Applying multi-view based metadata in personalized ranking for recommender systems

In this paper, we propose a multi-view based metadata extraction technique from unstructured textual content in order to be applied in recommendation algorithms based on latent factors. The solution aims at reducing the problem of intense and time-consuming human effort to identify, collect and label descriptions about the items. Our proposal uses a unsupervised learning method to construct topic hierarchies with named entity recognition as privileged information. We evaluate the technique using different recommendation algorithms, and show that better accuracy is obtained when additional information about items is considered.São Paulo Research Foundation (FAPESP) (Grants 2012/13830-9, 2013/16039-3, 2013/22547-1)CAPE

Personalisation of eSearch Services – Concepts, Techniques, and Market Overview

The importance of information in today’s society is still growing and information search has become an essential task in both the workplace and in private life. eSearch services provide access to the abundance of information available on the Internet by means of search engine technology. However, conventional search engines have certain limitations in dealing with the typical information overload problems. With the application of personalisation techniques search engine providers aim at moderating some of the problems by providing users with information access individualised to their needs. The aim of this paper is twofold. Firstly, techniques for personalisation of eSearch services are introduced. Secondly, the results of an empirical study of the market for eSearch services are presented. Typical examples illustrate eSearch personalisation in practice, and the diffusion of techniques and implications for further research in the domain are discussed