1. Universitatea Politehnica Bucureşti - Facultatea de Automatică şi Calculatoare Towards a Peer-to-Peer Recommender System Based on Collaborative Filtering Techniques Authors: Sâmbotin Ana-Delia, Mugurel Andreica E-mail: ana.sambotin@cti.pub.ro, mugurel.andreica@cs.pub.ro 10.03.20161

2. Universitatea Politehnica Bucureşti - Facultatea de Automatică şi Calculatoare Outline Introduction Problems Design Architecture Conclusion 10.03.20162

3. Universitatea Politehnica Bucureşti - Facultatea de Automatică şi Calculatoare Introduction Peer-to-peer One-to-one communication model Each node is at the same time both supplier and consumer File sharing, resource sharing Recommender system Automate the recommendation of a song when large sets of data are involved Content based approach – uses the product’s description Collaborative filtering – uses user's social environment 10.03.20163

4. Universitatea Politehnica Bucureşti - Facultatea de Automatică şi Calculatoare Problems –There isn’t any recommender system that is based on an infrastructure specific to its needs –Usually, these applications force the users to express their preferences Application purpose –To improve the main properties of a recommender system(the speed of a file transfer, the stability of the network) –To indicate which node is the closest one with similar preferences 10.03.20164

5. Universitatea Politehnica Bucureşti - Facultatea de Automatică şi Calculatoare Design Used properties: –User profile analyzer – the files shared by a user –Network manager – round time trip between 2 nodes –Recommendation system – the search queries 10.03.20165

6. Universitatea Politehnica Bucureşti - Facultatea de Automatică şi Calculatoare Design Different strategies will be adopted in dynamic way: –Improving the stability of the network –Improving the file transfer –Improving the semantic distance –Improving the number of messages through the network 10.03.20166

7. Universitatea Politehnica Bucureşti - Facultatea de Automatică şi Calculatoare Architecture Main modules –Network Stability Localization Similarity –Profile analyzer Recommender system –File search 10.03.20167

8. Universitatea Politehnica Bucureşti - Facultatea de Automatică şi Calculatoare Architecture Two main roles: Bootstrap and normal node The nodes will be first helped by the bootstrap and after organizing themselves Different types of messages depending on the adopted strategy 10.03.20168

9. Universitatea Politehnica Bucureşti - Facultatea de Automatică şi Calculatoare Strategies A network of “supernodes”, that hide a group of peers A “supernode” can be consider to be a “proxy” node Distance strategy– relative geographical coordinates and the distance between peers File searching strategy – peer’s interests and similarity coefficient 10.03.20169

10. Universitatea Politehnica Bucureşti - Facultatea de Automatică şi Calculatoare Architecture - formulas Distance metric Similarity coefficient 10.03.201610

11. Universitatea Politehnica Bucureşti - Facultatea de Automatică şi Calculatoare Conclusions Future work: –Validate the proposed model –Test with large sets of data –Optimize the metrics An infrastructure for an recommender system which: –Should indicate who is the closest peer with similar preferences –Should improve the speed transfer –Should reduce the number of messages exchanged through the network –Should improves the search query time 10.03.201611

12. Universitatea Politehnica Bucureşti - Facultatea de Automatică şi Calculatoare The end Thank you! Questions? 10.03.201612