%0 Conference Paper %1 paper:linas:2008 %A Baltrunas, Linas %B RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems %C New York, NY, USA %D 2008 %I ACM %K RecSys doctoral-symposium recommender %P 295--298 %R http://doi.acm.org/10.1145/1454008.1454056 %T Exploiting contextual information in recommender systems %U http://portal.acm.org/citation.cfm?id=1454056&jmp=cit&coll=ACM&dl=ACM&CFID=16806799&CFTOKEN=18249028#CIT %X Recommender Systems help an on-line user to tame information overload and are being used now in complex domains where it could be beneficial to exploit context-awareness, e.g., in travel recommendation. Technically, in Recommender Systems we can interpret context as a set of constraints or preferences over the usage of items determined by the contextual conditions (e.g., today it is raining or the user is in a particular location). In fact, there is a lack of approaches to deal effectively with contextual data. This thesis investigates some approaches to exploit context in Recommender Systems. It provides a general architecture of context-aware Recommender Systems and analyzes separate components of this model. The main focus is to investigate new approaches that can bring a real added value to users. In this paper I also describe my initial results on item selection and item weighting for context-dependent Collaborative Filtering (CF). Moreover, I shall present my ongoing research on CF hybridization using context. %@ 978-1-60558-093-7

@inproceedings{paper:linas:2008, abstract = {Recommender Systems help an on-line user to tame information overload and are being used now in complex domains where it could be beneficial to exploit context-awareness, e.g., in travel recommendation. Technically, in Recommender Systems we can interpret context as a set of constraints or preferences over the usage of items determined by the contextual conditions (e.g., today it is raining or the user is in a particular location). In fact, there is a lack of approaches to deal effectively with contextual data. This thesis investigates some approaches to exploit context in Recommender Systems. It provides a general architecture of context-aware Recommender Systems and analyzes separate components of this model. The main focus is to investigate new approaches that can bring a real added value to users. In this paper I also describe my initial results on item selection and item weighting for context-dependent Collaborative Filtering (CF). Moreover, I shall present my ongoing research on CF hybridization using context.}, added-at = {2009-07-21T09:49:10.000+0200}, address = {New York, NY, USA}, author = {Baltrunas, Linas}, biburl = {https://www.bibsonomy.org/bibtex/28c24f6f1d294245443164e36d0e61b3c/mschuber}, booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems}, description = {Exploiting contextual information in recommender systems}, doi = {http://doi.acm.org/10.1145/1454008.1454056}, interhash = {8405fcf11a40dc431d8ff743c861d597}, intrahash = {8c24f6f1d294245443164e36d0e61b3c}, isbn = {978-1-60558-093-7}, keywords = {RecSys doctoral-symposium recommender}, location = {Lausanne, Switzerland}, pages = {295--298}, publisher = {ACM}, timestamp = {2009-07-21T09:49:10.000+0200}, title = {Exploiting contextual information in recommender systems}, url = {http://portal.acm.org/citation.cfm?id=1454056&jmp=cit&coll=ACM&dl=ACM&CFID=16806799&CFTOKEN=18249028#CIT}, year = 2008 }