A framework for collaborative filtering recommender systems

As the use of recommender systems becomes more consolidated on the Net, an increasing need arises to develop some kind of evaluation framework for collaborative filtering measures and methods which is capable of not only testing the prediction and recommendation results, but also of other purposes which until now were considered secondary, such as novelty in the recommendations and the users? trust in these. This paper provides: (a) measures to evaluate the novelty of the users? recommendations and trust in their neighborhoods, (b) equations that formalize and unify the collaborative filtering process and its evaluation, (c) a framework based on the above-mentioned elements that enables the evaluation of the quality results of any collaborative filtering applied to the desired recommender systems, using four graphs: quality of the predictions, the recommendations, the novelty and the trust

A SUPPLIER DISCOVERY FRAMEWORK FOR EFFECTIVE AND EFFICIENT CONFIGURATION OF A SUPPLY CHAIN

A supplier registry can play a central role in configuring a global supply chain for service-oriented enterprise integration by providing an open platform for publishing and discovering suppliers distributed over Internet. The availability of correct classification schemes used to organize suppliers based on their capability descriptions is the key to building an effective registry. This paper discusses the clustering-based construction of classification schemes from existing capability descriptions of suppliers and then the registration and retrieval of suppliers based on these classification schemes. This approach is based on the use of similarity measures to discriminate any two suppliers’ capability descriptions. The results look promising in that small medium suppliers can expose themselves to the large companies in an efficient manner, while large companies can diversify their supply sources.&nbsp