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

Content-based Recommender Systems: State of the Art and Trends

Recommender systems have the effect of guiding users in a personalized way to interesting objects in a large space of possible options. Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Indeed, the basic process performed by a content-based recommender consists in matching up the attributes of a user profile in which preferences and interests are stored, with the attributes of a content object (item), in order to recommend to the user new interesting items. This chapter provides an overview of content-based recommender systems, with the aim of imposing a degree of order on the diversity of the different aspects involved in their design and implementation. The first part of the chapter presents the basic concepts and terminology of contentbased recommender systems, a high level architecture, and their main advantages and drawbacks. The second part of the chapter provides a review of the state of the art of systems adopted in several application domains, by thoroughly describing both classical and advanced techniques for representing items and user profiles. The most widely adopted techniques for learning user profiles are also presented. The last part of the chapter discusses trends and future research which might lead towards the next generation of systems, by describing the role of User Generated Content as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered

Personalization for the Web: Learning User Preferences from Text

As more information becomes available electronically, tools for finding information of interest to users become increasingly important. Information preferences vary greatly across users, therefore, filtering systems must be highly personalized to serve the individual interests of the user. Our research deals with learning approaches to build user profiles that accurately capture user interests from content (documents) and that could be used for personalized information filtering. The learning mechanisms analyzed in this paper are relevance feedback and a naive Bayes method. Experiments conducted in the context of a content-based profiling system for movies show the pros and cons of each method

WordNet-based Word Sense Disambiguation for Learning User Profiles

Nowadays, the amount of available information, especially on the Web and in Digital Libraries, is increasing over time. In this context, the role of user modeling and personalized information access is increasing. This paper focuses on the problem of choosing a representation of documents that can be suitable to induce concept-based user profiles as well as to support a content-based retrieval process. We propose a framework for content-based retrieval, which integrates a word sense disambiguation algorithm based on a semantic similarity measure between concepts (synsets) in the WordNet IS-A hierarchy, with a relevance feedback method to induce semantic user profiles. The document representation adopted in the framework, that we called Bag-Of-Synsets (BOS) extends and slightly improves the classic Bag-Of-Words (BOW) approach, as shown by an extensive experimental session

User Profiling to Support Internet Customers: what do you want to buy today?

In the recent years, the astonishing growth of the Internet and the considerable advances of Web technologies have promoted the development of electronic commerce. While e-commerce has not necessarily allowed businesses to produce more products, it has allowed them to provide consumers with more choices. Instead of tens of thousands of books in a superstore, consumers may choose among millions of books in an online store. Increasing choice has also increased the amount of information that scrupulous customers want process before they are able to select which items meet their needs. One way to address this information overload is the use of personalized systems able to support customers in retrieving information about products they are really interested in. Personalization has become an important strategy in Business-to-Consumer electronic commerce, where a user explicitly wants the e-commerce site to consider his or her own information, such as preferences, in order to improve access to relevant product information. In this paper, we propose a scheme to learn user profiles to support Internet customers. The proposed scheme is designed to handle different levels of users' interests simultaneously. Experimental evaluations show the promise of the approach

Personalization for the Web: Learning User Preferences from Text

As more information becomes available electronically, tools for finding information of interest to users become increasingly important. Information preferences vary greatly across users, therefore, filtering systems must be highly personalized to serve the individual interests of the user. Our research deals with learning approaches to build user profiles that accurately capture user interests from content (documents) and that could be used for personalized information filtering. The learning mechanisms analyzed in this paper are relevance feedback and a na¨ıve Bayes method. Experiments conducted in the context of a content-based profiling system for movies show the pros and cons of each method

WordNet-based User Profiles for Neighborhood Formation in Hybrid Recommender Systems

Recommender systems help to reduce information overload and provide customized information access for targeted domains. Such systems take input from users and, based on their needs and preferences, provide personalized advices that help people to filter useful information. Collaborative filtering and content-based filtering are the most widely recommendation techniques adopted to date. The paper presents a new hybrid recommendation technique based on the combination of classic collaborative filtering and user profiles inferred using content-based methods