Abstract not found

Since it was first proposed by Rich in 1979, stereotype-based user modelling has been applied numerous times in recommender systems. The primary motivation for stereotyping in user modelling is the new user problem — a purely individualised user model cannot be constructed for a user until he has provided some ratings of items. By appealing to a pool of manually-constructed stereotypes, each one rep-resenting the interests of a set of users with common socio-demographic attributes, and eliciting enough information from the user to match him to a set of stereotypes, the matching stereotypes can be combined and used to recommend items to the user. Many claims have been made in the literature regarding the efficacy of stereotyping but these have not been substantiated empirically. This thesis describes the first comprehensive empirical investigation into the rec-ommendation performance of stereotype-based user models and details an approach to training stereotype-based user models automatically. The recommendations pro-vided by stereotype-based user models are directly compared to those provided by

Abstract. Recommender systems are tools to help users find items that they deem of interest to them. They can be seen as an application of data mining process. In this paper, a new recommender system based on multi-features is introduced. Demographic and psychographic features are used to asses similarities between users. The model is built on a collaborative filtering method and addresses three problems: sparsity, scalability and cold-star. The sparsity problem is tackled by integrating users-documents relevant information within meta-clusters. The scalability and the cold-start problems are considered by using a suitable probability model calculated on meta-cluster information. Moreover, a weight similarity measure is introduced in order to take into account dynamic human being preferences behaviour. A prediction score for generating recommendations is proposed based on the target user previous behaviour and his/her neighbourhood preferences on the target document.