Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Item-Based and User-Based Incremental Collaborative Filtering for Web Recommendations
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
A hybrid approach for the personalisation of cloud-based e-governance services
International Journal of High Performance Computing and Networking
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Traditionally, recommender systems for the Web deal with applications that have two types of entities/dimensions, users and items. With these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a direct method that enriches the information in the access logs with new dimensions. We empirically test this method with two recommender systems, an item-based collaborative filtering technique and association rules, on three data sets. Our results show that while collaborative filtering is not able to take advantage of the new dimensions added, association rules are capable of profiting from our direct method.