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Semi-supervised context-aware matrix factorization: using contexts in a way of "latent" factors

Published: 24 March 2014 Publication History

Abstract

Context-aware recommender systems (CARS) additionally take contexts into consideration and try to adapt users' preferences according to their contextual situations. In the traditional recommender systems (RS), latent factor models, such as matrix factorization and latent dirichlet allocation, have demonstrated their efficiencies. Apparently, contexts could be those possible latent factors -- they are "latent" ones if we have no pre-knowledge of them, but currently we have explicit contextual information at hand, why not treat and use them in a similar way as the latent factors? Most research in CARS seeks ways to incorporate contexts in the recommendation process, but none of them continue to use the contexts in a way of "latent" factors. In this work, the research ideas, relevant challenges and expected outcomes about using contexts in a way of "latent" factors are introduced and discussed as one novel research direction in the CARS domain, and a semi-supervised context-aware matrix factorization approach is proposed as a result.

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Cited By

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  • (2021)Hybrid recommender system based on fuzzy neural algorithmConcurrency and Computation: Practice and Experience10.1002/cpe.647333:24Online publication date: 16-Jul-2021
  • (2017)A hybrid recommender system using artificial neural networksExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.04.04683:C(300-313)Online publication date: 15-Oct-2017

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  1. Semi-supervised context-aware matrix factorization: using contexts in a way of "latent" factors

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    cover image ACM Conferences
    SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing
    March 2014
    1890 pages
    ISBN:9781450324694
    DOI:10.1145/2554850
    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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    Published: 24 March 2014

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    Author Tags

    1. context
    2. context-aware recommendation
    3. matrix factorization

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    SAC 2014: Symposium on Applied Computing
    March 24 - 28, 2014
    Gyeongju, Republic of Korea

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    SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;
    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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    View all
    • (2021)Hybrid recommender system based on fuzzy neural algorithmConcurrency and Computation: Practice and Experience10.1002/cpe.647333:24Online publication date: 16-Jul-2021
    • (2017)A hybrid recommender system using artificial neural networksExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.04.04683:C(300-313)Online publication date: 15-Oct-2017

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