Recommender Systems diprodotti bancari-finanziariGiovanni Semeraro , Cataldo MustoSmart Companies and Artificial Intelligenc...
outline• Background• Needs Allocation• Anima SGR’s Progettometro• From Needs to Asset Allocation: recommendersystems• Stat...
BackgroundObjectWay Finance-as-a-ServiceSmart Application Software & Services for FinancialServices OperatorsG.Semeraro, C...
BackgroundG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intellig...
BackgroundG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intellig...
BackgroundWealth Management reference frameworkG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.S...
Current work•Progettometro• iPad app (https://itunes.apple.com/it/app/progettometro/id515222798?mt=8)• iOs 4.3 required• D...
Progettometroprofile selectionG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and...
Progettometrofive stereotypesG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and ...
Progettometroinput informationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies an...
ProgettometroprojectionG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artifi...
Progettometrolife projectsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Ar...
Progettometroinformation about life projectsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smar...
Progettometroupdated projectionG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies a...
Progettometrofinal reportG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Arti...
from needsto assetallocationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and ...
researchquestionis it possible to evolve aneeds allocation tooltowards an assetallocation one byexploiting artificialinteli...
our proposal: personalizationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and...
to introduce an holistic vision of the userG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart...
to adapt asset portfolioson the ground of personal user profile and needsG.Semeraro, C.Musto, - Recommender Systems di Prod...
to introduce a tool helpful for supportingfinancial advisors (not for private investors!)G.Semeraro, C.Musto, - Recommender...
SolutionRecommender SystemsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and A...
Recommender SystemsRelevant items (movies, news, books, etc.) are suggested tothe user according to her preferences.G.Seme...
definitionRecommender Systems have the goal of guiding theusers in a personalized way to interestingor useful objects in a ...
does it fit our scenario?“we are leaving the age of information, we are entering the age of recommendation”(C.Anderson,The ...
Amazon.comTesto“ The technology is used by shopping websites such as Amazon, which receives about 35 percent of its revenu...
Netflix.comRecommendations“ The technology is used by shopping websites such as Amazon, which receives about 35 percent of ...
Recommender Systemscurrent literatureG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Compa...
Recommender Systemscurrent literatureCollaborative/Social FilteringContent-based FilteringG.Semeraro, C.Musto, - Recommend...
Recommender Systemscurrent literatureCollaborative/Social FilteringContent-based FilteringG.Semeraro, C.Musto, - Recommend...
collaborative recommendersSuggest items that similar users liked in the past.G.Semeraro, C.Musto, - Recommender Systems di...
collaborative recommendersSuggest items that similar users liked in the past.It capitalizes the‘word of mouth’ effectG.Sem...
collaborative recommendersexample: user-item matrixitem 1 item 2 item 3 item 4user1♥ ♥user2♥ ♥ ♥user3♥user4♥ ♥G.Semeraro, ...
collaborative recommenderstarget user: user 4item 1 item 2 item 3 item 4user1♥ ♥user2♥ ♥ ♥user3♥user4♥ ♥G.Semeraro, C.Must...
collaborative recommenderslooking for like-minded usersitem 1 item 2 item 3 item 4user1♥ ♥user2♥ ♥ ♥user3♥user4♥ ♥G.Semera...
collaborative recommendersrecommendationsitem 1 item 2 item 3 item 4user1♥ ♥user2 ♥ ♥ ♥user3♥user4♥ ♥G.Semeraro, C.Musto, ...
Recommender Systemscurrent literatureCollaborative/Social FilteringContent-based FilteringG.Semeraro, C.Musto, - Recommend...
content-based recommendersSuggest items similar to those liked in the past by the userG.Semeraro, C.Musto, - Recommender S...
content-based recommenderskey concepts•Each item has to be described through a set oftextual features•Movie plots, content...
content-based recommendersexample: news recommendationsItems♥♥User ProfileUser isinterested innews articlesabout sports,foo...
content-based recommendersexample: news recommendationsItems♥♥RecommendationsG.Semeraro, C.Musto, - Recommender Systems di...
content-based recommendersexample: news recommendationsItems♥♥RecommendationsXG.Semeraro, C.Musto, - Recommender Systems d...
content-based recommendersexample: news recommendationsItems♥♥RecommendationsXG.Semeraro, C.Musto, - Recommender Systems d...
both collaborative and content-based filteringare not feasible for recommendingfinancial products.G.Semeraro, C.Musto, - Rec...
CF drawback: flockingG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificia...
CF drawback: flockingSimilar users receive similarassets.G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Fin...
CF drawback: flockingToo many users could be movedtowards the same suggestionsG.Semeraro, C.Musto, - Recommender Systems di...
CF drawback: flockingconsequence: price manipulation(as in trader forums)G.Semeraro, C.Musto, - Recommender Systems di Prod...
CBRS drawback: poor contentG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and A...
CBRS drawback: poor contentFeatures describing both assetsand private investors are verypoor (e.g. risk profile)G.Semeraro,...
CBRS drawback: poor contentDifficult to calculate the overlap betweenitem and user (feature) descriptionG.Semeraro, C.Musto...
SolutionKnowledge-based Recommender SystemsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart...
Knowledge-basedRecommender Systems• Useful for complex domains• Computers, cameras, financial products• Need a deep underst...
Knowledge-basedRecommender Systems• Recommendation process• Gets information about user needs;• Exploits the knowledge sto...
we focus on a subclassofknowledge-based recommender systemsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-...
we focus on a subclassofknowledge-based recommender systemscase-based recommender systemsG.Semeraro, C.Musto, - Recommende...
case-based RSs• Knowledge base Case base• Similar problems solved in the past are used asknowledge base• To each case is a...
case-based RSssolving cycleG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and A...
case-based RSsformallyG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artific...
case-based RSsformallyitem modelG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies ...
case-based RSsformallyitem model= (model, producer, megapixel, zoom, etc.)G.Semeraro, C.Musto, - Recommender Systems di Pr...
case-based RSsformallyitem model= (product, asset class, macro asset class, yield, etc.)G.Semeraro, C.Musto, - Recommender...
case-based RSsformallyitem modeluser modelG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart ...
case-based RSsformallyitem modeluser model= (risk profile, experience, goals, etc.)G.Semeraro, C.Musto, - Recommender Syste...
case-based RSsformallyitem modeluser modelsession modelG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Fina...
case-based RSsformallyitem modeluser modelsession modelevaluationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Ba...
case-based RSsformallyitem modeluser modelsession modelevaluationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Ba...
case-based RSsformallyitem modeluser modelsession modelevaluationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Ba...
given a case base, it is necessary todefine similarity metrics tocompute how similar two cases areG.Semeraro, C.Musto, - Re...
case-based RSssimilarityG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Arti...
case-based RSssimilaritystate of the art:heterogeneous euclidean overlap metricG.Semeraro, C.Musto, - Recommender Systems ...
case-based RSssimilarityn featuresG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companie...
case-based RSssimilarityweight of the i-th featureG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziar...
case-based RSssimilaritydistanceG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies ...
the retrieved solutions can berefined and modified before beingproposed to the userG.Semeraro, C.Musto, - Recommender System...
solutions considered as ‘correct’can be stored in the case base andexploited again in the futureG.Semeraro, C.Musto, - Rec...
case-based reasoning forfinancial product recommendationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Fina...
scenario“Scrooge McDuck wants toget richer. He decided toinvest some of his savingsand he asked for help to afinancial advi...
step 1user modelingG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial...
scenarioWhich featuresmay describeScrooge McDuck?G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari...
scenarioUser FeaturesRisk ProfileFinancial ExperienceFinancial SituationInvestment GoalsTemporal GoalsG.Semeraro, C.Musto, ...
scenarioUser FeaturesRisk Profile: LowFinancial Experience: HighFinancial Situation:Very HighInvestment Goals: MediumTempor...
scenarioUser FeaturesRisk Profile: LowFinancial Experience: HighFinancial Situation:Very HighInvestment Goals: MediumTempor...
in a classical pipeline, the target user would have receiveda “model” porfolio tailored on her profileG.Semeraro, C.Musto, ...
in a pipeline fostered by a recommender system, thefinancial advisor can analyze the portfolios proposed tosimilar users.G....
step 2retrieval of similar usersG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies ...
retrievalcase baseG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial ...
retrieval0.30.70.90.1G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artifici...
retrieval0.30.70.90.1similarity scoreG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Compa...
retrieval0.30.70.90.1G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artifici...
retrieval0.30.70.90.1helpful casesG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companie...
in real-world scenarios, the case base containsmuch more helpful casesusually, it is necessary to introduce some strategy ...
case-based RSsdifferentiate solutionsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Compa...
to each case is assigned an agreed portofliothe set of the portfolios represents the set of the possible recommendationsG.S...
retrievalObbligazionario Euro Bot 30%Obbligazionario HighYield 15%Obbligazionario Globale 15%Azionario Europa 20%Azionario...
how to combine the retrieved cases?several strategies availableG.Semeraro, C.Musto, - Recommender Systems di Prodotti Banc...
step 3revise and reviewG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artifi...
revise and reviewObbligazionario Euro Bot 30%Obbligazionario HighYield 12.5%Obbligazionario Globale 18.5%Azionario Europa ...
revise and reviewclustering (proposing diversified solutions)Obbligazionario Euro Bot 30%Obbligazionario HighYield 15%Obbli...
financial advisor and private investor canfurther discuss the portfolioG.Semeraro, C.Musto, - Recommender Systems di Prodot...
revise and reviewOriginal Discussed GapObbligazionarioEuro Bot 30% 30%ObbligazionarioHighYield 12.5% 10% -2.5%Obbligaziona...
an evaluation score is finally assigned to theproposed solutionyield, e.g.retainG.Semeraro, C.Musto, - Recommender Systems ...
good solutions are stored in the case base andexploited for future recommendationsretainG.Semeraro, C.Musto, - Recommender...
case baseG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intellige...
(new) case baseG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Int...
recap• Case-based reasoning for recommending financial products• Goal: to help financial promoters considering solutions pro...
open points• Research is not over :-)• How to model investors?• How to model portfolios?• Which features should be assigne...
questions?Cataldo Musto, Ph.Dcataldo.musto@uniba.itprof. Giovanni Semerarogiovanni.semeraro@uniba.itG.Semeraro, C.Musto, -...
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Financial Recommender Systems

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Smart Companies and Artificial Intelligence - AI*IA (Associazione Italiana Intelligenza Artificiale) event.

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Financial Recommender Systems

  1. 1. Recommender Systems diprodotti bancari-finanziariGiovanni Semeraro , Cataldo MustoSmart Companies and Artificial IntelligenceFirenze (Italy) - May 14, 2013
  2. 2. outline• Background• Needs Allocation• Anima SGR’s Progettometro• From Needs to Asset Allocation: recommendersystems• State of the art: Collaborative filtering, content-based filtering• Our choice: case-based reasoning• A possible use caseG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  3. 3. BackgroundObjectWay Finance-as-a-ServiceSmart Application Software & Services for FinancialServices OperatorsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  4. 4. BackgroundG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  5. 5. BackgroundG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  6. 6. BackgroundWealth Management reference frameworkG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  7. 7. Current work•Progettometro• 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 Artificial Intelligence, Firenze (Italy) - May 14, 2013
  8. 8. Progettometroprofile selectionG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  9. 9. Progettometrofive stereotypesG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  10. 10. Progettometroinput informationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  11. 11. ProgettometroprojectionG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  12. 12. Progettometrolife projectsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  13. 13. Progettometroinformation about life projectsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  14. 14. Progettometroupdated projectionG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  15. 15. Progettometrofinal reportG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  16. 16. from needsto assetallocationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  17. 17. researchquestionis it possible to evolve aneeds allocation tooltowards an assetallocation one byexploiting artificialinteligence techniques?G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  18. 18. our proposal: personalizationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  19. 19. to introduce an holistic vision of the userG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  20. 20. to adapt asset portfolioson the ground of personal user profile and needsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  21. 21. to introduce a tool helpful for supportingfinancial advisors (not for private investors!)G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  22. 22. SolutionRecommender SystemsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  23. 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 Artificial Intelligence, Firenze (Italy) - May 14, 2013
  24. 24. definitionRecommender 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 Artificial Intelligence, Firenze (Italy) - May 14, 2013
  25. 25. does it fit 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 Artificial Intelligence, Firenze (Italy) - May 14, 2013
  26. 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 flights, hotels, and rental cars; by social-networking sites such as LinkedIn; by video sites like Netflix to recommend movies and TV shows, and by music, news, and foodsites to suggest songs, news stories, and restaurants, respectively. Even financial-services firms 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 Artificial Intelligence, Firenze (Italy) - May 14, 2013
  27. 27. Netflix.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 flights, hotels, and rental cars; by social-networking sites such as LinkedIn; by video sites like Netflix to recommend movies and TV shows, and by music, news, and foodsites to suggest songs, news stories, and restaurants, respectively. Even financial-services firms 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 Artificial Intelligence, Firenze (Italy) - May 14, 2013
  28. 28. Recommender Systemscurrent literatureG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  29. 29. Recommender Systemscurrent literatureCollaborative/Social FilteringContent-based FilteringG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  30. 30. Recommender Systemscurrent literatureCollaborative/Social FilteringContent-based FilteringG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  31. 31. collaborative recommendersSuggest items that similar users liked in the past.G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  32. 32. collaborative recommendersSuggest items that similar users liked in the past.It capitalizes the‘word of mouth’ effectG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  33. 33. collaborative recommendersexample: user-item matrixitem 1 item 2 item 3 item 4user1♥ ♥user2♥ ♥ ♥user3♥user4♥ ♥G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  34. 34. collaborative recommenderstarget user: user 4item 1 item 2 item 3 item 4user1♥ ♥user2♥ ♥ ♥user3♥user4♥ ♥G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  35. 35. collaborative recommenderslooking for like-minded usersitem 1 item 2 item 3 item 4user1♥ ♥user2♥ ♥ ♥user3♥user4♥ ♥G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  36. 36. collaborative recommendersrecommendationsitem 1 item 2 item 3 item 4user1♥ ♥user2 ♥ ♥ ♥user3♥user4♥ ♥G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  37. 37. Recommender Systemscurrent literatureCollaborative/Social FilteringContent-based FilteringG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  38. 38. content-based recommendersSuggest items similar to those liked in the past by the userG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  39. 39. content-based recommenderskey concepts•Each item has to be described through a set oftextual features•Movie plots, content of news, book summaries,etc.G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  40. 40. content-based recommendersexample: news recommendationsItems♥♥User ProfileUser isinterested innews articlesabout sports,football,cycling, etc.G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  41. 41. content-based recommendersexample: news recommendationsItems♥♥RecommendationsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  42. 42. content-based recommendersexample: news recommendationsItems♥♥RecommendationsXG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  43. 43. content-based recommendersexample: news recommendationsItems♥♥RecommendationsXG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013.
  44. 44. both collaborative and content-based filteringare not feasible for recommendingfinancial products.G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  45. 45. CF drawback: flockingG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  46. 46. CF drawback: flockingSimilar users receive similarassets.G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  47. 47. CF drawback: flockingToo many users could be movedtowards the same suggestionsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  48. 48. CF drawback: flockingconsequence: price manipulation(as in trader forums)G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  49. 49. CBRS drawback: poor contentG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  50. 50. CBRS drawback: poor contentFeatures describing both assetsand private investors are verypoor (e.g. risk profile)G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  51. 51. CBRS drawback: poor contentDifficult to calculate the overlap betweenitem and user (feature) descriptionG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  52. 52. SolutionKnowledge-based Recommender SystemsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  53. 53. Knowledge-basedRecommender Systems• Useful for complex domains• Computers, cameras, financial products• Need a deep understanding of the domain• Typically encoded by experts• Focused on producing correct recommendations• Focused on explanations of the recommendationsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  54. 54. Knowledge-basedRecommender Systems• Recommendation process• Gets information about user needs;• Exploits the knowledge stored in the KB to meetuser needs;• (eventually) ask user to relax or to modify some ofthe needs (e.g. expected interest rate);• Proposes a recommendation.G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  55. 55. we focus on a subclassofknowledge-based recommender systemsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  56. 56. we focus on a subclassofknowledge-based recommender systemscase-based recommender systemsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  57. 57. case-based RSs• Knowledge base Case base• Similar problems solved in the past are used asknowledge base• To each case is assigned a set of features• User needs• Description of the case• The recommendation process consists of theretrieval and the adaptation of similar alreadysolved casesG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  58. 58. case-based RSssolving cycleG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  59. 59. case-based RSsformallyG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  60. 60. case-based RSsformallyitem modelG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  61. 61. case-based RSsformallyitem model= (model, producer, megapixel, zoom, etc.)G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  62. 62. case-based RSsformallyitem model= (product, asset class, macro asset class, yield, etc.)G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  63. 63. case-based RSsformallyitem modeluser modelG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  64. 64. case-based RSsformallyitem modeluser model= (risk profile, experience, goals, etc.)G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  65. 65. case-based RSsformallyitem modeluser modelsession modelG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  66. 66. case-based RSsformallyitem modeluser modelsession modelevaluationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  67. 67. case-based RSsformallyitem modeluser modelsession modelevaluationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  68. 68. case-based RSsformallyitem modeluser modelsession modelevaluationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013$ 174.18http://tinyurl.com/d3nt2fq
  69. 69. given a case base, it is necessary todefine similarity metrics tocompute how similar two cases areG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  70. 70. case-based RSssimilarityG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  71. 71. case-based RSssimilaritystate of the art:heterogeneous euclidean overlap metricG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  72. 72. case-based RSssimilarityn featuresG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  73. 73. case-based RSssimilarityweight of the i-th featureG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  74. 74. case-based RSssimilaritydistanceG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  75. 75. the retrieved solutions can berefined and modified before beingproposed to the userG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  76. 76. solutions considered as ‘correct’can be stored in the case base andexploited again in the futureG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  77. 77. case-based reasoning forfinancial product recommendationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  78. 78. scenario“Scrooge McDuck wants toget richer. He decided toinvest some of his savingsand he asked for help to afinancial advisor”G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  79. 79. step 1user modelingG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  80. 80. scenarioWhich featuresmay describeScrooge McDuck?G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  81. 81. scenarioUser FeaturesRisk ProfileFinancial ExperienceFinancial SituationInvestment GoalsTemporal GoalsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  82. 82. scenarioUser FeaturesRisk Profile: LowFinancial Experience: HighFinancial Situation:Very HighInvestment Goals: MediumTemporal Goals: MediumG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  83. 83. scenarioUser FeaturesRisk Profile: LowFinancial Experience: HighFinancial Situation:Very HighInvestment Goals: MediumTemporal Goals: MediumMiFID-basedG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  84. 84. in a classical pipeline, the target user would have receiveda “model” porfolio tailored on her profileG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  85. 85. in a pipeline fostered by a recommender system, thefinancial advisor can analyze the portfolios proposed tosimilar users.G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  86. 86. step 2retrieval of similar usersG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  87. 87. retrievalcase baseG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  88. 88. retrieval0.30.70.90.1G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  89. 89. retrieval0.30.70.90.1similarity scoreG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  90. 90. retrieval0.30.70.90.1G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  91. 91. retrieval0.30.70.90.1helpful casesG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  92. 92. in real-world scenarios, the case base containsmuch more helpful casesusually, it is necessary to introduce some strategy to diversify similar casesG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  93. 93. case-based RSsdifferentiate solutionsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  94. 94. to each case is assigned an agreed portofliothe set of the portfolios represents the set of the possible recommendationsG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  95. 95. retrievalObbligazionario Euro Bot 30%Obbligazionario HighYield 15%Obbligazionario Globale 15%Azionario Europa 20%Azionario Paesi Emergenti 12%Flessibili BassaVolatilità 8%Obbligazionario Euro Bot 30%Obbligazionario HighYield 10%Obbligazionario Globale 22%Azionario Europa 23%Azionario Paesi Emergenti 7%Flessibili BassaVolatilità 8%G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  96. 96. how to combine the retrieved cases?several strategies availableG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  97. 97. step 3revise and reviewG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  98. 98. revise and reviewObbligazionario Euro Bot 30%Obbligazionario HighYield 12.5%Obbligazionario Globale 18.5%Azionario Europa 21.5%Azionario Paesi Emergenti 9.5%Flessibili BassaVolatilità 8%rough averageG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  99. 99. revise and reviewclustering (proposing diversified solutions)Obbligazionario Euro Bot 30%Obbligazionario HighYield 15%Obbligazionario Globale 15%Azionario Europa 20%Azionario Paesi Emergenti 12%Flessibili BassaVolatilità 8%Obbligazionario Euro Bot 30%Obbligazionario HighYield 10%Obbligazionario Globale 22%Azionario Europa 23%Azionario Paesi Emergenti 7%Flessibili BassaVolatilità 8%G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  100. 100. financial advisor and private investor canfurther discuss the portfolioG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  101. 101. revise and reviewOriginal Discussed GapObbligazionarioEuro Bot 30% 30%ObbligazionarioHighYield 12.5% 10% -2.5%ObbligazionarioGlobale 18.5% 20% +1.5%Azionario Europa 21.5% 24% +2.5%Azionario PaesiEmergenti 9.5% 8% -1.5%Flessibili BassaVolatilità 8% 8%interactive personalizationG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  102. 102. an evaluation score is finally assigned to theproposed solutionyield, e.g.retainG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  103. 103. good solutions are stored in the case base andexploited for future recommendationsretainG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  104. 104. case baseG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  105. 105. (new) case baseG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  106. 106. recap• Case-based reasoning for recommending financial products• Goal: to help financial promoters considering solutions proposed tosimilar users• Case base: user features and agreed portfolios• User features: risk profile (MiFID questionnaire), financialexperience, financial situation, investment goals, temporal goals• Portfolio: model portfolio, macro asset classes, asset classdistribution, products, etc.• Similarity: HEOM to retrieve similar ‘cases’• Revise and Review: several strategies for cases aggregation andcombination• Retain: considering external factors (e.g. yield) to evaluate theeffectiveness of the proposed solutionG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  107. 107. open points• Research is not over :-)• How to model investors?• How to model portfolios?• Which features should be assigned a greater weight?• Which one is the best strategy to aggregaterecommended portfolios?• How to model temporal constraints?• How to consider contextual information (e.g.,stock market situation) ?G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
  108. 108. questions?Cataldo Musto, Ph.Dcataldo.musto@uniba.itprof. Giovanni Semerarogiovanni.semeraro@uniba.itG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013

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