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A recipe based on-line food store

Authors:

Martin Svensson,

Jarmo Laaksolahti,

Kristina Höök,

Annika WaernAuthors Info & Claims

IUI '00: Proceedings of the 5th international conference on Intelligent user interfaces

Pages 260 - 263

https://doi.org/10.1145/325737.325866

Published: 09 January 2000 Publication History

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Abstract

Recent research in the area of information retrieval hypothesizes that people benefit from social clues, so called social navigation, when they try to navigate information spaces [7]. We have designed an on-line grocery store building upon those ideas manifested in several different ways. The most central feature is that the system uses a combination of content-based and collaborative filtering as the basis for recipe recommendations. This filtering process can in turn be controlled by editors, whose role is to control the content of the “recipe clubs”. Other types of social clues are also present, such as displaying how many users that have chosen a recipe. Finally, the system shows information about other users currently present in the system, and allows users to get in direct contact through chat.

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Cited By

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Rani JMittal UGupta G(2020)Product or Item‐Based Recommender SystemRecommender System with Machine Learning and Artificial Intelligence10.1002/9781119711582.ch14(269-290)Online publication date: 15-Jul-2020
https://doi.org/10.1002/9781119711582.ch14
Nag NRao AKulhalli AMehta KBhattacharya NRamkumar PBharadwaj ASitaram DJain RMougiakakou SMaria Farinella GYanai K(2019)Flavour Enhanced Food RecommendationProceedings of the 5th International Workshop on Multimedia Assisted Dietary Management10.1145/3347448.3357169(60-66)Online publication date: 15-Oct-2019
https://dl.acm.org/doi/10.1145/3347448.3357169
Hors-Fraile Sde Vries HMalwade SLuna-Perejon FAmaya CCivit ASchneider FBamidis PSyed-Abdul SLi Y(2019)Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change ModelIEEE Access10.1109/ACCESS.2019.29576967(176525-176540)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2957696
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A recipe based on-line food store

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Information

Published In

IUI '00: Proceedings of the 5th international conference on Intelligent user interfaces

January 2000

288 pages

ISBN:1581131348

DOI:10.1145/325737

Chairmen:
Doug Riecken
IBM T.J. Watson Research Center Yorktown Heights, NY; and Rutgers Univ., New Brunswick, NJ
,
David Benyon
Napier Univ., UK
,
Henry Lieberman
Massachusetts Institute of Technology, Cambridge

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 January 2000

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IUI00

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IUI00: International Conference on Intelligent User Interfaces

January 9 - 12, 2000

Louisiana, New Orleans, USA

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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Cited By

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Rani JMittal UGupta G(2020)Product or Item‐Based Recommender SystemRecommender System with Machine Learning and Artificial Intelligence10.1002/9781119711582.ch14(269-290)Online publication date: 15-Jul-2020
https://doi.org/10.1002/9781119711582.ch14
Nag NRao AKulhalli AMehta KBhattacharya NRamkumar PBharadwaj ASitaram DJain RMougiakakou SMaria Farinella GYanai K(2019)Flavour Enhanced Food RecommendationProceedings of the 5th International Workshop on Multimedia Assisted Dietary Management10.1145/3347448.3357169(60-66)Online publication date: 15-Oct-2019
https://dl.acm.org/doi/10.1145/3347448.3357169
Hors-Fraile Sde Vries HMalwade SLuna-Perejon FAmaya CCivit ASchneider FBamidis PSyed-Abdul SLi Y(2019)Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change ModelIEEE Access10.1109/ACCESS.2019.29576967(176525-176540)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2957696
Hsu M(2018)Proposing an ESL recommender teaching and learning systemExpert Systems with Applications: An International Journal10.1016/j.eswa.2007.02.04134:3(2102-2110)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1016/j.eswa.2007.02.041
Trang Tran TAtas MFelfernig AStettinger M(2018)An overview of recommender systems in the healthy food domainJournal of Intelligent Information Systems10.1007/s10844-017-0469-050:3(501-526)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10844-017-0469-0
Vairale VShukla S(2018)Recommendation Framework for Diet and Exercise Based on Clinical Data: A Systematic ReviewData Science and Big Data Analytics10.1007/978-981-10-7641-1_29(333-346)Online publication date: 2-Aug-2018
https://doi.org/10.1007/978-981-10-7641-1_29
Farzan RBrusilovsky P(2018)Social NavigationSocial Information Access10.1007/978-3-319-90092-6_5(142-180)Online publication date: 3-May-2018
https://doi.org/10.1007/978-3-319-90092-6_5
Gaur NSingh A(2016)Recommender system — Making lifestyle healthy using information retrieval2016 2nd International Conference on Next Generation Computing Technologies (NGCT)10.1109/NGCT.2016.7877463(479-484)Online publication date: Oct-2016
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Ge MElahi MFernaández-Tobías IRicci FMassimo DKostkova PGrasso F(2015)Using Tags and Latent Factors in a Food Recommender SystemProceedings of the 5th International Conference on Digital Health 201510.1145/2750511.2750528(105-112)Online publication date: 18-May-2015
https://dl.acm.org/doi/10.1145/2750511.2750528
Cheng TYusof UKhalid M(2014)Content-based filtering algorithm for mobile recipe application2014 8th. Malaysian Software Engineering Conference (MySEC)10.1109/MySec.2014.6986011(183-188)Online publication date: Sep-2014
https://doi.org/10.1109/MySec.2014.6986011
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