building recommendation systems on social data @kth - futurefriday - march 2014

Computing in Social Networks:Building Recommendation Systems on Social Data

2NIMA DOKOOHAKI, [email protected] POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, STOCKHOLM, SWEDEN

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


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)


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


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.

Image: https://www.facebook.com/notes/facebook-engineering/visualizing-friendships/

https://www.facebook.com/notes/facebook-engineering/visualizing-friendships/

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)


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.


Visualization of Trust Relations in Ciao Dataset

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


Trust networks and recommendations:Impact of Trust Metric on Generated Networks Structure

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)

Nima Dokoohaki

Add publications to each slide


Trust networks and recommendations:Prediction accuracy against the variations of Trustworthiness and Neighborhood size

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 recommendationsRating Prediction Accuracy against network (neighborhood) size

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 ?

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Privacy and recommendations:Component Architectures for Preserving Privacy during Computations

NIMA DOKOOHAKI, [email protected] POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, 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.


Data: From Review Profiles to Topic models


Recommending Summarized Reviews:Comparing Customer Ratings and estimated Sentiments

Ralf Krestel, Nima DokoohakiDiversifying 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

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!

building recommendation systems on social data @kth - futurefriday - march 2014

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