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A new direction for recommender systems: balancing privacy and personalisation
Dr. Benjamin Heitmann discusses the balance between privacy and personalization in recommender systems, highlighting the growing consumer preference for personalized services despite increasing privacy concerns. He explains the essential data requirements for effective recommender systems and emphasizes the need for privacy-enabled personalized algorithms, user experience adjustments, and consumer-centric business models. The talk also calls for open research into innovative methods for integrating privacy into personalization without sacrificing performance.
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A new direction for recommender systems: balancing privacy and personalisation
- 1. A new directionfor recommender systems: balancing privacy and personalisation Dr. Benjamin Heitmann Unit for Information and Retrieval (UIMR) INSIGHT Centre for Data Analytics National University of Ireland, Galway
- 2. Overview of talk •Why is privacy important for personalisation? – Trade-off between privacy and personalisation • Basics of recommender systems – Why do they need so much data? • Pieces of the puzzle / parts of the solution – Ideas on open research topics 2
- 3. About me • Post-doctoralresearcher, INSIGHT @ NUI Galway Research interests: 1. Personalisation using knowledge graphs 2. Architecture of data intensive ecosystems 3. Balancing privacy and personalisation 3
- 4. Why is privacyimportant for personalisation?
- 5. Personalisation versus privacy •Personalisation has become an expected feature: – 75% of consumers prefer personalised E-Commerce retailers – 94% of companies view personalisation as critical to business performance • However, reluctance to share user preferences is growing 5
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- 7. 2013/14 – Aturning point for privacy online ? • June 2013: ex-NSA contractor Edward Snowden leaks 1,7 million documents • Reveals systematic international and domestic surveillance in: – USA – “Five Eyes” (UK, AU, CA) – Europe • Targets commercial companies such as Microsoft, Apple, Google, Facebook 7Why privacy for the Web ?
- 8. Abuse of surveillancefor industrial espionage and LOVEINT 8Why privacy for the Web ?
- 9. What has changed? 9Why privacy for the Web ?
- 10. The elephant inthe room – “Surveillance is the business model of the internet” – Bruce Schneier • Personalisation and privacy are fundamentally opposed – Always a trade-off: utility versus privacy • Data analytics and privacy have the same relationship 10Why privacy for the Web ?
- 11. Examples from Facebookand Google 11Why privacy for the Web ?
- 12. Summary: Why isprivacy important for personalisation? • “Surveillance” is the business model of the web • Everybody does it: – Governments and Businesses • Why ? Data required for: – Personalisation – Analytics – Advertisements – More data -> better results • How could we change that? – We need to understand how personalisation works 12Why privacy for the Web ?
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- 14. Parts of arecommender system 14 • Background data: data about the music, books, .. – Ratings – Content data • User profile: preferences as ratings or keywords • Algorithm: uses background data to provide recs for one user profile Recommender systems basics
- 15. RecSys algorithm: CollaborativeFiltering • Most reliable algorithm • Uses ratings to determine similarity • Predict missing ratings • Requires lots of ratings • Example: – 10.000 users – 1000 items – 10.000.000 matrix entries – 7% required: 700.000 ratings – 70 per user on average 15Recommender systems basics
- 16. RecSys algorithm: Content-basedfiltering • Similarity determined from content features: – Genre – Keywords – Description – Author • Requires less data from users • Requires high quality content description • Used together with Collaborative Filtering 16 Garth BrooksJohnny Cash Iron MaidenMetallica similar similar Music Catch 22 Harry Potter 1 ks Kyoto New York Travel ? ? ? Recommender systems basics
- 17. Collecting user profiledata for personalisation • Architecture of real-world recommender systems has changed: – Shift from closed to open inventories – Emergence of ecosystems to share user preference data 17Recommender systems basics
- 18. Summary: Basics ofrecommender systems • Personalisation algorithms require a lot of data – Collaborative Filtering: uses rating data – Content-based Filtering: uses content data • “Magical” results not possible without gigantic amounts of data • Current approaches collect even more data: – De-centralised collection approach • Processing of algorithm is centralised 18Recommender systems basics
- 19. Parts of thesolution
- 20. Can we addprivacy to personalisation ? • Let’s add “privacy” as a requirement. – For instance, through public policy. • What changes ? – Algorithm – User experience (UX) – Business model 20Open research questions: parts of the solution
- 21. Part of thesolution: Recommendation algorithm • First step: privacy-enabled data mining • Second step: privacy- enabled personalisation • Use anonymised data • De-centralised processing – Cryptography ? – Secure multi-party computation ? – Oblivious data structures 21 Aggregation & Anonymisation All users of a system Anonymised data set Privacy-enabled personalisation ? Personlisation for individual user Aggregation & Anonymisation All users of a system Insights from anonymised data set Open research questions: parts of the solution
- 22. Part of thesolution: User Experience • PGP vs. SSL • Both are meant to keep communication secure and private • Both use similar cryptographic ideas • Very different user experience! • One is a success, one is a failure. 22Open research questions: parts of the solution
- 23. Part of thesolution: Business Model • Make business model consumer centric • Treat users as customers, not as data sources • Provide infrastructure for monetisation of private data • Revenue from premium services 23Open research questions: parts of the solution
- 24. Summary: Parts ofthe solution • Nobody has figured out a recipe for adding privacy to personalised services. • If somebody does, it will probably depend on these puzzle pieces: – Algorithm: • Use anonymised data, de-centralised processing – User experience: • Make it easy to use and understand. – Business model: • Who pays? Where does the money come from? 24Open research questions: parts of the solution
- 25. Questions & Feedback? • If you want to work / contribute / research / hack / start a business in relation to this new direction of personalisation, make sure to get in touch with me! Benjamin.Heitmann@insight-centre.org 25Recommender systems basics
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- 27. Changes in thearchitecture of recommender systems 27
- 28. Main goal ofPhD research: Enable cross-domain personalisation • Open framework for cross-domain personalisation • Prototype implementation based on the framework 28 Travel destinations: Movies: Multi-source user profiles with preferences from multiple domains Cross-domain recommendation algorithm (SemStim) uses DBpedia as background knowledge Recommendations for target domains
- 29. Cross-domain algorithm: SemStim •Algorithm uses knowledge graph from DBpedia • Graph search between two sets of items 29 Douglas Adams User profile Recommendable items Start of spreading activation DBpedia Atheism Activists Cambridge United Kingdom Macmillian Restaurant at the end of the universe Kurt Vonnegut Richard Dawkins dc:subject author subsequentWork influencedBy influencedBy dc:subject publisher author birthplace subdivisionName country The Hitchhikers Guide to the Galaxy (novel)
- 30. Part of thesolution: Public Policy • Who provides incentives for privacy ? • Public policy bodies, i.e. European Union • INSIGHT proposed “Magna Carta for Data” in the EU • Can provide mandate for privacy-enabled personalisation. 30Open research questions: parts of the solution