{"type":"preprint","publication":"arXiv:1803.05796","title":"Deep Architectures for Learning Context-dependent Ranking Functions","date_created":"2020-09-17T10:53:39Z","date_updated":"2022-01-06T06:54:06Z","status":"public","author":[{"last_name":"Pfannschmidt","full_name":"Pfannschmidt, Karlson","first_name":"Karlson"},{"first_name":"Pritha","full_name":"Gupta, Pritha","last_name":"Gupta"},{"full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"language":[{"iso":"eng"}],"_id":"19524","year":"2018","abstract":[{"lang":"eng","text":"Object ranking is an important problem in the realm of preference learning.\r\nOn the basis of training data in the form of a set of rankings of objects,\r\nwhich are typically represented as feature vectors, the goal is to learn a\r\nranking function that predicts a linear order of any new set of objects.\r\nCurrent approaches commonly focus on ranking by scoring, i.e., on learning an\r\nunderlying latent utility function that seeks to capture the inherent utility\r\nof each object. These approaches, however, are not able to take possible\r\neffects of context-dependence into account, where context-dependence means that\r\nthe utility or usefulness of an object may also depend on what other objects\r\nare available as alternatives. In this paper, we formalize the problem of\r\ncontext-dependent ranking and present two general approaches based on two\r\nnatural representations of context-dependent ranking functions. Both approaches\r\nare instantiated by means of appropriate neural network architectures, which\r\nare evaluated on suitable benchmark task."}],"project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"department":[{"_id":"7"},{"_id":"355"}],"citation":{"apa":"Pfannschmidt, K., Gupta, P., &#38; Hüllermeier, E. (2018). Deep Architectures for Learning Context-dependent Ranking Functions. <i>ArXiv:1803.05796</i>.","ieee":"K. Pfannschmidt, P. Gupta, and E. Hüllermeier, “Deep Architectures for Learning Context-dependent Ranking Functions,” <i>arXiv:1803.05796</i>. 2018.","mla":"Pfannschmidt, Karlson, et al. “Deep Architectures for Learning Context-Dependent Ranking Functions.” <i>ArXiv:1803.05796</i>, 2018.","ama":"Pfannschmidt K, Gupta P, Hüllermeier E. Deep Architectures for Learning Context-dependent Ranking Functions. <i>arXiv:180305796</i>. 2018.","bibtex":"@article{Pfannschmidt_Gupta_Hüllermeier_2018, title={Deep Architectures for Learning Context-dependent Ranking Functions}, journal={arXiv:1803.05796}, author={Pfannschmidt, Karlson and Gupta, Pritha and Hüllermeier, Eyke}, year={2018} }","chicago":"Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Deep Architectures for Learning Context-Dependent Ranking Functions.” <i>ArXiv:1803.05796</i>, 2018.","short":"K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1803.05796 (2018)."},"user_id":"13472"}