Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Design patterns: elements of reusable object-oriented software
Design patterns: elements of reusable object-oriented software
Role model based framework design and integration
Proceedings of the 13th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Object oriented framework development
Crossroads
IEEE Transactions on Knowledge and Data Engineering
Mahout in Action
Speculative analysis of integrated development environment recommendations
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
Radialize: a tool for social listening experience on the web based on radio station programs
Proceedings of the 22nd international conference on World Wide Web companion
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Recommender systems constitute a new field that provides nowadays an important support for information search from huge amounts of data, since some of those information may be of interest of users but hard to be searched manually. According to forecasts, data production tends to grow more and more, which places recommender systems on the way to play a key role in mining those data. Technologies about to be adopted in large scale require tools to enable their mass production, and this fact is the key motivation of this work. In this paper we present our research in the field of component-based software engineering to design a framework for recommender systems. We have elicited the requirements that all recommender systems might address, and developed a vertical framework to support the development of such a sort of systems by designing components in accordance with those requirements. With the Idealize Recommendation Framework, we intend to make the development of recommender systems an easier, faster, and standardized process, as well as to perform the foundation of the technical terms to be used in the area.