{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-architectures-for-learning-context","title":"Deep Architectures for Learning Context-dependent Ranking Functions","arxiv_id":"1803.05796","date":"2018-03-15","proceeding":null,"authors":["Karlson Pfannschmidt","Pritha Gupta","Eyke Hüllermeier"],"abstract":"Object ranking is an important problem in the realm of preference learning.\nOn the basis of training data in the form of a set of rankings of objects,\nwhich are typically represented as feature vectors, the goal is to learn a\nranking function that predicts a linear order of any new set of objects.\nCurrent approaches commonly focus on ranking by scoring, i.e., on learning an\nunderlying latent utility function that seeks to capture the inherent utility\nof each object. These approaches, however, are not able to take possible\neffects of context-dependence into account, where context-dependence means that\nthe utility or usefulness of an object may also depend on what other objects\nare available as alternatives. In this paper, we formalize the problem of\ncontext-dependent ranking and present two general approaches based on two\nnatural representations of context-dependent ranking functions. Both approaches\nare instantiated by means of appropriate neural network architectures, which\nare evaluated on suitable benchmark task.","url_abs":"http://arxiv.org/abs/1803.05796v2","url_pdf":"http://arxiv.org/pdf/1803.05796v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-architectures-for-learning-context","repo_url":"https://github.com/kiudee/cs-ranking","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"object","task_name":"Object"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}