A Deep Evaluation of Two Cognitive User Models for Personalized Search

Personalized retrieval of documents is a research field that has been gaining interest, since it is a possible solution to the information overload problem. The ability to adapt the retrieval process to the current user needs increases the accuracy and reduces the time users spend to formulate and sift through result lists. In this chapter we show two instances of user modeling. One is based on the human memory theory named Search of Associative Memory, and a further approach based on the Hyperspace Analogue to Language model. We prove how by implicit feedback techniques we are able to unobtrusively recognize user needs and monitor the user working context. This is important to provide personalization during traditional information retrieval and for recommender system development. We discuss an evaluation comparing the two cognitive approaches, their similarities and drawbacks. An extended analysis reveals interesting evidence about the good performance of SAM-based user modeling, but it also proves how HAL-based models evaluated in the Web browsing context shows slightly higher degree of precision