Personalized Wealth Management through Case-based Recommender SystemsCataldo Musto
The document describes a case-based recommender system for personalized wealth management. It discusses how recommender systems can be used to adapt asset portfolios to a user's personal profile and needs by retrieving and adapting solutions from similar past cases. A case is defined as a formalized description of a user, a portfolio they were recommended, and an evaluation of that recommendation. The system aims to solve new users' investment problems by analogizing to past similar cases through a cycle of retrieving and adapting recommended portfolios.
Case-based Recommender Systems for Personalized Finance AdvisoryCataldo Musto
Case-based Recommender Systems for Personalized Finance Advisory - talk by Cataldo Musto and Giovanni Semeraro - workshop FinRec 2015 - 1st International Workshop on Personalization & Recommender Systems in Financial Services, Graz, Austria, Apr 16th 2015
Workshop Website: http://finrec.ist.tugraz.at
Building a Recommendation Engine - An example of a product recommendation engineNYC Predictive Analytics
This document provides an example of building a predictive model for product recommendations. It outlines using a k-Nearest Neighbor (kNN) algorithm and singular value decomposition (SVD) for dimensionality reduction on order history and cart event data to create a recommendation engine. It discusses selecting features from the data, normalizing the data, using kNN to find similar items, and reducing the data dimensions with SVD before applying kNN. It also introduces using a synthetic dataset to test and tune the model and compares different experimental setups like random, kNN, and SVD+kNN recommendations. The goal is to increase business metrics like revenue, conversion rate, and average order value through effective product recommendations.
“SETU” is an unprecedented application designed to guard you. Application can be used for reporting crime, distress messaging and to know traffic rules you should follow for your safety, female can use this application to know their rights and laws related to them.
"Ecommerce Trends 2023" is a forward-thinking presentation that explores the latest developments, strategies, and technologies shaping the future of online retail. Attendees will gain insights into the key trends and emerging opportunities in the e-commerce industry, including the growing importance of mobile commerce, the rise of social commerce, the impact of AI and machine learning, and the increasing emphasis on sustainability and ethical practices. Through real-world examples and expert analysis, this presentation will provide valuable guidance for businesses looking to stay ahead of the curve in the fast-evolving world of e-commerce.
Holistic User Modeling for Personalized Services in Smart CitiesCataldo Musto
The document describes a holistic user modeling approach for building personalized user profiles. Data is collected from various sources like social media, smartphones, and fitness trackers. This data is processed and used to populate facets of a user profile like demographics, interests, behaviors, and physical states. The profiles are made available via an API to power personalized services while giving users control over their data and privacy settings. A platform called Myrror was developed to create and manage these holistic user profiles.
Personalized Wealth Management through Case-based Recommender SystemsCataldo Musto
The document describes a case-based recommender system for personalized wealth management. It discusses how recommender systems can be used to adapt asset portfolios to a user's personal profile and needs by retrieving and adapting solutions from similar past cases. A case is defined as a formalized description of a user, a portfolio they were recommended, and an evaluation of that recommendation. The system aims to solve new users' investment problems by analogizing to past similar cases through a cycle of retrieving and adapting recommended portfolios.
Case-based Recommender Systems for Personalized Finance AdvisoryCataldo Musto
Case-based Recommender Systems for Personalized Finance Advisory - talk by Cataldo Musto and Giovanni Semeraro - workshop FinRec 2015 - 1st International Workshop on Personalization & Recommender Systems in Financial Services, Graz, Austria, Apr 16th 2015
Workshop Website: http://finrec.ist.tugraz.at
Building a Recommendation Engine - An example of a product recommendation engineNYC Predictive Analytics
This document provides an example of building a predictive model for product recommendations. It outlines using a k-Nearest Neighbor (kNN) algorithm and singular value decomposition (SVD) for dimensionality reduction on order history and cart event data to create a recommendation engine. It discusses selecting features from the data, normalizing the data, using kNN to find similar items, and reducing the data dimensions with SVD before applying kNN. It also introduces using a synthetic dataset to test and tune the model and compares different experimental setups like random, kNN, and SVD+kNN recommendations. The goal is to increase business metrics like revenue, conversion rate, and average order value through effective product recommendations.
“SETU” is an unprecedented application designed to guard you. Application can be used for reporting crime, distress messaging and to know traffic rules you should follow for your safety, female can use this application to know their rights and laws related to them.
"Ecommerce Trends 2023" is a forward-thinking presentation that explores the latest developments, strategies, and technologies shaping the future of online retail. Attendees will gain insights into the key trends and emerging opportunities in the e-commerce industry, including the growing importance of mobile commerce, the rise of social commerce, the impact of AI and machine learning, and the increasing emphasis on sustainability and ethical practices. Through real-world examples and expert analysis, this presentation will provide valuable guidance for businesses looking to stay ahead of the curve in the fast-evolving world of e-commerce.
Holistic User Modeling for Personalized Services in Smart CitiesCataldo Musto
The document describes a holistic user modeling approach for building personalized user profiles. Data is collected from various sources like social media, smartphones, and fitness trackers. This data is processed and used to populate facets of a user profile like demographics, interests, behaviors, and physical states. The profiles are made available via an API to power personalized services while giving users control over their data and privacy settings. A platform called Myrror was developed to create and manage these holistic user profiles.
Setu ppt. Detailed information of application.Kashyap Chauhan
Currently after launching the application in Surat City & Rural , Junagadh , Rajkot City & Jamnagar Districts for android & Iphone smart phone users, more than 1,00,000 users has registered themselves with SETU and making an efficient use of this application.
Interpret is a research and consulting firm that focuses on the intersections of media, technology, advertising, and consumer behavior. It offers custom research solutions and syndicated product solutions to deliver innovative, consumer-driven insights. Interpret prides itself on having expertise across many research methodologies and experience working with clients in various industries. It provides consulting services, market reports, and data access to help clients understand trends and profit from insights into digital media and emerging technologies.
A new FinTech hub is emerging in Europe centered around Barcelona, Spain. Similar to how the first Industrial Revolution spread from certain regions, the FinTech Revolution is also spreading from its initial hubs in San Francisco and London. Just as certain regions had the necessary "social capability" like skills and infrastructure to import and develop new technologies during the Industrial Revolution, places like Barcelona have similar qualities that are allowing the FinTech sector to grow there. Barcelona offers proximity and connections to Latin America that make it appealing for FinTech companies focused on those markets. The new FinTech hub forming in Barcelona shows how these types of disruptive industries can spread from their initial centers as new regions develop the right environments for growth.
Digital Brand Reputation and Social Media Monitoring: two key factors in the ...Tommaso Bartali
Analysis concerning two key factors in the social web era: the digital brand reputation and the social media monitoring.
Through the monitoring platform Talkwalker have been followed for three months nine fashion luxury brands with the object to answer these questions:
Could a brand reduce the risk of a possible online reputational crisis by deciding to use, in the social media area, a simple strategy based on a presence without any type of activity/interaction with the users?
Do social media marketing activities really encourage the customer engagement?
Industrio Hardware Accelerator - Class Summer 2014Jari Ognibeni
Industrio is a hardware startup accelerator located in Trento, Italy. They run a 6-month acceleration program where they provide seed funding of up to €50,000 along with access to mentors, prototyping facilities, and their network. They are accepting applications for their Summer 2014 program. Eligible startups are those with innovative hardware products in sectors like industry automation, healthcare, agriculture, and more. The program helps teams develop their prototypes into real products and startups through modules focused on product development, market fit testing, industrialization, and growth strategy. Startups receive equity investment in exchange for 15% equity in their company.
Fernando Angulo will give a keynote on ecommerce trends for 2023 at DigiMarConSoutheast. The presentation will cover the growth of the global ecommerce industry, shifts in online shopper behavior, popular product categories and retailers, and trends that will define 2023 like social commerce, new payment methods, web 3.0 technologies, and the increasing role of artificial intelligence. Emerging trends include shopping and advertising on social media, programmatic advertising using AI, digital wallets and biometric authentication, and chatbots that can answer complex questions and generate content.
In this document you can find the output of a technology-push innovation process. Starting from the market analysis, till the definition of the UX and the BM to adopt to win the market, including the definition of the ecosystem where the technology will move.
Attentio is a company based in Brussels that provides social media monitoring and analysis services. It pioneered Word of Mouth Intelligence solutions and has strong experience working with companies in industries like pharma, consumer electronics, and automotive. Attentio's technology and tools allow companies to track discussions on blogs, videos and forums to understand brand perceptions, monitor trends, identify influencers and measure campaign effectiveness. It helps companies with tasks like competitive intelligence, crisis management, and content strategy.
4th Mobile Commerce Summit Asia 2011 Call For PapersNeoedge Pte Ltd
The document announces the 4th Mobile Commerce Summit Asia 2011 conference and calls for speakers, sponsors, and exhibitors. It provides information on submission deadlines, topics that will be discussed related to mobile payments, banking, and commerce. Interested parties are asked to submit an abstract, contact information, and details on potential speaking, moderating, or panel participation. Sponsorship opportunities are also outlined. The goal is to gather industry leaders and discuss growth in Asian mobile commerce.
Dataquest Insight The Top 10 Consumer Mobile Applications In 2012guestb92038
This document summarizes a report on the top 10 consumer mobile applications in 2012. It identifies mobile money transfer, location-based services, and mobile search as the top three applications. The report analyzes market trends for each application, provides examples, and offers recommendations for industry players on how to take advantage of growing opportunities in mobile applications. Key findings include that user experience is important for competitive advantage, and control of the mobile "ecosystem" will benefit revenue and user loyalty.
Digital Marketing Strategies to catch the Omin-Channel customerFederico Gasparotto
The consumer is evolving: eCommerce does not exists anymore.... It doesn’t make sense to talk about mobile or social: it only exists the «Commerce» handled through different tools, in different contexts.
E-Commerce is one of the business pillar of the Omni-Channel and customer-centric company.
The adoption of new tools, new formats and new strategies becomes essential to be more relevant and excel in the market.
Improve Customer Engagement With An Intelligent Application StrategyCI&T
1) The document discusses how companies can improve customer engagement through an intelligent application strategy.
2) It recommends listening to customers continuously to understand their behavior, sentiments, and context. Companies should then use this information to learn about customer needs and expectations.
3) The strategy involves using analytics on customer data from various touchpoints to identify patterns that maximize customer happiness and conversion rates. Applications should engage customers by speaking to their needs based on these patterns.
Best Practice Guide Mobile Apps - Marketing Overviewcrisdresch
This is a best practice guide on Mobile Applications, focused on the Marketing overview. It talks about the mobile market and the planning and strategy behind the mobile apps. Hope you enjoy it.
Increase Customer Engagement through Immersive Reality.Caroline Russo
Thomas Edison State University student Caroline Russo presented on how augmented reality and virtual reality can increase customer engagement. Russo discussed how AR is being used currently in retail settings to allow virtual try-ons of products. Examples provided included IKEA, Adidas, and makeup apps. Russo predicted AR use will grow in industries like education, healthcare, and real estate. The future may see AR used for GPS navigation and to enhance learning and visualizations in education and healthcare.
The drive to eliminate human fallibility has made artificial intelligence driven to the forefront of research and development. Its applications range from sorting what gets shown on your social media newsfeed to self-driving cars. It’s also expected to have a major impact in Fintech due to potential of game changing insights that can be derived from the sheer volume of data that humanity is generating. Enterprising ventures are banking on it to expose the gap in the market that has become increasingly small due to competition.
The document describes WeSpline, a social network and search engine designed to connect startups, enterprises, investors, and other stakeholders. It aims to foster business and innovation through features like an intelligent search engine, communications tools, and AI recommendations. The platform will help users find partners, clients, mentors, and opportunities. Revenue will come from free basic services and premium subscriptions, as well as targeted ads.
Demonetization, IoT and related thoughts! by "Sherlin Mathew" from "Cogizant" The presentation was done at #doppa17 DevOps++ Global Summit 2017. All the copyrights are reserved with the author
InsurTechNews: Follow the money | FinTechStage Milan 2017Andrea Silvello
InsurTechNews is an online media company focused exclusively on insurtech. It creates monthly lists of top 50 insurtech influencers and sends a weekly newsletter with headlines and events. The site also features original content from thought leaders, interviews, and reports on trends. InsurTechNews sees over 130,000 users annually and 300% yearly growth in pageviews. It is considered an influential brand in the insurtech space based on various lists and has over 12,000 Twitter followers. Venture Scanner data shows there are over 1,200 insurtech startups worldwide pursuing 14 categories that have raised a total of $18 billion in funding to date.
Setu ppt. Detailed information of application.Kashyap Chauhan
Currently after launching the application in Surat City & Rural , Junagadh , Rajkot City & Jamnagar Districts for android & Iphone smart phone users, more than 1,00,000 users has registered themselves with SETU and making an efficient use of this application.
Interpret is a research and consulting firm that focuses on the intersections of media, technology, advertising, and consumer behavior. It offers custom research solutions and syndicated product solutions to deliver innovative, consumer-driven insights. Interpret prides itself on having expertise across many research methodologies and experience working with clients in various industries. It provides consulting services, market reports, and data access to help clients understand trends and profit from insights into digital media and emerging technologies.
A new FinTech hub is emerging in Europe centered around Barcelona, Spain. Similar to how the first Industrial Revolution spread from certain regions, the FinTech Revolution is also spreading from its initial hubs in San Francisco and London. Just as certain regions had the necessary "social capability" like skills and infrastructure to import and develop new technologies during the Industrial Revolution, places like Barcelona have similar qualities that are allowing the FinTech sector to grow there. Barcelona offers proximity and connections to Latin America that make it appealing for FinTech companies focused on those markets. The new FinTech hub forming in Barcelona shows how these types of disruptive industries can spread from their initial centers as new regions develop the right environments for growth.
Digital Brand Reputation and Social Media Monitoring: two key factors in the ...Tommaso Bartali
Analysis concerning two key factors in the social web era: the digital brand reputation and the social media monitoring.
Through the monitoring platform Talkwalker have been followed for three months nine fashion luxury brands with the object to answer these questions:
Could a brand reduce the risk of a possible online reputational crisis by deciding to use, in the social media area, a simple strategy based on a presence without any type of activity/interaction with the users?
Do social media marketing activities really encourage the customer engagement?
Industrio Hardware Accelerator - Class Summer 2014Jari Ognibeni
Industrio is a hardware startup accelerator located in Trento, Italy. They run a 6-month acceleration program where they provide seed funding of up to €50,000 along with access to mentors, prototyping facilities, and their network. They are accepting applications for their Summer 2014 program. Eligible startups are those with innovative hardware products in sectors like industry automation, healthcare, agriculture, and more. The program helps teams develop their prototypes into real products and startups through modules focused on product development, market fit testing, industrialization, and growth strategy. Startups receive equity investment in exchange for 15% equity in their company.
Fernando Angulo will give a keynote on ecommerce trends for 2023 at DigiMarConSoutheast. The presentation will cover the growth of the global ecommerce industry, shifts in online shopper behavior, popular product categories and retailers, and trends that will define 2023 like social commerce, new payment methods, web 3.0 technologies, and the increasing role of artificial intelligence. Emerging trends include shopping and advertising on social media, programmatic advertising using AI, digital wallets and biometric authentication, and chatbots that can answer complex questions and generate content.
In this document you can find the output of a technology-push innovation process. Starting from the market analysis, till the definition of the UX and the BM to adopt to win the market, including the definition of the ecosystem where the technology will move.
Attentio is a company based in Brussels that provides social media monitoring and analysis services. It pioneered Word of Mouth Intelligence solutions and has strong experience working with companies in industries like pharma, consumer electronics, and automotive. Attentio's technology and tools allow companies to track discussions on blogs, videos and forums to understand brand perceptions, monitor trends, identify influencers and measure campaign effectiveness. It helps companies with tasks like competitive intelligence, crisis management, and content strategy.
4th Mobile Commerce Summit Asia 2011 Call For PapersNeoedge Pte Ltd
The document announces the 4th Mobile Commerce Summit Asia 2011 conference and calls for speakers, sponsors, and exhibitors. It provides information on submission deadlines, topics that will be discussed related to mobile payments, banking, and commerce. Interested parties are asked to submit an abstract, contact information, and details on potential speaking, moderating, or panel participation. Sponsorship opportunities are also outlined. The goal is to gather industry leaders and discuss growth in Asian mobile commerce.
Dataquest Insight The Top 10 Consumer Mobile Applications In 2012guestb92038
This document summarizes a report on the top 10 consumer mobile applications in 2012. It identifies mobile money transfer, location-based services, and mobile search as the top three applications. The report analyzes market trends for each application, provides examples, and offers recommendations for industry players on how to take advantage of growing opportunities in mobile applications. Key findings include that user experience is important for competitive advantage, and control of the mobile "ecosystem" will benefit revenue and user loyalty.
Digital Marketing Strategies to catch the Omin-Channel customerFederico Gasparotto
The consumer is evolving: eCommerce does not exists anymore.... It doesn’t make sense to talk about mobile or social: it only exists the «Commerce» handled through different tools, in different contexts.
E-Commerce is one of the business pillar of the Omni-Channel and customer-centric company.
The adoption of new tools, new formats and new strategies becomes essential to be more relevant and excel in the market.
Improve Customer Engagement With An Intelligent Application StrategyCI&T
1) The document discusses how companies can improve customer engagement through an intelligent application strategy.
2) It recommends listening to customers continuously to understand their behavior, sentiments, and context. Companies should then use this information to learn about customer needs and expectations.
3) The strategy involves using analytics on customer data from various touchpoints to identify patterns that maximize customer happiness and conversion rates. Applications should engage customers by speaking to their needs based on these patterns.
Best Practice Guide Mobile Apps - Marketing Overviewcrisdresch
This is a best practice guide on Mobile Applications, focused on the Marketing overview. It talks about the mobile market and the planning and strategy behind the mobile apps. Hope you enjoy it.
Increase Customer Engagement through Immersive Reality.Caroline Russo
Thomas Edison State University student Caroline Russo presented on how augmented reality and virtual reality can increase customer engagement. Russo discussed how AR is being used currently in retail settings to allow virtual try-ons of products. Examples provided included IKEA, Adidas, and makeup apps. Russo predicted AR use will grow in industries like education, healthcare, and real estate. The future may see AR used for GPS navigation and to enhance learning and visualizations in education and healthcare.
The drive to eliminate human fallibility has made artificial intelligence driven to the forefront of research and development. Its applications range from sorting what gets shown on your social media newsfeed to self-driving cars. It’s also expected to have a major impact in Fintech due to potential of game changing insights that can be derived from the sheer volume of data that humanity is generating. Enterprising ventures are banking on it to expose the gap in the market that has become increasingly small due to competition.
The document describes WeSpline, a social network and search engine designed to connect startups, enterprises, investors, and other stakeholders. It aims to foster business and innovation through features like an intelligent search engine, communications tools, and AI recommendations. The platform will help users find partners, clients, mentors, and opportunities. Revenue will come from free basic services and premium subscriptions, as well as targeted ads.
Demonetization, IoT and related thoughts! by "Sherlin Mathew" from "Cogizant" The presentation was done at #doppa17 DevOps++ Global Summit 2017. All the copyrights are reserved with the author
InsurTechNews: Follow the money | FinTechStage Milan 2017Andrea Silvello
InsurTechNews is an online media company focused exclusively on insurtech. It creates monthly lists of top 50 insurtech influencers and sends a weekly newsletter with headlines and events. The site also features original content from thought leaders, interviews, and reports on trends. InsurTechNews sees over 130,000 users annually and 300% yearly growth in pageviews. It is considered an influential brand in the insurtech space based on various lists and has over 12,000 Twitter followers. Venture Scanner data shows there are over 1,200 insurtech startups worldwide pursuing 14 categories that have raised a total of $18 billion in funding to date.
Intelligenza Artificiale e Social Media - Monitoraggio della Farnesina e La M...Cataldo Musto
Convegno a Porte Chiuse dell'Associazione Italiana per l'Intelligenza Artificiale insieme al Ministero per gli Affari Esteri e la Cooperazione Internazionale - 30 Giugno 2021
Exploring the Effects of Natural Language Justifications in Food Recommender ...Cataldo Musto
Cataldo Musto, Alain D. Starke, Christoph Trattner, Amon Rapp, and Giovanni Semeraro. 2021. Exploring the Effects of Natural Language Justifications in Food Recommender Systems. In Proceedings of the 29th ACM
Conference on User Modeling, Adaptation and Personalization (UMAP ’21), June 21–25, 2021, Utrecht, Netherlands. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3450613.3456827
Exploiting Distributional Semantics Models for Natural Language Context-aware...Cataldo Musto
The document proposes a methodology to generate context-aware natural language justifications for recommender systems by exploiting distributional semantics models. It involves learning a vector space representation of contexts, identifying the most suitable review excerpts given an item and context, and combining excerpts to form a justification. The goal is to produce justifications that vary based on different consumption contexts and are independent of the underlying recommendation model.
Towards a Knowledge-aware Food Recommender System Exploiting Holistic User Mo...Cataldo Musto
This document presents a knowledge-aware food recommender system that uses holistic user models. It aims to address limitations of content-based and collaborative filtering approaches. The proposed system uses a profiler to build comprehensive user profiles incorporating demographics, behaviors, health data and domain knowledge. Recipes are then filtered and ranked using this user information and food knowledge rules. An evaluation with 200 MTurk participants found users with health goals preferred recipes recommended by the holistic model over popular recipes. The study provides initial evidence the approach can better support healthy eating goals.
Natural Language Justifications for Recommender Systems Exploiting Text Summa...Cataldo Musto
The document describes a method for generating natural language justifications for recommender systems using text summarization and sentiment analysis techniques. It discusses prior approaches using descriptive properties or review-based features, and proposes a new approach that exploits automatic text summarization of user reviews. The proposed method involves extracting aspects from reviews, ranking aspects based on frequency, sentiment, and importance, and generating a summary justification using a centroid-based text summarization algorithm on filtered sentences from reviews. The goal is to provide a higher-quality justification by summarizing relevant information from multiple reviews.
Explanation Strategies - Advances in Content-based Recommender SystemCataldo Musto
This document summarizes a talk on content-based explanation strategies for recommender systems. It discusses using linked open data to provide explanations based on descriptive properties of recommended items and the user's preferences. It also discusses using sentiment analysis and text summarization of user reviews to justify recommendations. The talk presents the ExpLOD framework for generating explanations from linked open data and evaluates it in a user study comparing it to popularity-based and non-personalized baselines.
Justifying Recommendations through Aspect-based Sentiment Analysis of Users R...Cataldo Musto
The document describes a method for justifying recommendations through aspect-based sentiment analysis of users' reviews. It involves extracting aspects from reviews using natural language processing, ranking aspects by relevance and sentiment polarity, and generating a natural language justification using positive excerpts about high-ranking aspects. An experimental evaluation with 286 subjects compared justifications from different combinations of parameters and to a feature-based baseline. Results showed that review-based justifications scored higher than the baseline in terms of transparency, persuasion, engagement, trust and effectiveness.
A Framework for Holistic User Modeling Merging Heterogeneous Digital FootprintsCataldo Musto
A Framework for Holistic User Modeling Merging Heterogeneous Digital Footprints - HUM 2018 – Holistic User Modeling Workshop jointly held with
UMAP 2018 – 26th International
Conference on User Modeling,
Adaptation and Personalization
Singapore - July 8, 2018
Semantics-aware Recommender Systems Exploiting Linked Open Data and Graph-bas...Cataldo Musto
The document discusses semantics-aware recommender systems that exploit linked open data and graph-based features. It proposes combining heterogeneous groups of features, including popularity, collaborative, content, linked open data, and graph-based features to learn representations of items for recommendation. The approach is evaluated on movie recommendation datasets to assess the impact of incorporating linked open data and graph-based features into a hybrid recommendation framework.
A Multi-Criteria Recommender System Exploiting Aspect-based Sentiment Analysi...Cataldo Musto
The document describes a multi-criteria recommender system that exploits aspect-based sentiment analysis of user reviews. It involves a two-step methodology: 1) performing aspect extraction and sentiment analysis on user reviews using an algorithm based on SABRE to identify aspects, sub-aspects, and sentiment, and 2) creating and populating a multi-criteria data model with the extracted information and using it to generate recommendations. The system aims to develop a multi-criteria data model for recommendations without overwhelming users by automatically extracting product aspects and sentiments from reviews rather than requiring users to manually evaluate each aspect.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Financial Recommender Systems
1. Recommender Systems di
prodotti bancari-finanziari
Giovanni Semeraro , Cataldo Musto
Smart Companies and Artificial Intelligence
Firenze (Italy) - May 14, 2013
2. outline
• Background
• Needs Allocation
• Anima SGR’s Progettometro
• From Needs to Asset Allocation: recommender
systems
• State of the art: Collaborative filtering, content-based filtering
• Our choice: case-based reasoning
• A possible use case
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
3. Background
ObjectWay Finance-as-a-Service
Smart Application Software & Services for Financial
Services Operators
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
4. Background
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
5. Background
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
6. Background
Wealth Management reference framework
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
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 allocation
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
13. Progettometro
information about life projects
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
16. from needs
to asset
allocation
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
17. research
question
is it possible to evolve a
needs allocation tool
towards an asset
allocation one by
exploiting artificial
inteligence techniques?
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
18. our proposal: personalization
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
19. to introduce an holistic vision of the user
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
20. to adapt asset portfolios
on the ground of personal user profile and needs
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
21. to introduce a tool helpful for supporting
financial 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
23. Recommender Systems
Relevant items (movies, news, books, etc.) are suggested to
the 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. definition
Recommender Systems have the goal of guiding the
users in a personalized way to interesting
or useful objects in a large space of possible
options.
Burke, 2002 (*)
(*) Robin D. Burke: Hybrid Recommender
Systems: 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. 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. Amazon.com
Testo
“ The technology is used by shopping websites such as Amazon, which receives about 35 percent of its revenue via product
recommendations. 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 food
sites to suggest songs, news stories, and restaurants, respectively. Even financial-services firms recently began using
recommender systems to provide alerts for investors about key market events in which they might
be 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. Netflix.com
Recommendations
“ The technology is used by shopping websites such as Amazon, which receives about 35 percent of its revenue via product
recommendations. 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 food
sites to suggest songs, news stories, and restaurants, respectively. Even financial-services firms recently began using
recommender systems to provide alerts for investors about key market events in which they might
be 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
29. Recommender Systems
current literature
Collaborative/Social Filtering
Content-based Filtering
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
30. Recommender Systems
current literature
Collaborative/Social Filtering
Content-based Filtering
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
31. collaborative recommenders
Suggest 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. collaborative recommenders
Suggest items that similar users liked in the past.
It capitalizes the
‘word of mouth’ effect
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
33. collaborative recommenders
example: user-item matrix
item 1 item 2 item 3 item 4
user
1
♥ ♥
user
2
♥ ♥ ♥
user
3
♥
user
4
♥ ♥
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
34. collaborative recommenders
target user: user 4
item 1 item 2 item 3 item 4
user
1
♥ ♥
user
2
♥ ♥ ♥
user
3
♥
user
4
♥ ♥
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
35. collaborative recommenders
looking for like-minded users
item 1 item 2 item 3 item 4
user
1
♥ ♥
user
2
♥ ♥ ♥
user
3
♥
user
4
♥ ♥
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
36. collaborative recommenders
recommendations
item 1 item 2 item 3 item 4
user
1
♥ ♥
user
2 ♥ ♥ ♥
user
3
♥
user
4
♥ ♥
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
37. Recommender Systems
current literature
Collaborative/Social Filtering
Content-based Filtering
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
38. content-based recommenders
Suggest items similar to those liked in the past by the user
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
39. content-based recommenders
key concepts
•Each item has to be described through a set of
textual 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. content-based recommenders
example: news recommendations
Items
♥
♥
User Profile
User is
interested in
news articles
about 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. content-based recommenders
example: news recommendations
Items
♥
♥
Recommendations
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
42. content-based recommenders
example: news recommendations
Items
♥
♥
Recommendations
XG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
43. content-based recommenders
example: news recommendations
Items
♥
♥
Recommendations
XG.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013.
44. both collaborative and content-based filtering
are not feasible for recommending
financial products.
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
45. CF drawback: flocking
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
46. CF drawback: flocking
Similar users receive similar
assets.
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
47. CF drawback: flocking
Too many users could be moved
towards the same suggestions
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
48. CF drawback: flocking
consequence: 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. CBRS drawback: poor content
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
50. CBRS drawback: poor content
Features describing both assets
and private investors are very
poor (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. CBRS drawback: poor content
Difficult to calculate the overlap between
item and user (feature) description
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
53. Knowledge-based
Recommender 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 recommendations
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
54. Knowledge-based
Recommender Systems
• Recommendation process
• Gets information about user needs;
• Exploits the knowledge stored in the KB to meet
user needs;
• (eventually) ask user to relax or to modify some of
the 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. we focus on a subclassof
knowledge-based recommender systems
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
56. we focus on a subclassof
knowledge-based recommender systems
case-based recommender systems
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
57. case-based RSs
• Knowledge base Case base
• Similar problems solved in the past are used as
knowledge base
• To each case is assigned a set of features
• User needs
• Description of the case
• The recommendation process consists of the
retrieval and the adaptation of similar already
solved cases
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
58. case-based RSs
solving cycle
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
60. case-based RSs
formally
item model
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
61. case-based RSs
formally
item 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. case-based RSs
formally
item 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. case-based RSs
formally
item model
user model
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
64. case-based RSs
formally
item model
user 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. case-based RSs
formally
item model
user model
session model
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
66. case-based RSs
formally
item model
user model
session model
evaluation
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
67. case-based RSs
formally
item model
user model
session model
evaluation
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
68. case-based RSs
formally
item model
user model
session model
evaluation
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
$ 174.18
http://tinyurl.com/d3nt2fq
69. given a case base, it is necessary to
define similarity metrics to
compute how similar two cases are
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
71. case-based RSs
similarity
state of the art:
heterogeneous euclidean overlap metric
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
73. case-based RSs
similarity
weight of the i-th feature
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
75. the retrieved solutions can be
refined and modified before being
proposed to the user
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
76. solutions considered as ‘correct’
can be stored in the case base and
exploited again in the future
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
77. case-based reasoning for
financial product recommendation
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
78. scenario
“Scrooge McDuck wants to
get richer. He decided to
invest some of his savings
and he asked for help to a
financial advisor”
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
79. step 1
user modeling
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
80. scenario
Which features
may describe
Scrooge McDuck?
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
81. scenario
User Features
Risk Profile
Financial Experience
Financial Situation
Investment Goals
Temporal Goals
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
82. scenario
User Features
Risk Profile: Low
Financial Experience: High
Financial Situation:Very High
Investment Goals: Medium
Temporal Goals: Medium
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
83. scenario
User Features
Risk Profile: Low
Financial Experience: High
Financial Situation:Very High
Investment Goals: Medium
Temporal Goals: Medium
MiFID-based
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
84. in a classical pipeline, the target user would have received
a “model” porfolio tailored on her profile
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
85. in a pipeline fostered by a recommender system, the
financial advisor can analyze the portfolios proposed to
similar users.
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
86. step 2
retrieval of similar users
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
87. retrieval
case base
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
92. in real-world scenarios, the case base contains
much more helpful cases
usually, it is necessary to introduce some strategy to diversify similar cases
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
94. to each case is assigned an agreed portoflio
the set of the portfolios represents the set of the possible recommendations
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
95. retrieval
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
96. how to combine the retrieved cases?
several strategies available
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
97. step 3
revise and review
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
98. revise and review
Obbligazionario Euro Bot 30%
Obbligazionario HighYield 12.5%
Obbligazionario Globale 18.5%
Azionario Europa 21.5%
Azionario Paesi Emergenti 9.5%
Flessibili BassaVolatilità 8%
rough average
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
99. revise and review
clustering (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. financial advisor and private investor can
further discuss the portfolio
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
101. revise and review
Original Discussed Gap
Obbligazionario
Euro Bot 30% 30%
Obbligazionario
HighYield 12.5% 10% -2.5%
Obbligazionario
Globale 18.5% 20% +1.5%
Azionario Europa 21.5% 24% +2.5%
Azionario Paesi
Emergenti 9.5% 8% -1.5%
Flessibili Bassa
Volatilità 8% 8%
interactive personalization
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
102. an evaluation score is finally assigned to the
proposed solution
yield, e.g.
retain
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
103. good solutions are stored in the case base and
exploited for future recommendations
retain
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
104. case base
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
105. (new) case base
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
106. recap
• Case-based reasoning for recommending financial products
• Goal: to help financial promoters considering solutions proposed to
similar users
• Case base: user features and agreed portfolios
• User features: risk profile (MiFID questionnaire), financial
experience, financial situation, investment goals, temporal goals
• Portfolio: model portfolio, macro asset classes, asset class
distribution, products, etc.
• Similarity: HEOM to retrieve similar ‘cases’
• Revise and Review: several strategies for cases aggregation and
combination
• Retain: considering external factors (e.g. yield) to evaluate the
effectiveness of the proposed solution
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013
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 aggregate
recommended 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. questions?
Cataldo Musto, Ph.D
cataldo.musto@uniba.it
prof. Giovanni Semeraro
giovanni.semeraro@uniba.it
G.Semeraro, C.Musto, - Recommender Systems di Prodotti Bancari-Finanziari.
Smart Companies and Artificial Intelligence, Firenze (Italy) - May 14, 2013