper
Recommender Engines Seminar Paper
Thomas Hess•12.9K views
ICWE 2015 - REST Web Service Description for Graph-Based Service DiscoveryICWE 2015 - REST Web Service Description for Graph-Based Service Discovery
ICWE 2015 - REST Web Service Description for Graph-Based Service Discovery
Nikolas Gonzalo Bravo Rakela•1.1K views
Web Services Discovery for DevicesWeb Services Discovery for Devices
Web Services Discovery for Devices
Jorgen Thelin•18.1K views
Analysis of Trust-Based Approaches for Web Service SelectionAnalysis of Trust-Based Approaches for Web Service Selection
Analysis of Trust-Based Approaches for Web Service Selection
Nicola Miotto•961 views
EnhancingWeb Process Self-Awareness with Context-Aware Service CompositionEnhancingWeb Process Self-Awareness with Context-Aware Service Composition
EnhancingWeb Process Self-Awareness with Context-Aware Service Composition
FET AWARE project - Self Awareness in Autonomic Systems•1.2K views
An approach for Context-aware Service Discovery and RecommendationAn approach for Context-aware Service Discovery and Recommendation
An approach for Context-aware Service Discovery and Recommendation
ronson1989•588 views
Recommendation Systems: Applying Amazon's Collaborative Filtering Methods to ...Recommendation Systems: Applying Amazon's Collaborative Filtering Methods to ...
Recommendation Systems: Applying Amazon's Collaborative Filtering Methods to ...
Nguyen Cao•732 views
Clustering WSDL Documents to Bootstrap the Discovery of Web Services Clustering WSDL Documents to Bootstrap the Discovery of Web Services
Clustering WSDL Documents to Bootstrap the Discovery of Web Services
ronson1989•1.1K views
Spectral clustering TutorialSpectral clustering Tutorial
Spectral clustering Tutorial
Zitao Liu•8.7K views
Trust-Based Rating Prediction for Recommendation in Web 2.0 Collaborative Lea...Trust-Based Rating Prediction for Recommendation in Web 2.0 Collaborative Lea...
Trust-Based Rating Prediction for Recommendation in Web 2.0 Collaborative Lea...
jianjinshu•591 views
Icwl2010 epflIcwl2010 epfl
Icwl2010 epfl
jianjinshu•226 views
ICSSEA 2007 - Toward a semantic Web Service discovery and dynamic orchestrat...ICSSEA 2007 - Toward a semantic Web Service discovery and dynamic orchestrat...
ICSSEA 2007 - Toward a semantic Web Service discovery and dynamic orchestrat...
Pierre CHATEL•527 views
Multilayer tutorial-netsci2014-slightlyupdatedMultilayer tutorial-netsci2014-slightlyupdated
Multilayer tutorial-netsci2014-slightlyupdated
Mason Porter•14.5K views
Rekomendujemy - Szybkie wprowadzenie do systemów rekomendacji oraz trochę wie...Rekomendujemy - Szybkie wprowadzenie do systemów rekomendacji oraz trochę wie...
Rekomendujemy - Szybkie wprowadzenie do systemów rekomendacji oraz trochę wie...
Bartlomiej Twardowski•4.5K views
tensor-decompositiontensor-decomposition
tensor-decomposition
Kenta Oono•33.6K views
Content Recommendation Based on Data Mining in Adaptive Social NetworksContent Recommendation Based on Data Mining in Adaptive Social Networks
Content Recommendation Based on Data Mining in Adaptive Social Networks
Marcel Caraciolo•4.2K views
Tensor Spectral ClusteringTensor Spectral Clustering
Tensor Spectral Clustering
Austin Benson•1K views
world wide webworld wide web
world wide web
Zainab Muneer•57.1K views
Exploring temporal graph data with Python: 
a study on tensor decomposition o...Exploring temporal graph data with Python: 
a study on tensor decomposition o...
Exploring temporal graph data with Python: 
a study on tensor decomposition o...
André Panisson•5.3K views

Similar to Social Recommender Systems Tutorial - WWW 2011

WORDWORD
WORDbutest
712 views•22 slides

Similar to Social Recommender Systems Tutorial - WWW 2011(20)

Collaborative filteringCollaborative filtering
Collaborative filtering
Aravindharamanan S•32 views
WORDWORD
WORD
butest•712 views
Recommender SystemsRecommender Systems
Recommender Systems
vivatechijri•71 views
Digital Trails Dave King 1 5 10 Part 2 D3Digital Trails Dave King 1 5 10 Part 2 D3
Digital Trails Dave King 1 5 10 Part 2 D3
Dave King•800 views
Fuzzy Logic Based Recommender SystemFuzzy Logic Based Recommender System
Fuzzy Logic Based Recommender System
RSIS International•122 views
Overview of recommender systemOverview of recommender system
Overview of recommender system
Stanley Wang•6.9K views
Recommenders SystemsRecommenders Systems
Recommenders Systems
Tariq Hassan•363 views
Sweeny group think-ias2015Sweeny group think-ias2015
Sweeny group think-ias2015
Marianne Sweeny•1.4K views
Recommendation system (1).pptxRecommendation system (1).pptx
Recommendation system (1).pptx
prathammishra28•414 views
Presentation for Doctoral Consortium at UMAP'11Presentation for Doctoral Consortium at UMAP'11
Presentation for Doctoral Consortium at UMAP'11
Thieme Hennis•644 views
Knowledge discovery in social media mining for market analysisKnowledge discovery in social media mining for market analysis
Knowledge discovery in social media mining for market analysis
Senuri Wijenayake•616 views
B1802021823B1802021823
B1802021823
IOSR Journals•132 views
Recommendation System Using Social Networking Recommendation System Using Social Networking
Recommendation System Using Social Networking
ijcseit•24 views
CONTEXTUAL MODEL OF RECOMMENDING RESOURCES ON AN ACADEMIC NETWORKING PORTALCONTEXTUAL MODEL OF RECOMMENDING RESOURCES ON AN ACADEMIC NETWORKING PORTAL
CONTEXTUAL MODEL OF RECOMMENDING RESOURCES ON AN ACADEMIC NETWORKING PORTAL
cscpconf•38 views
Contextual model of recommending resources on an academic networking portalContextual model of recommending resources on an academic networking portal
Contextual model of recommending resources on an academic networking portal
csandit•432 views
An Adaptive Framework for Enhancing Recommendation Using Hybrid TechniqueAn Adaptive Framework for Enhancing Recommendation Using Hybrid Technique
An Adaptive Framework for Enhancing Recommendation Using Hybrid Technique
ijcsit•359 views
243243
243
vivatechijri•20 views
Study of Recommendation System Used In Tourism and TravelStudy of Recommendation System Used In Tourism and Travel
Study of Recommendation System Used In Tourism and Travel
ijtsrd•10 views
Introduction to recommendation systemIntroduction to recommendation system
Introduction to recommendation system
Aravindharamanan S•106 views
FIND MY VENUE: Content & Review Based Location Recommendation SystemFIND MY VENUE: Content & Review Based Location Recommendation System
FIND MY VENUE: Content & Review Based Location Recommendation System
IJTET Journal•426 views

Recently uploaded

Recently uploaded(20)

METHOD AND SYSTEM FOR PREDICTING OPTIMAL LOAD FOR WHICH THE YIELD IS MAXIMUM ...METHOD AND SYSTEM FOR PREDICTING OPTIMAL LOAD FOR WHICH THE YIELD IS MAXIMUM ...
METHOD AND SYSTEM FOR PREDICTING OPTIMAL LOAD FOR WHICH THE YIELD IS MAXIMUM ...
Prity Khastgir IPR Strategic India Patent Attorney Amplify Innovation•23 views
GigaIO: The March of Composability Onward to Memory with CXLGigaIO: The March of Composability Onward to Memory with CXL
GigaIO: The March of Composability Onward to Memory with CXL
CXL Forum•118 views
AI: mind, matter, meaning, metaphors, being, becoming, life valuesAI: mind, matter, meaning, metaphors, being, becoming, life values
AI: mind, matter, meaning, metaphors, being, becoming, life values
Twain Liu 刘秋艳•28 views
Java 21 and Beyond- A Roadmap of Innovations .pdfJava 21 and Beyond- A Roadmap of Innovations .pdf
Java 21 and Beyond- A Roadmap of Innovations .pdf
Ana-Maria Mihalceanu•51 views
The Research Portal of Catalonia: Growing more (information) & more (services)The Research Portal of Catalonia: Growing more (information) & more (services)
The Research Portal of Catalonia: Growing more (information) & more (services)
CSUC - Consorci de Serveis Universitaris de Catalunya•51 views
AMD: 4th Generation EPYC CXL DemoAMD: 4th Generation EPYC CXL Demo
AMD: 4th Generation EPYC CXL Demo
CXL Forum•117 views
"Fast Start to Building on AWS", Igor Ivaniuk"Fast Start to Building on AWS", Igor Ivaniuk
"Fast Start to Building on AWS", Igor Ivaniuk
Fwdays•31 views
MemVerge: Memory Viewer SoftwareMemVerge: Memory Viewer Software
MemVerge: Memory Viewer Software
CXL Forum•115 views
"Thriving Culture in a Product Company — Practical Story", Volodymyr Tsukur"Thriving Culture in a Product Company — Practical Story", Volodymyr Tsukur
"Thriving Culture in a Product Company — Practical Story", Volodymyr Tsukur
Fwdays•35 views
Webinar : Competing for tomorrow’s leaders – How MENA insurers can win the wa...Webinar : Competing for tomorrow’s leaders – How MENA insurers can win the wa...
Webinar : Competing for tomorrow’s leaders – How MENA insurers can win the wa...
The Digital Insurer•24 views
Liqid: Composable CXL PreviewLiqid: Composable CXL Preview
Liqid: Composable CXL Preview
CXL Forum•118 views
The details of description: Techniques, tips, and tangents on alternative tex...The details of description: Techniques, tips, and tangents on alternative tex...
The details of description: Techniques, tips, and tangents on alternative tex...
BookNet Canada•97 views
Micron CXL product and architecture updateMicron CXL product and architecture update
Micron CXL product and architecture update
CXL Forum•23 views
PyCon ID 2023 - Ridwan Fadjar Septian.pdfPyCon ID 2023 - Ridwan Fadjar Septian.pdf
PyCon ID 2023 - Ridwan Fadjar Septian.pdf
Ridwan Fadjar•163 views
"Role of a CTO in software outsourcing company", Yuriy Nakonechnyy"Role of a CTO in software outsourcing company", Yuriy Nakonechnyy
"Role of a CTO in software outsourcing company", Yuriy Nakonechnyy
Fwdays•35 views
Astera Labs: Intelligent Connectivity for Cloud and AI InfrastructureAstera Labs: Intelligent Connectivity for Cloud and AI Infrastructure
Astera Labs: Intelligent Connectivity for Cloud and AI Infrastructure
CXL Forum•118 views
Combining Orchestration and Choreography for a Clean ArchitectureCombining Orchestration and Choreography for a Clean Architecture
Combining Orchestration and Choreography for a Clean Architecture
ThomasHeinrichs1•64 views
Empathic Computing: Delivering the Potential of the MetaverseEmpathic Computing: Delivering the Potential of the Metaverse
Empathic Computing: Delivering the Potential of the Metaverse
Mark Billinghurst•389 views
.conf Go 2023 - SIEM project @ SNF.conf Go 2023 - SIEM project @ SNF
.conf Go 2023 - SIEM project @ SNF
Splunk•163 views
Level-up Your Cloud Visibility Into AWS With ThousandEyesLevel-up Your Cloud Visibility Into AWS With ThousandEyes
Level-up Your Cloud Visibility Into AWS With ThousandEyes
ThousandEyes•74 views

Social Recommender Systems Tutorial - WWW 2011

  • 1. Social Recommender Systems Ido Guy, David Carmel IBM Research-Haifa, Israel WWW 2011, March 28 th -April 1 st , Hyderabad, India
  • 2.
  • 3. Introduction IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 15.
  • 16. Fundamental Recommendation Approaches IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
  • 17.
  • 18. Recommendation techniques ( Burke,2002 ) Generate a classifier based on u ’s ratings, use it to classify new items Rating from u to items Features of items Content-based Infer a match between items and u ’s needs User needs Features of items Knowledge-based Identify similar users, extrapolate from their rating Demographic information about u Demographic information about users Demographic Identify similar users, extrapolate from their rating Rating from u to items User-item matrix CF Process Input Background Technique
  • 19.
  • 20.
  • 21.
  • 22. Cosine based similarity between users Pearson based similarity between users
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29. Content Recommendation IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39.
  • 40.
  • 41.
  • 42.
  • 43.
  • 44.
  • 45. Tag Recommendation IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
  • 46.
  • 47.  
  • 48.
  • 49. Flickr’s tag recommender (Sigurbjornsson WWW2008 )
  • 50.
  • 51.
  • 52.
  • 53.
  • 54. People Recommendation IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
  • 55.
  • 56.
  • 57.
  • 58.
  • 59.
  • 60.
  • 61.
  • 62.
  • 63.
  • 64.
  • 65.
  • 66.
  • 67. Community Recommendations IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
  • 68.
  • 69.
  • 70.
  • 71.
  • 72.
  • 73.
  • 74.
  • 75.
  • 76. Recommendations for Groups IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
  • 77.
  • 78. Issues in recommendation for groups How to support the process of arriving at a final decision Negotiation may be required. Members decide which recommendation (if any) to accept Aggregation methods Aggregating preferences/results must be applied System generates recommendations Benefits/drawback for the group/system Members can examine each other Members specify their preferences General issues Differences from individuals Phase of recommendation
  • 79. Explicit specification of preferences: MusicFX ( McCarthy, CSCW 2000) Hate Don’t mind Love Alternative rock : 1 2 3 4 5 Hot country: 1 2 3 4 5 50’oldies 1 2 3 4 5 …90 more genres
  • 80. Collaborative speciation of preferences in the Travel Decision Forum [Jameson 04]
  • 81.
  • 82.
  • 84.
  • 85.
  • 86. The Cold Start Problem IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
  • 87.
  • 88.
  • 89.
  • 90.
  • 91.
  • 92.
  • 93.
  • 94.
  • 95. Trust and Distrust IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
  • 96.
  • 97.  
  • 98.
  • 99. Social trust graph User-item rating matrix Problem Definition
  • 100. TidalTrust: Accumulate recommendation from trusted people only (Golbeck06)
  • 101.
  • 102. Inferring Trust The Goal: Select two individuals - the source and sink - and recommend to the source how much to trust the sink . Sink Source
  • 103.
  • 104.
  • 105.
  • 106. Trust in Recommendation (by explanations) Good explanations could help inspire user trust and loyalty, increase satisfaction, make it quicker and easier for users to find what they want, and persuade them to try or purchase a recommended item MoviExplain: A Recommender System with Explanations (Symeonidis09)
  • 107.
  • 108.
  • 109.
  • 111.
  • 112. Social Recommender Systems in the Enterprise IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
  • 113.
  • 114. IBM Lotus Connections – Enterprise Social Software
  • 115.
  • 116.
  • 117.
  • 118.
  • 119.
  • 120.
  • 121. Temporal Aspects in Social Recommendation IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
  • 122.
  • 123.
  • 124.
  • 125.
  • 126.
  • 127. Time-based Evaluation in SRS Google Reader News Recommendation Network effects of people recommendation Increasing engagement of new users
  • 128.
  • 129.
  • 130. Social Recommendation over Activity Streams IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
  • 131.
  • 132.
  • 133.
  • 134.
  • 135.
  • 136.
  • 137.
  • 138.
  • 139.
  • 140.
  • 141. Evaluation Methods IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
  • 142.
  • 143.
  • 144.
  • 145.
  • 146.
  • 147.
  • 148.
  • 149.
  • 150. Questions to measure subjective constructs
  • 151.
  • 152.
  • 153.
  • 154. Open Issues and Research Challenges IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
  • 155.
  • 156.

Editor's Notes

  1. The sites in the top sites lists are ordered by their 1 month alexa traffic rank. The 1 month rank is calculated using a combination of average daily visitors and pageviews over the past month. The site with the highest combination of visitors and pageviews is ranked #1.
  2. Facebook – updated March 2011 Twitter – June 2010 YouTube – May 2010
  3. To mention that table entries can be filled by implicit feedback
  4. Outside the box – since similar users can recommend surprising items compared to content-based approaches which is limited to items that fit the user profile
  5. What a Difference a Group Makes:Web-Based Recommendations for Interrelated Users (Mason & Smith)
  6. Explaining collaborative filtering
  7. Tagsplanations: explaining recommendations using tags, Vig, IUI09
  8. User Evaluation Framework of Recommender Systems, Chen (SRS 2010)