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Smart Companies and Artificial Intelligence - AI*IA (Associazione Italiana Intelligenza Artificiale) event. Florence. May 14, 2013.

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  • 1. Recommender Systems diprodotti bancari-nanziariGiovanni Semeraro , Cataldo MustoSmart Companies and Articial IntelligenceFirenze (Italy) - May 14, 2013

2. outline Background Needs Allocation Anima SGRs Progettometro From Needs to Asset Allocation: recommendersystems State of the art: Collaborative ltering, content-based ltering Our choice: case-based reasoning A possible use caseG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 3. BackgroundObjectWay Finance-as-a-ServiceSmart Application Software & Services for FinancialServices OperatorsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 4. BackgroundG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 5. BackgroundG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 6. BackgroundWealth Management reference frameworkG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 7. Current workProgettometro iPad app (https://itunes.apple.com/it/app/progettometro/id515222798?mt=8) iOs 4.3 required Designed by Anima SGR Helps people building their life projects Tool for needs allocationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 8. Progettometroprole selectionG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 9. Progettometrove stereotypesG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 10. Progettometroinput informationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 11. ProgettometroprojectionG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 12. Progettometrolife projectsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 13. Progettometroinformation about life projectsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 14. Progettometroupdated projectionG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 15. Progettometronal reportG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 16. from needsto assetallocationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 17. researchquestionis it possible to evolve aneeds allocation tooltowards an assetallocation one byexploiting articialinteligence techniques?G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 18. our proposal: personalizationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 19. to introduce an holistic vision of the userG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 20. to adapt asset portfolioson the ground of personal user prole and needsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 21. to introduce a tool helpful for supportingnancial advisors (not for private investors!)G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 22. SolutionRecommender SystemsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 23. Recommender SystemsRelevant items (movies, news, books, etc.) are suggested tothe user according to her preferences.G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 24. denitionRecommender Systems have the goal of guiding theusers in a personalized way to interestingor useful objects in a large space of possibleoptions.Burke, 2002 (*)(*) Robin D. Burke: Hybrid RecommenderSystems: Survey and Experiments. UMUAI,volume 12, issue 4, 331-370 (2002)G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 25. does it t our scenario?we are leaving the age of information, we are entering the age of recommendation(C.Anderson,The LongTail.Wired. October 2004)G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 26. Amazon.comTesto The technology is used by shopping websites such as Amazon, which receives about 35 percent of its revenue via productrecommendations. It is also used by coupon sites like Groupon; by travel sites to suggest ights, hotels, and rental cars; by social-networking sites such as LinkedIn; by video sites like Netix to recommend movies and TV shows, and by music, news, and foodsites to suggest songs, news stories, and restaurants, respectively. Even nancial-services rms recently began usingrecommender systems to provide alerts for investors about key market events in which they mightbe interested (N.Leavitt,A technology that comes highly recommended - http://tinyurl.com/d5y5hyl)G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 27. Netix.comRecommendations The technology is used by shopping websites such as Amazon, which receives about 35 percent of its revenue via productrecommendations. It is also used by coupon sites like Groupon; by travel sites to suggest ights, hotels, and rental cars; by social-networking sites such as LinkedIn; by video sites like Netix to recommend movies and TV shows, and by music, news, and foodsites to suggest songs, news stories, and restaurants, respectively. Even nancial-services rms recently began usingrecommender systems to provide alerts for investors about key market events in which they mightbe interested (N.Leavitt,A technology that comes highly recommended - http://tinyurl.com/d5y5hyl)G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 28. Recommender Systemscurrent literatureG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 29. Recommender Systemscurrent literatureCollaborative/Social FilteringContent-based FilteringG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 30. Recommender Systemscurrent literatureCollaborative/Social FilteringContent-based FilteringG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 31. collaborative recommendersSuggest items that similar users liked in the past.G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 32. collaborative recommendersSuggest items that similar users liked in the past.It capitalizes theword of mouth effectG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 33. collaborative recommendersexample: user-item matrixitem 1 item 2 item 3 item 4user1 user2 user3user4 G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 34. collaborative recommenderstarget user: user 4item 1 item 2 item 3 item 4user1 user2 user3user4 G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 35. collaborative recommenderslooking for like-minded usersitem 1 item 2 item 3 item 4user1 user2 user3user4 G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 36. collaborative recommendersrecommendationsitem 1 item 2 item 3 item 4user1 user2 user3user4 G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 37. Recommender Systemscurrent literatureCollaborative/Social FilteringContent-based FilteringG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Articial Intelligence, Firenze (Italy) - May 14, 2013 38. content-based recommendersSuggest items similar to those liked in the past by the userG.Semeraro, C.Musto, - Recommender Systems di Prod