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Recommender Systems Beyond E-Commerce: Presence and Future

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Consumer Happiness: Multiple Perspectives

Part of the book series: Studies in Rhythm Engineering ((SRE))

Abstract

Recommender systems are supporting users in the identification of items that fulfill their wishes and needs and are also helping to foster consumer happiness. These systems have been successfully applied in different application domains—examples thereof are the recommendation of movies, books, digital cameras, points of interest, financial services, and software requirements. The major objectives of this chapter are to provide an overview of recommendation approaches including criteria when to use which algorithm, to show different applications of recommendation algorithms going beyond standard e-commerce scenarios and to discuss issues for future research.

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Notes

  1. 1.

    twitter.com.

  2. 2.

    autodesk.com.

  3. 3.

    eventhelpr.com.

  4. 4.

    www.eclipse.org.

  5. 5.

    openreq.eu.

  6. 6.

    eclipse.org.

  7. 7.

    knowledgecheckr.com.

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  1. Institute of Software Technology, Applied Artificial Intelligence, TU Graz, Inffeldgasse 16b/2, A-8010, Graz, Austria

    Alexander Felfernig, Thi Ngoc Trang Tran & Viet-Man Le

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  1. Alexander Felfernig
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  2. Thi Ngoc Trang Tran
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Correspondence to Alexander Felfernig .

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  1. Indian Institute of Management Ranchi, Ranchi, Jharkhand, India

    Tanusree Dutta

  2. Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India

    Manas Kumar Mandal

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Felfernig, A., Tran, T.N.T., Le, VM. (2021). Recommender Systems Beyond E-Commerce: Presence and Future. In: Dutta, T., Mandal, M.K. (eds) Consumer Happiness: Multiple Perspectives. Studies in Rhythm Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-33-6374-8_14

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