%0 Book Section %1 citeulike:11489041 %A Parra, Denis %A Sahebi, Shaghayegh %B Advanced Techniques in Web Intelligence-2 %C Berlin, Heidelberg %D 2013 %E Velásquez, Juan D. %E Palade, Vasile %E Jain, Lakhmi C. %I Springer Berlin Heidelberg %K recommender review %P 149--175 %R 10.1007/978-3-642-33326-2_7 %T Recommender Systems: Sources of Knowledge and Evaluation Metrics %U http://dx.doi.org/10.1007/978-3-642-33326-2_7 %V 452 %X Recommender or Recommendation Systems (RS) aim to help users dealing with information overload: finding relevant items in a vast space of resources. Research on RS has been active since the development of the first recommender system in the early 1990s, Tapestry, and some articles and books that survey algorithms and application domains have been published recently. However, these surveys have not extensively covered the different types of information used in RS (sources of knowledge), and only a few of them have reviewed the different ways to assess the quality and performance of RS. In order to bridge this gap, in this chapter we present a classification of recommender systems, and then we focus on presenting the main sources of knowledge and evaluation metrics that have been described in the research literature. %& 7 %@ 978-3-642-33325-5

@incollection{citeulike:11489041, abstract = {{Recommender or Recommendation Systems (RS) aim to help users dealing with information overload: finding relevant items in a vast space of resources. Research on RS has been active since the development of the first recommender system in the early 1990s, Tapestry, and some articles and books that survey algorithms and application domains have been published recently. However, these surveys have not extensively covered the different types of information used in RS (sources of knowledge), and only a few of them have reviewed the different ways to assess the quality and performance of RS. In order to bridge this gap, in this chapter we present a classification of recommender systems, and then we focus on presenting the main sources of knowledge and evaluation metrics that have been described in the research literature.}}, added-at = {2018-03-19T12:24:51.000+0100}, address = {Berlin, Heidelberg}, author = {Parra, Denis and Sahebi, Shaghayegh}, biburl = {https://www.bibsonomy.org/bibtex/2f5ea22c44153b6f57f20d5eddd4db326/aho}, booktitle = {Advanced Techniques in Web Intelligence-2}, chapter = 7, citeulike-article-id = {11489041}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-33326-2_7}, citeulike-linkout-1 = {http://www.springerlink.com/content/m6w320266j111402}, citeulike-linkout-2 = {http://link.springer.com/chapter/10.1007/978-3-642-33326-2_7}, doi = {10.1007/978-3-642-33326-2_7}, editor = {Vel\'{a}squez, Juan D. and Palade, Vasile and Jain, Lakhmi C.}, interhash = {0f92927fe8c126c05ee4a274a6db9df7}, intrahash = {f5ea22c44153b6f57f20d5eddd4db326}, isbn = {978-3-642-33325-5}, keywords = {recommender review}, pages = {149--175}, posted-at = {2016-02-07 17:52:05}, priority = {2}, publisher = {Springer Berlin Heidelberg}, series = {Studies in Computational Intelligence}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{Recommender Systems: Sources of Knowledge and Evaluation Metrics}}, url = {http://dx.doi.org/10.1007/978-3-642-33326-2_7}, volume = 452, year = 2013 }