On probabilistic notions of precision as a function of recall
Information Processing and Management: an International Journal
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Generic support for personalized mobile multimedia tourist applications
Proceedings of the 12th annual ACM international conference on Multimedia
IEEE Transactions on Knowledge and Data Engineering
Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System
IEEE Intelligent Systems
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
The effects of transparency on trust in and acceptance of a content-based art recommender
User Modeling and User-Adapted Interaction
Collaborative filtering recommender systems
The adaptive web
Content-based recommendation systems
The adaptive web
The adaptive web
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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In recent years there has been a growing interest in mobile recommender systems for the tourism domain, because they can support users visiting new places, suggesting restaurants, hotels, attractions, or entire itineraries. The effectiveness of their suggestions mainly depends on the item retrieval process. How well is the system able to retrieve items that meet users' needs and preferences? In this paper we propose a multi-criteria collaborative approach, that offers a complete method for calculating users' similarities and rating predictions on items to be recommended. It is a purely multi-criteria approach, that uses Pearson's correlation coefficient to compute similarities among users. Experimental results evaluating the retrieval effectiveness of the proposed approach in a prototype mobile cultural heritage recommender system (that suggests visits to cultural locations in Apulia region) show a better retrieval precision than a standard collaborative approach based on the same metrics.