FinRec: The 3rd International Workshop on Personalization & Recommender Systems in Financial Services (original) (raw)

Recommender Systems meet Finance: A literature review

The present work overviews the application of recom-mender systems in various financial domains. The relevant literature is investigated based on two directions. First, a domain-based cate-gorization is discussed focusing on those recommendation problems, where the existing literature is significant. Second, the application of various recommendation algorithms and data mining techniques is summarized. The purpose of this paper is providing a basis for further scientific research and product development in this field.

Recommendation and personalization: a survey

Journal of Intelligent Information Systems, 2002

Recommendation and personalization attempt to reduce information overload and retain customers. While research in both recommender systems and personalization grew mainly out of information retrieval, both areas have emerged from nascent levels to veritable and ...

Personalization Beyond Recommender Systems

IFIP International Federation for Information Processing, 2006

Personalization is an interdisciplinary topic that has been discussed in the literature of marketing and information systems as well as in other research areas. In this paper we present findings from a longitudinal research project on personalization of e-commerce systems. The findings were taken from interviews and software development projects with company partners (action research). The main contribution described in this paper is the Personalization Map. The map provides an extensive overview on personalization functions that can be used to individualize and improve human-computer-interaction both in B2C and B2B e-commerce environments. In a first step, the functions are classified according to their order of appearance in the buying process. In a second step they are grouped into subcategories. There is no single strategy for selecting successful personalization functions as the suitability varies depending on the industry and the goods sold. Most definitions of personalization are closely connected to the recommendation of items based on user preferences. The Personalization Map shows that recommender systems are an interesting but rather small part of the universe of personalization functions.

Providing Personalized Services to Users in a Recommender System

International Journal of Web-Based Learning and Teaching Technologies, 2015

Instructors recommend learning materials to a class of students not minding the learning ability and reading habit of each student. Learners are finding it problematic to make a decision about which available learning materials best meet their situation and will be beneficial to their course of study. In order to address this challenge, a new e-learning material recommender system that is able to recommend quality items to learners individually is required. The aim of this work is to develop a Personalized Recommender System that switches between Content-based and Collaborative filtering techniques, with an objective to design an algorithm to recommend electronic library materials, as well as personalize recommendations to both new and existing users. Experiments were conducted with evaluations showing that the recommender system was most effective when content-based filtering and collaborative filtering were used to recommend items for new users and existing users respectively, and...

Knowledge-Based Recommender Technologies Supporting the Interactive Selling of Financial Services

Due to a restricted knowledge about product assortments and sales processes, sales representatives in the financial services domain are often overwhelmed and prefer a product-oriented advisory approach leading to low quality results for the customer (Eckert-Niemeyer, 2000; Felfernig & Kiener, 2005a; Keltner & Finegold, 1996). In this context, financial service providers ask for tools supporting sales representatives in the dialog with the customer. Such tools should provide adaptive interfaces (

Recommendation strategies in personalization applications

Information & Management, 2019

While the initial goal of recommender systems (RSes) was to reduce the information overload for Internet users and make the information retrieval more efficient, they have become a crucial strategic tool for companies in the online markets. According to this evolution, research on RSes has produced a wide variety of approaches and algorithms. As a consequence, the companies deploying RSes in their business applications face the decision of how to generate and deliver personalized recommendations to their users by choosing among many options. The problem has been largely treated from the machine learning performance perspective because there is relatively little research done from the business perspective. The decision of what kind of recommender engines should be used in a personalization application, given certain business conditions, has a strategic value because it affects the way customers perceive the company with respect to its competitors. Choosing the wrong way to personalize recommendations may not only require the redesign of the information systems but also to rebuild the relationships with customers and even the entire brand strategic positioning. The research issues addressed by this paper are (i) which recommendation strategies a company can deploy to generate and deliver recommendations to users, and (ii) which specific strategies should be used depending on the current business conditions. We propose taxonomy based on a literature analysis and a framework to associate each strategy with a certain setting. The proposed framework is empirically supported by four case studies.

Recommender Systems: An Overview

AI Magazine

Recommender systems are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The field, christened in 1995, has grown enormously in the variety of problems addressed and techniques employed, as well as in its practical applications. Recommender systems research has incorporated a wide variety of artificial intelligence techniques including machine learning, data mining, user modeling, case-based reasoning, and constraint satisfaction, among others. Personalized recommendations are an important part of many on-line e-commerce applications such as Amazon.com, Netflix, and Pandora. This wealth of practical application experience has provided inspiration to researchers to extend the reach of recommender systems into new and challenging areas. The purpose of the articles in this special issue is to take stock of the current landscape of recommender systems research and id...