Computing in Social Networks:
Building Recommendation
Systems on Social Data
Outlook
Introduction
Recommender Systems
Examples of recommender systems
Challenges with recommendation research
Social ne...
Personalization and recommendations
Problem:
• Information overload…
Personalization and Profiles
• Users want to get pers...
NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL
RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT,
STOCKHOLM, SWEDEN
4
Application...
Important Challenges in Recommendation
Research
1. Explaining the recommendations
It increases the trust of users as they ...
Social networks
Social networks [Wasserman et al,
1994]
• Focus of fields such as
behavioral, marketing, economics,
etc.
R...
Benefits of using social networks
for recommendations
• Take advantage of social network structure:
• Trust, social and st...
Experimental work with trust and
recommendations
• Extracting trust networks from
• Getting better reach to items and user...
Visualization of Trust Relations in
Ciao Dataset
9NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL
RESEARCHER SEMINAR @ FUTURE FR...
Trust networks and recommendations:
Data: Ratings Profiles to Trust Networks
10NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL
R...
Trust networks and recommendations:
Impact of Trust Metric on Generated
Networks Structure
11NIMA DOKOOHAKI, NIMAD@KTH.SE ...
Trust networks and recommendations:
Prediction accuracy against the variations
of Trustworthiness and Neighborhood size
12...
Trust networks and recommendations
Rating Prediction Accuracy against network
(neighborhood) size
13NIMA DOKOOHAKI, NIMAD@...
Experimental work with
Privacy and recommendations
• Proposing for software architectures that improve privacy of
recommen...
Privacy and recommendations:
Component Architectures for Preserving
Privacy during Computations
15NIMA DOKOOHAKI, NIMAD@KT...
NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL
RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT,
STOCKHOLM, SWEDEN
16
Privacy an...
Experimental work with diversity and
opinions recommendations
• How to diversify the recommendations
• What models can be ...
Data: From Review Profiles to Topic models
18NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL
RESEARCHER SEMINAR @ FUTURE FRIDAY ...
Recommending Summarized Reviews:
Comparing Customer Ratings and estimated Sentiments
19NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDO...
Diversifying Summarized Reviews:
Comparing Recency of Summarization
Strategy Comparing LDA and LM
20NIMA DOKOOHAKI, NIMAD@...
NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL
RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT,
STOCKHOLM, SWEDEN
21
Recommendi...
Recommending Users:
Link Prediction on inferred trust relations,
tweets from 2009
22NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTO...
Conclusion
• This trail of research and education will continue under the
trends of data science and big data.
• KTH and o...
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Building Recommendation Systems on Social Data @KTH - FutureFriday - March 2014

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My talk entitled" Computing in Social Networks: Building Recommendation Systems on Social Data "

Given at Future Friday event, KTH ICT March 2014

The talk is televised by Swedish TV Kunskapkanalen/ UR Samtid

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  • 30 minutes
    Would be 15 slides + 5 minutes questions I guess

    Image CC: https://www.flickr.com/photos/daniel_iversen/5440728466/sizes/m/in/photostream/
  • https://www.flickr.com/photos/daviderickson/5579493777/sizes/o/in/photostream/
    https://www.flickr.com/photos/daviderickson/5580079906/sizes/o/in/photostream/
    https://www.flickr.com/photos/42696116@N00/3979783546/in/photoli
    https://www.flickr.com/photos/stevegarfield/
  • Social network of Google+ Image: http://www.flickr.com/photos/ajc1/6260304760/
  • For the sake of simplicity, we display only users(displayed as nodes) and
    their connections (trust relationships) to top-10 trustworthy users. As mentioned,
    each cluster is described as a group of like-minded users in terms of trust.

    It is shown that the number of common users between clusters increases which enables
    users of different clusters to find each other easier. In our case, more users form
    divergent areas of users’ interests, presented as clusters, can be accessible.
  • Results have been partially competable with Neil Lathia’s work
  • ROC curve for Pearson (left) and Kullback-Leibler (right) Variable: social network size
  • Building Recommendation Systems on Social Data @KTH - FutureFriday - March 2014

    1. 1. Computing in Social Networks: Building Recommendation Systems on Social Data
    2. 2. 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 2
    3. 3. 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) NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, STOCKHOLM, SWEDEN 3
    4. 4. NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, STOCKHOLM, SWEDEN 4 Applications of Recommendation Systems
    5. 5. 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 RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, STOCKHOLM, SWEDEN
    6. 6. 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. 6NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, STOCKHOLM, SWEDEN Image: https://www.facebook.com/notes/facebook- engineering/visualizing-friendships/
    7. 7. 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) 7NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, STOCKHOLM, SWEDEN
    8. 8. 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. 8NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, STOCKHOLM, SWEDEN
    9. 9. Visualization of Trust Relations in Ciao Dataset 9NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, 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)
    10. 10. Trust networks and recommendations: Data: Ratings Profiles to Trust Networks 10NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, STOCKHOLM, SWEDEN
    11. 11. Trust networks and recommendations: Impact of Trust Metric on Generated Networks Structure 11NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, STOCKHOLM, SWEDEN 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)
    12. 12. Trust networks and recommendations: Prediction accuracy against the variations of Trustworthiness and Neighborhood size 12NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, STOCKHOLM, SWEDEN 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.
    13. 13. Trust networks and recommendations Rating Prediction Accuracy against network (neighborhood) size 13NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, STOCKHOLM, SWEDEN 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)
    14. 14. 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 ? 14NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, STOCKHOLM, SWEDEN
    15. 15. Privacy and recommendations: Component Architectures for Preserving Privacy during Computations 15NIMA DOKOOHAKI, NIMAD@KTH.SE 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)
    16. 16. NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, STOCKHOLM, SWEDEN 16 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)
    17. 17. 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. 17NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, STOCKHOLM, SWEDEN
    18. 18. Data: From Review Profiles to Topic models 18NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, STOCKHOLM, SWEDEN
    19. 19. Recommending Summarized Reviews: Comparing Customer Ratings and estimated Sentiments 19NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, 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.
    20. 20. Diversifying Summarized Reviews: Comparing Recency of Summarization Strategy Comparing LDA and LM 20NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, STOCKHOLM, SWEDEN
    21. 21. NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, STOCKHOLM, SWEDEN 21 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)
    22. 22. Recommending Users: Link Prediction on inferred trust relations, tweets from 2009 22NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, 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)
    23. 23. 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! 24NIMA DOKOOHAKI, NIMAD@KTH.SE POSTDOCTORAL RESEARCHER SEMINAR @ FUTURE FRIDAY 2014 KTH/ICT, STOCKHOLM, SWEDEN
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