E-commerce recommenders' authority
Sílvio César Cazella, Eliseo Berní Reategui, Luís Otávio Álvares · 2006
Considerable time is wasted in searching for information on the Internet because there is such a variety of items competing for attention. In an attempt to minimize this difficulty, and to provide support in the search for interesting and useful information, significant research efforts have been made --- from systems based on pure information recovery, to systems applying information filtering to recommend items. In this paper we describe a model (Mo-DROP) for the computation of the user's relevance of opinion (Recommneder's Rank metric) and its application in a specific domain of knowledge using information from Recommender Systems. The aim of this solution is to offer the users of a Recommender System conditions to identify other people's authority in a Collaborative Filtering mechanism. In addition, we present an extended example and an experiment applying this model and the Recommender's Rank metric.