Finding academic experts on a multisensor approach using Shannon’s entropy

Expert finding is an information retrieval task concerned with the search for the most knowledgeable people, in some topic, with basis on documents describing peoples activities. The task involves taking a user query as input and returning a list of people sorted by their level of expertise regarding the user query. This paper introduces a novel approach for combining multiple estimators of expertise based on a multisensor data fusion framework together with the Dempster–Shafer theory of evidence and Shannon’s entropy. More specifically, we defined three sensors which detect heterogeneous information derived from the textual contents, from the graph structure of the citation patterns for the community of experts, and from profile information about the academic experts. Given the evidences collected, each sensor may define different candidates as experts and consequently do not agree in a final ranking decision. To deal with these conflicts, we applied the Dempster–Shafer theory of evidence combined with Shannon’s Entropy formula to fuse this information and come up with a more accurate and reliable final ranking list. Experiments made over two datasets of academic publications from the Computer Science domain attest for the adequacy of the proposed approach over the traditional state of the art approaches. We also made experiments against representative supervised state of the art algorithms. Results revealed that the proposed method achieved a similar performance when compared to these supervised techniques, confirming the capabilities of the proposed framework.     

A Study on an Information Recommendation System that Provides Topical Information Related to User’s Inquiry for Information Retrieval

As the Internet has been commonly used in our everyday lives, we have been able to obtain large amount of information from it, whereas we have simultaneously had a problem that it is difficult to find proper information for us from the large amount of information on the Web. Although many information recommendation methods have been proposed in order to solve this problem, most recommendation methods are based on a large amount of user’s personal data such as operation log, schedule, etc – which means that we have to manage a large amount of personal data in the system in order to provide proper information to users, and it would be expensive to construct such a system. With this background, in this study, against aiming to construct a sophisticated information recommendation system based on large personal data, we propose a handy and not expensive information recommendation method, working beside a normal search engine, which does not depend on user profile data, but on topical news information.