Personalized Search based on a Memory Retrieval Theory

Personalization is the ability to retrieve information content related to users' profile and facilitate their information-seeking activities. Several environments, such as the Web, take advantage of personalization techniques because of the large amount of available information. For this reason, there is a growing interest in providing automated personalization processes during the human-computer interaction. In this paper we introduce a new approach for user modeling, which grounds in the Search of Associative Memory (SAM) theory. By means of implicit feedback techniques, the approach is able to unobtrusively recognize user needs and monitor the user working context in order to provide important information useful to personalize traditional search tools and implement recommender systems. Experimental results based on precision and recall measures indicate improvements in comparison with traditional user models.