An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Information Retrieval
The New Science of Management Decision
The New Science of Management Decision
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Information extraction from free-text business documents
Effective databases for text & document management
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
marService: multiattribute utility recommendation for e-markets
International Journal of Computer Applications in Technology
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The authors' interest is focused on advanced recommending functionalities proposed by more and more Internet websites w.r.t. the selection of movies, e-business sites, or any e-purchases. These functionalities often rely on the Internet users' opinions and evaluations. A « movie-recommender » application is presented. Recommender websites generally propose an aggregation of the user's evaluations critics according to different relevant criteria w.r.t. the application.The authors propose an Information Processing System (IPS) to collect, process and manage as automatically as possible these opinions or critics to support this multi criteria evaluation for recommendation. The RS (Recommender System) firstly proposes information extraction techniques in order to classify the available users' critics w.r.t. the criteria implied in the evaluation process and to automatically associate numerical scores to these critics. Then multicriteria techniques are introduced to numerically evaluate, compare and rank the competing movies the critics are reported to. Finally the RS is presented as an interactive Decision-Making Support System (DMSS) relying on a sensibility analysis of the movies ranking. A particular attention is paid to the automation of the information phase in the decision-making process: movie comments cartography according to users' evaluation criteria and attribution of a partial score to each critic considered as the expression of a value judgment in natural language.