Extending recommender systems: A multidimensional approach Export

@misc{citeulike:1205686, abstract = {In this paper, we present new extensions to traditional approaches to recommender systems by making recommender systems support data warehousing capabilities. In particular, we propose recommender systems to work in multidimensional settings as opposed to the traditional twodimensional user/item environments. We also propose recommender systems to support rich profiling and OLAP capabilities. 1}, author = {Adomavicius, G. and Tuzhilin, A.}, citeulike-article-id = {1205686}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.164}, keywords = {personalization, recommender}, posted-at = {2007-10-28 15:27:16}, priority = {3}, title = {Extending recommender systems: A multidimensional approach}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.164} }

TY - GEN ID - citeulike:1205686 L3 - citeulike-article-id:1205686 N2 - In this paper, we present new extensions to traditional approaches to recommender systems by making recommender systems support data warehousing capabilities. In particular, we propose recommender systems to work in multidimensional settings as opposed to the traditional twodimensional user/item environments. We also propose recommender systems to support rich profiling and OLAP capabilities. 1 TI - Extending recommender systems: A multidimensional approach KW - personalization KW - recommender AU - Adomavicius, G AU - Tuzhilin, A UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.164 ER -