The Contextual Turn: From Context-Aware to Context-Driven Recommender Systems

Roberto Pagano; Paolo Cremonesi; Martha Larson; Balázs Hidasi; Domonkos Tikk; Alexandros Karatzoglou; Massimo Quadrana

doi:10.1145/2959100.2959136

The Contextual Turn: From Context-Aware to Context-Driven Recommender Systems

Roberto Pagano, Paolo Cremonesi, Martha Larson, Balázs Hidasi, Domonkos Tikk, Alexandros Karatzoglou, Massimo Quadrana

Multimedia Computing

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

27 Citations (Scopus)

Abstract

A critical change has occurred in the status of context in recommender systems. In the past, context has been considered 'additional evidence'. This past picture is at odds with many present application domains, where user and item information is scarce. Such domains face continuous cold start conditions and must exploit session rather than user information. In this paper, we describe the `Contextual Turn?: the move towards context-driven recommendation algorithms for which context is critical, rather than additional. We cover application domains, algorithms that promise to address the challenges of context-driven recommendation, and the steps that the community has taken to tackle context-driven problems. Our goal is to point out the commonalities of context-driven problems, and urge the community to address the overarching challenges that context-driven recommendation poses.

Original language	English
Title of host publication	RecSys'16 Proceedings of the 10th ACM Conference on Recommender Systems
Editors	S. Sen, W. Geyer
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Pages	249-252
Number of pages	4
ISBN (Electronic)	978-1-4503-4035-9
DOIs	https://doi.org/10.1145/2959100.2959136
Publication status	Published - 2016
Event	10th ACM Conference on Recommender Systems, RecSys 2016 - MIT, Boston, MA, United States Duration: 15 Sept 2016 → 19 Sept 2016 https://recsys.acm.org/recsys16/

Conference

Conference	10th ACM Conference on Recommender Systems, RecSys 2016
Country/Territory	United States
City	Boston, MA
Period	15/09/16 → 19/09/16
Internet address	https://recsys.acm.org/recsys16/

Keywords

position paper
context
recommender systems

Access to Document

10.1145/2959100.2959136

Cite this

Pagano, R., Cremonesi, P., Larson, M., Hidasi, B., Tikk, D., Karatzoglou, A., & Quadrana, M. (2016). The Contextual Turn: From Context-Aware to Context-Driven Recommender Systems. In S. Sen, & W. Geyer (Eds.), RecSys'16 Proceedings of the 10th ACM Conference on Recommender Systems (pp. 249-252). Association for Computing Machinery (ACM). https://doi.org/10.1145/2959100.2959136

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title = "The Contextual Turn: From Context-Aware to Context-Driven Recommender Systems",

abstract = "A critical change has occurred in the status of context in recommender systems. In the past, context has been considered 'additional evidence'. This past picture is at odds with many present application domains, where user and item information is scarce. Such domains face continuous cold start conditions and must exploit session rather than user information. In this paper, we describe the `Contextual Turn?: the move towards context-driven recommendation algorithms for which context is critical, rather than additional. We cover application domains, algorithms that promise to address the challenges of context-driven recommendation, and the steps that the community has taken to tackle context-driven problems. Our goal is to point out the commonalities of context-driven problems, and urge the community to address the overarching challenges that context-driven recommendation poses.",

keywords = "position paper, context, recommender systems",

author = "Roberto Pagano and Paolo Cremonesi and Martha Larson and Bal{\'a}zs Hidasi and Domonkos Tikk and Alexandros Karatzoglou and Massimo Quadrana",

year = "2016",

doi = "10.1145/2959100.2959136",

language = "English",

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editor = "S. Sen and W. Geyer",

booktitle = "RecSys'16 Proceedings of the 10th ACM Conference on Recommender Systems",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

note = "10th ACM Conference on Recommender Systems, RecSys 2016 ; Conference date: 15-09-2016 Through 19-09-2016",

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}

Pagano, R, Cremonesi, P, Larson, M, Hidasi, B, Tikk, D, Karatzoglou, A & Quadrana, M 2016, The Contextual Turn: From Context-Aware to Context-Driven Recommender Systems. in S Sen & W Geyer (eds), RecSys'16 Proceedings of the 10th ACM Conference on Recommender Systems. Association for Computing Machinery (ACM), New York, pp. 249-252, 10th ACM Conference on Recommender Systems, RecSys 2016, Boston, MA, United States, 15/09/16. https://doi.org/10.1145/2959100.2959136

The Contextual Turn: From Context-Aware to Context-Driven Recommender Systems. / Pagano, Roberto; Cremonesi, Paolo; Larson, Martha et al.
RecSys'16 Proceedings of the 10th ACM Conference on Recommender Systems. ed. / S. Sen; W. Geyer. New York: Association for Computing Machinery (ACM), 2016. p. 249-252.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Karatzoglou, Alexandros

AU - Quadrana, Massimo

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AB - A critical change has occurred in the status of context in recommender systems. In the past, context has been considered 'additional evidence'. This past picture is at odds with many present application domains, where user and item information is scarce. Such domains face continuous cold start conditions and must exploit session rather than user information. In this paper, we describe the `Contextual Turn?: the move towards context-driven recommendation algorithms for which context is critical, rather than additional. We cover application domains, algorithms that promise to address the challenges of context-driven recommendation, and the steps that the community has taken to tackle context-driven problems. Our goal is to point out the commonalities of context-driven problems, and urge the community to address the overarching challenges that context-driven recommendation poses.

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The Contextual Turn: From Context-Aware to Context-Driven Recommender Systems

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Keywords

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