Communications of the ACM
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
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
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Multidimensional Recommender Systems: A Data Warehousing Approach
WELCOM '01 Proceedings of the Second International Workshop on Electronic Commerce
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
On the complexity of inducing categorical and quantitative association rules
Theoretical Computer Science
Selective Markov models for predicting Web page accesses
ACM Transactions on Internet Technology (TOIT)
Resource space model, its design method and applications
Journal of Systems and Software
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Multidimensional Filtering Approach Based on Contextual Information
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 02
A novel collaborative filtering-based framework for personalized services in m-commerce
Proceedings of the 16th international conference on World Wide Web
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
IEEE Intelligent Systems
Using Context to Improve Predictive Modeling of Customers in Personalization Applications
IEEE Transactions on Knowledge and Data Engineering
A collaborative constraint-based meta-level recommender
Proceedings of the 2008 ACM conference on Recommender systems
Improving top-n recommendation techniques using rating variance
Proceedings of the 2008 ACM conference on Recommender systems
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Using contextual information and multidimensional approach for recommendation
Expert Systems with Applications: An International Journal
Research on the RSM-Based Multidimensional Recommendation
SKG '08 Proceedings of the 2008 Fourth International Conference on Semantics, Knowledge and Grid
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Context-based splitting of item ratings in collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems
Proceedings of the third ACM conference on Recommender systems
MoviExplain: a recommender system with explanations
Proceedings of the third ACM conference on Recommender systems
Contextual recommendation based on text mining
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Exploiting Additional Dimensions as Virtual Items on Top-N Recommender Systems
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Binary recommender systems: introduction, an application and outlook
Proceedings of the International C* Conference on Computer Science and Software Engineering
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Traditionally, recommender systems for the web deal with applications that have two dimensions, users and items. Based on access data that relate 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 multidimensional approach, called DaVI (Dimensions as Virtual Items), that consists in inserting contextual and background information as new user-item pairs. The main advantage of this approach is that it can be applied in combination with several existing two-dimensional recommendation algorithms. To evaluate its effectiveness, we used the DaVI approach with two different top-N recommender algorithms, Item-based Collaborative Filtering and Association Rules based, and ran an extensive set of experiments in three different real world data sets. In addition, we have also compared our approach to the previously introduced combined reduction and weight post-filtering approaches. The empirical results strongly indicate that our approach enables the application of existing two-dimensional recommendation algorithms in multidimensional data, exploiting the useful information of these data to improve the predictive ability of top-N recommender systems.