Case-based recommender systems for personalized finance advisory

Wealth Management is a business model operated by banks and brokers, that offers a broad range of investment services to individual clients to help them reach their investment objectives. Wealth management services include investment advisory, subscription of mandates, sales of financial products, collection of investment orders by clients. Due to the complexity of the tasks, which largely require a deep knowledge of the financial domain, a trend in the area is the exploitation of recommendation technologies to support financial advisors and to improve the effectiveness of the process. The talk presents a framework to support financial advisors in the task of providing clients with personalized investment strategies. The methodology is based on the exploitation of case-based reasoning and the introduction of a diversification technique. A prototype of the framework has been used to generate personalized portfolios, and its performance, evaluated against 1,172 real users, shows that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in most experimental settings

Personalized Finance Advisory through Case-based Recommender Systems and Diversification Strategies

Recommendation of financial investment strategies is a complex and knowledge-intensive task. Typically, financial advisors have to discuss at length with their wealthy clients and have to sift through several investment proposals before finding one able to completely meet investors' needs and constraints. As a consequence, a recent trend in wealth management is to improve the advisory process by exploiting recommendation technologies. This paper proposes a framework for recommendation of asset allocation strategies which combines case-based reasoning with a novel diversification strategy to support financial advisors in the task of proposing diverse and personalized investment portfolios. The performance of the framework has been evaluated by means of an experimental session conducted against 1172 real users, and results show that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in most experimental settings while meeting the preferred risk profile. Furthermore, our diversification strategy shows promising results in terms of both diversity and average yield

A hybrid recommender system for industrial symbiotic networks

Various solutions enabling the realization of synergies in Industrial Symbiotic Networks have been proposed. However, incorporating intelligence into the platforms that these networks use, supporting the involved actors to automatically find possible candidates able to cover their needs, is still of high importance. Usually, the actors participating in these networks act based on previously predefined patterns, without taking into account all the possible alternatives, usually due to the difficulty of finding and properly evaluating them. Therefore, the recommendation of new matches that the users were not aware of is a big challenge, as companies many times are not willing to change their established workflows if they are not sure about the outcome. Thus, the ability of a platform to properly identify symbiotic alternatives that could provide both economic and environmental benefits for the companies, and the sector as a whole, is of high importance and delivering such recommendations is crucial. In this work, we propose a hybrid recommender system aiming to support users in properly filtering and identifying the symbiotic relationships that may provide them an improved performance. Several criteria are taken into account in order to generate, each time, the list of the most suitable solutions for the current user, at a given moment. In addition, the implemented system uses a graph-based similarity model in order to identify resource similarities while performing a hybrid case-based recommendation in order to find the optimal solutions according to more features than just the resources’ similarities.Peer ReviewedPostprint (published version

Toward a collective intelligence recommender system for education

The development of Information and Communication Technology (ICT), have revolutionized the world and have moved us into the information age, however the access and handling of this large amount of information is causing valuable time losses. Teachers in Higher Education especially use the Internet as a tool to consult materials and content for the development of the subjects. The internet has very broad services, and sometimes it is difficult for users to find the contents in an easy and fast way. This problem is increasing at the time, causing that students spend a lot of time in search information rather than in synthesis, analysis and construction of new knowledge. In this context, several questions have emerged: Is it possible to design learning activities that allow us to value the information search and to encourage collective participation?. What are the conditions that an ICT tool that supports a process of information search has to have to optimize the student's time and learning? This article presents the use and application of a Recommender System (RS) designed on paradigms of Collective Intelligence (CI). The RS designed encourages the collective learning and the authentic participation of the students. The research combines the literature study with the analysis of the ICT tools that have emerged in the field of the CI and RS. Also, Design-Based Research (DBR) was used to compile and summarize collective intelligence approaches and filtering techniques reported in the literature in Higher Education as well as to incrementally improving the tool. Several are the benefits that have been evidenced as a result of the exploratory study carried out. Among them the following stand out: • It improves student motivation, as it helps you discover new content of interest in an easy way. • It saves time in the search and classification of teaching material of interest. • It fosters specialized reading, inspires competence as a means of learning. • It gives the teacher the ability to generate reports of trends and behaviors of their students, real-time assessment of the quality of learning material. The authors consider that the use of ICT tools that combine the paradigms of the CI and RS presented in this work, are a tool that improves the construction of student knowledge and motivates their collective development in cyberspace, in addition, the model of Filltering Contents used supports the design of models and strategies of collective intelligence in Higher Education.Postprint (author's final draft

A smart financial advisory system exploiting Case-Based Reasoning

In the financial advisory context, knowledge-based recommendations based on Case-Based Reasoning are an emerging trend. They usually exploit knowledge about past experiences and about the characterization of both customers and financial products. In the present paper, we report the experience related to the development of a case-based recommendation module in a project called SmartFasi. We present a solution aimed at personalizing the asset picking phase, by taking into consideration choices made by customers who have a financial and personal data profile "similar" to the current one. We discuss the notion of distance-based similarity adopted in our system and how to actually implement an asset recommendation strategy integrated with the other software modules of SmartFasi. We finally discuss the impact such a strategy may have both from the point of view of private investors and professional users

Determination of Attribute Weights for Recommender Systems Based on Product Popularity

In content- and knowledge-based recommender systems often a measure of (dis)similarity between products is used. Frequently, this measure is based on the attributes of the products. However, which attributes are important for the users of the system remains an important question to answer. In this paper, we present two approaches to determine attribute weights in a dissimilarity measure based on product popularity. We count how many times products are sold and based on this, we create two models to determine attribute weights: a Poisson regression model and a novel boosting model minimizing Poisson deviance. We evaluate these two models in two ways, namely using a clickstream analysis on four different product catalogs and a user experiment. The clickstream analysis shows that for each product catalog the standard equal weights model is outperformed by at least one of the weighting models. The user experiment shows that users seem to have a different notion of product similarity in an experimental context

Nudged to Win: Designing Robo-Advisory to Overcome Decision Inertia

Decision inertia is a serious problem in financial decision-making and thus a challenge for decision support systems. We discuss recent findings and review antecedents and consequences of decision inertia from a psychological perspective. We use these insights to develop IT-based methods designed to overcome decision inertia using psychologically optimized financial decision support systems. Furthermore, we propose an experimental study to evaluate the design features of such a system. Our work is a first step in designing adaptive decision support systems that detect situations in which the user is prone to decision inertia and react by adapting interface elements appropriately that might otherwise exacerbate decision inertia – for a specific user in a specific decision situation

Choosing Attribute Weights for Item Dissimilarity using Clikstream Data with an Application to a Product Catalog Map

In content- and knowledge-based recommender systems often a measure of (dis)similarity between items is used. Frequently, this measure is based on the attributes of the items. However, which attributes are important for the users of the system remains an important question to answer. In this paper, we present an approach to determine attribute weights in a dissimilarity measure using clickstream data of an e-commerce website. Counted is how many times products are sold and based on this a Poisson regression model is estimated. Estimates of this model are then used to determine the attribute weights in the dissimilarity measure. We show an application of this approach on a product catalog of MP3 players provided by Compare Group, owner of the Dutch price comparison site http://www.vergelijk.nl, and show how the dissimilarity measure can be used to improve 2D product catalog visualizations.dissimilarity measure;attribute weights;clickstream data;comparison

Intelligent information processing in a digital library using semantic web

With the explosive growth of information, it is becoming increasingly difficult to retrieve the relevant documents with current search engine only. The information is treated as an ordinary database that manages the contents and positions. To the individual user, there is a great deal of useless information in addition to the substantial amount of useful information. This begets new challenges to docent community and motivates researchers to look for intelligent information retrieval approach and ontologies that search and/or filter information automatically based on some higher level of understanding are required. We study improving the efficiency of search methods and classify the search patrons into several models based on the profiles of agent based on ontology. We have proposed a method to efficiently search for the target information on a Digital Library network with multiple independent information sources. This paper outlines the development of an expert prototype system based in an ontology for retrieval information of the Digital Library University of Seville. The results of this study demonstrate that by improving representation by incorporating more metadata from within the information and the ontology into the retrieval process, the effectiveness of the information retrieval is enhanced. We used Jcolibri and Prótége for developing the ontology and creation the expert system respectively