Building Recommendation Systems on Social Data @KTH - FutureFriday - March 2014

Computing in Social Networks:
Building Recommendation
Systems on Social Data

Outlook
Introduction
Recommender Systems
Examples of recommender systems
Challenges with recommendation research
Social networks and recommendations
Show case of experimental work on:
Trust-aware recommendations
Privacy preserving recommendations
Diversity and opinions
Conclusion
NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL
RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT,
STOCKHOLM, SWEDEN
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Personalization and recommendations
Problem:
• Information overload…
Personalization and Profiles
• Users want to get personalized experience and at the same time don’t
want to share a lot of their personal information.
Recommendation systems
• Referred to as a range of algorithms which suggest a collection of items
to users, based on the knowledge of their profiles or previous
interactions.
Recommendation systems types:
• Collaborative filtering (User-based)
• Content-based filtering (Item-based)
• Hybrid filtering (Mix of users and content)
STOCKHOLM, SWEDEN
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Applications of
Recommendation Systems

Important Challenges in Recommendation
Research
1. Explaining the recommendations
It increases the trust of users as they know what is the basis
of the suggestions
2. Preserving the user privacy
How to make good recommendations without ignoring user
privacy
3. Diversity and novelty of recommendations
Recommenders suggest similar stuff to what you have seen,
it is important to get
5NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL
STOCKHOLM, SWEDEN

Social networks
Social networks [Wasserman et al,
1994]
• Focus of fields such as
behavioral, marketing, economics,
etc.
Relationships types
• Interactions, social relations
Explicit relationships
• Relations in online social networks
like in facebook, linkedin, etc).
Implicit relationships
• Computed based on users
behavior. For instance rating
movies, music, etc.
STOCKHOLM, SWEDEN
Image: https://www.facebook.com/notes/facebook-
engineering/visualizing-friendships/

Benefits of using social networks
for recommendations
• Take advantage of social network structure:
• Trust, social and structural Influence, transitivity, etc.
• Resilient against fraud, spam and fake accounts
• Identity and connections of the people on a social
network helps on dealing with bad guys
• Cold start problem
• System always has people to suggest (as long as they
are connected to the social network)
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Experimental work with trust and
recommendations
• Extracting trust networks from
• Getting better reach to items and users for improved
guessing of items to suggest.
• Using trust (networks) to improve accuracy of
recommendations generated
• Accurate suggestions of movies to watch, people to
follow, etc.
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Visualization of Trust Relations in
Ciao Dataset
STOCKHOLM, SWEDEN
In Nima Dokoohaki, Shahab Mokarizadeh, Mihhail Matskin, Ramona Bunea.
Correlating Trust and Privacy in Recommender Systems,
Special Issue on Web Intelligence and Personalization on Social Media,
Web Intelligence and Agent Systems An International Journal. IOS Press, 2014.
(submitted for review)

Trust networks and recommendations:
Data: Ratings Profiles to Trust Networks
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Impact of Trust Metric on Generated
Networks Structure
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Generated Trust Networks for Top-10 Trustworthy Users
(n= 5, m= 5): Without T-index
Generated Trust Networks for Top-10 Trustworthy
Users (n= 5, m=5): With T-index (= 100)
Soude Fazeli, Alireza Zarghami, Nima Dokoohaki, Mihhail Matskin,
Mechanizing Social Trust-Aware Recommenders with T-index Augmented Trustworthiness,
In proceedings of the 7th International Conference on Trust, Privacy & Security in Digital Business (Trustbus 2010)

Prediction accuracy against the variations
of Trustworthiness and Neighborhood size
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Soude Fazeli, Alireza Zarghami, Nima Dokoohaki, Mihhail Matskin,
Elevating Prediction Accuracy in Trust-aware Collaborative Filtering Recommenders through T-index Metric and TopTrustee lists,
In the Journal of Emerging Technologies in Web Intelligence (JETWI),
Special Issue On Web Personalization, Reputation and Recommender Systems, 2010.

Trust networks and recommendations
Rating Prediction Accuracy against network
(neighborhood) size
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Influence of search range on item coverage and prediction accuracy for Epinions dataset.
Stefan Magureanu, Nima Dokoohaki, Shahab Mokarizadeh, Mihhail Matskin,
Epidemic Trust-Based Recommender Systems ,
In proceedings of 2012 ASE/IEEE International Social Computing Conference
(SocialCom2012)

Experimental work with
Privacy and recommendations
• Proposing for software architectures that improve privacy of
recommendations
• How much data should the system use, can we control
this amount ?
• Can we use enough data and still get decent
suggestions ?
STOCKHOLM, SWEDEN

Privacy and recommendations:
Component Architectures for Preserving
Privacy during Computations
STOCKHOLM, SWEDEN
Nima Dokoohaki, Cihan Kaleli, Huseyin Polat and Mihhail Matskin,
Achieving Optimal Privacy in Trust-Aware Collaborative Filtering Recommender
Systems, The Second International Conference on Social Informatics (SocInfo 10)
Ramona Bunea, Shahab Mokarizadeh, Nima Dokoohaki and Mihhail Matskin,
Exploiting Dynamic Privacy in Socially Regularized Recommenders,
PinSoDa: Privacy in Social Data, in conjunction with the 11th IEEE International Conference on Data Mining (ICDM 2012)

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Privacy and recommendations:
Comparing performance of recommendations generated
Ramona Bunea, Shahab Mokarizadeh, Nima Dokoohaki and Mihhail Matskin,
Exploiting Dynamic Privacy in Socially Regularized Recommenders,
PinSoDa: Privacy in Social Data, in conjunction with the 11th IEEE International Conference on Data Mining (ICDM 2012)

Experimental work with diversity and
opinions recommendations
• How to diversify the recommendations
• What models can be proposed to give better summary
of reviews
• How to improve the recommendations of opinions in terms
of accuracy and scalability
• What models can be proposed to find more similar
people to read their Tweets.
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Data: From Review Profiles to Topic models
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Recommending Summarized Reviews:
Comparing Customer Ratings and estimated Sentiments
STOCKHOLM, SWEDEN
Ralf Krestel, Nima Dokoohaki
Diversifying Review Rankings, Special issue on Big Social Data Analytics,
Elsevier Journal of Neural Networks, 2014. Submitted for review.

Diversifying Summarized Reviews:
Comparing Recency of Summarization
Strategy Comparing LDA and LM
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Recommending Tweets:
Visualizing variations of topics for #wikileaks
and #eurozone tweets, 2011
Extended results from: Nima Dokoohaki, Mihhail Matskin,
Mining Divergent Opinion Trust Networks through Latent Dirichlet Allocation,
In proceedings of 2012 IEEE/ACM International Conference on Social Network Analysis and Mining (ASONAM 2012)

Recommending Users:
Link Prediction on inferred trust relations,
tweets from 2009
STOCKHOLM, SWEDEN
AUROC vs Number of Topics (Cosine)AUROC vs Number of Topics (KLD)
Extended results from: Nima Dokoohaki, Mihhail Matskin,
Mining Divergent Opinion Trust Networks through Latent Dirichlet Allocation,
In proceedings of 2012 IEEE/ACM International Conference on Social Network Analysis and Mining
(ASONAM 2012)

Conclusion
• This trail of research and education will continue under the
trends of data science and big data.
• KTH and other European institutions are planning to
design and offer study programs on data science and
analytics to students, hopefully very soon…
• Thank you!
STOCKHOLM, SWEDEN

Building Recommendation Systems on Social Data @KTH - FutureFriday - March 2014

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