Fab: content-based, collaborative recommendation
Communications of the ACM
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
User Modeling and User-Adapted Interaction
Cohesive Design of Personalized Web Applications
IEEE Internet Computing
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Model-driven development of context-aware Web applications
ACM Transactions on Internet Technology (TOIT)
Collaborative filtering recommender systems
The adaptive web
Content-based recommendation systems
The adaptive web
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Recommendation techniques have been increasingly incorporated in e-commerce applications, supporting clients in identifying those items that best fit their needs. Unfortunately, little effort has been made to integrate these techniques into methodological proposals of Web development, discouraging the adoption of engineering approaches to face the complexity of recommender systems. This paper introduces a proposal to develop Web-based recommender systems from a model-driven perspective, specifying the elements of recommendation algorithms from a high abstraction level. Adopting the item-to-item approach, this proposal adopts the conceptual models of an existing Web development process to represent the preferences of users for different items, the similarity between obtained from different algorithms, and the selection and ordering of the recommended items according to a predicted rating value. Along with systematizing the development of these systems, this approach permits to evaluate different algorithms with minor changes at conceptual level, simplifying their mapping to final implementations.