%0 Conference Paper %1 wenige2015scalable %A Wenige, Lisa %A Ruhland, Johannes %B 2015 3rd International Conference on Future Internet of Things and Cloud %D 2015 %K group\_aksw wenige %P 451--456 %T Scalable Property Aggregation for Linked Data Recommender Systems %U https://www.researchgate.net/profile/Lisa_Wenige/publication/314095877_Scalable_Property_Aggregation_for_Linked_Data_Recommender_Systems/links/5b4f3c18a6fdcc8dae2884f2/Scalable-Property-Aggregation-for-Linked-Data-Recommender-Systems.pdf %X Recommender systems are an integral part of todays internet landscape. Recently the enhancement of recommendation services through Linked Open Data (LOD) became a new research area. The ever growing amount of structured data on the web can be used as additional background information for recommender systems. But current approaches in Linked Data recommender systems (LDRS) miss out on an adequate item feature representation in their prediction model and an efficient processing of LOD resources. In this paper, we present a scalable Linked Data recommender system that calculates preferences on multiple property dimensions. The system achieves scalability through parallelization of property-specific rating prediction on a MapReduce framework. Separate prediction results are summarized through a stacking technique. Evaluation results show an increased performance both in terms of accuracy and scalability.

@inproceedings{wenige2015scalable, abstract = {Recommender systems are an integral part of todays internet landscape. Recently the enhancement of recommendation services through Linked Open Data (LOD) became a new research area. The ever growing amount of structured data on the web can be used as additional background information for recommender systems. But current approaches in Linked Data recommender systems (LDRS) miss out on an adequate item feature representation in their prediction model and an efficient processing of LOD resources. In this paper, we present a scalable Linked Data recommender system that calculates preferences on multiple property dimensions. The system achieves scalability through parallelization of property-specific rating prediction on a MapReduce framework. Separate prediction results are summarized through a stacking technique. Evaluation results show an increased performance both in terms of accuracy and scalability.}, added-at = {2020-06-18T14:14:56.000+0200}, author = {Wenige, Lisa and Ruhland, Johannes}, bdsk-url-1 = {https://www.researchgate.net/profile/Lisa_Wenige/publication/314095877_Scalable_Property_Aggregation_for_Linked_Data_Recommender_Systems/links/5b4f3c18a6fdcc8dae2884f2/Scalable-Property-Aggregation-for-Linked-Data-Recommender-Systems.pdf}, biburl = {https://www.bibsonomy.org/bibtex/27f8242f523b55e072b73b5c2d81f15ac/dice-research}, booktitle = {2015 3rd International Conference on Future Internet of Things and Cloud}, interhash = {4de9418f43d01c0e0d00b667bb0b65b5}, intrahash = {7f8242f523b55e072b73b5c2d81f15ac}, keywords = {group\_aksw wenige}, organization = {IEEE}, pages = {451--456}, timestamp = {2023-04-25T16:34:05.000+0200}, title = {Scalable Property Aggregation for Linked Data Recommender Systems}, url = {https://www.researchgate.net/profile/Lisa_Wenige/publication/314095877_Scalable_Property_Aggregation_for_Linked_Data_Recommender_Systems/links/5b4f3c18a6fdcc8dae2884f2/Scalable-Property-Aggregation-for-Linked-Data-Recommender-Systems.pdf}, year = 2015 }