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Displaying results 1 to 5 of 5.

  1. The economics of recommender systems
    evidence from a field experiment on MovieLens
    Author: Aridor, Guy; Gonçalves, Duarte; Kluver, Daniel; Kong, Ruoyan; Konstan, Joseph A.
    Published: December 2022
    Publisher:  CESifo, Munich, Germany

    We conduct a field experiment on a movie-recommendation platform to identify if and how recommendations affect consumption. We use within-consumer randomization at the good level and elicit beliefs about unconsumed goods to disentangle exposure from... more

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    We conduct a field experiment on a movie-recommendation platform to identify if and how recommendations affect consumption. We use within-consumer randomization at the good level and elicit beliefs about unconsumed goods to disentangle exposure from informational effects. We find recommendations increase consumption beyond its role in exposing goods to consumers. We provide support for an informational mechanism: recommendations affect consumers' beliefs, which in turn explain consumption. Recommendations reduce uncertainty about goods consumers are most uncertain about and induce information acquisition. Our results highlight the importance of recommender systems' informational role when considering policies targeting these systems in online marketplaces.

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/271773
    Series: CESifo working papers ; 10129 (2022)
    Subjects: recommender systems; information acquisition; field experiment
    Scope: 1 Online-Ressource (circa 57 Seiten), Illustrationen
  2. Similarity and consistency in algorithm-guided exploration
    Author: Bao, Yongping; Danwitz, Ludwig; Dvorak, Fabian; Fehrler, Sebastian; Hornuf, Lars; Lin, Hsuan Yu; Helversen, Bettina von
    Published: December 2022
    Publisher:  CESifo, Munich, Germany

    Algorithm-based decision support systems play an increasingly important role in decisions involving exploration tasks, such as product searches, portfolio choices, and human resource procurement. These tasks often involve a trade-off between... more

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    Algorithm-based decision support systems play an increasingly important role in decisions involving exploration tasks, such as product searches, portfolio choices, and human resource procurement. These tasks often involve a trade-off between exploration and exploitation, which can be highly dependent on individual preferences. In an online experiment, we study whether the willingness of participants to follow the advice of a reinforcement learning algorithm depends on the fit between their own exploration preferences and the algorithm's advice. We vary the weight that the algorithm places on exploration rather than exploitation, and model the participants' decision-making processes using a learning model comparable to the algorithm's. This allows us to measure the degree to which one's willingness to accept the algorithm's advice depends on the weight it places on exploration and on the similarity between the exploration tendencies of the algorithm and the participant. We find that the algorithm's advice affects and improves participants' choices in all treatments. However, the degree to which participants are willing to follow the advice depends heavily on the algorithm's exploration tendency. Participants are more likely to follow an algorithm that is more exploitative than they are, possibly interpreting the algorithm's relative consistency over time as a signal of expertise. Similarity between human choices and the algorithm's recommendations does not increase humans' willingness to follow the recommendations. Hence, our results suggest that the consistency of an algorithm's recommendations over time is key to inducing people to follow algorithmic advice in exploration tasks.

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/271832
    Series: CESifo working papers ; 10188 (2022)
    Subjects: algorithms; decision support systems; recommender systems; advice-taking; multi-armed bandit; search; exploration-exploitation; cognitive modeling
    Scope: 1 Online-Ressource (circa 50 Seiten), Illustrationen
  3. Contextualized consumers
    theories and evidence on consumer ethics, product recommendations, and self-control = Gecontextualiseerde consumenten : theorieën en bewijs van consumentenethiek, productaanbevelingen en zelfbeheersing
    Author: Gai, Phyliss Jia
    Published: [2020]
    Publisher:  Erasmus Institute of Management (ERIM), Rotterdam

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    Source: Union catalogues
    Language: English
    Media type: Dissertation
    Format: Online
    ISBN: 9789058925787
    Other identifier:
    hdl: 1765/127680
    Series: ERIM PhD series in research in management ; EPS-2020-498-MKT
    Subjects: Contextual effects; globalization; digitalization; eating behaviour; bilingualism; framing; recommender systems; unethical behaviour; consumer behaviour; nudge
    Scope: 1 Online-Ressource, Illustrationen
    Notes:

    Aus Copyright-Gründen enthält das Dokument anfangs nur Teile der Hochschulschrift. Das Datum der Bereitstellungs des vollständigen Textes ist nicht bekannt

    Dissertation, Erasmus University Rotterdam, 2020
  4. The potential of recommender systems for directing job search
    a large-scale experiment
    Author: Behaghel, Luc; Dromundo, Sofia; Gurgand, Marc; Hazard, Yagan; Zuber, Thomas
    Published: February 2024
    Publisher:  IZA - Institute of Labor Economics, Bonn, Germany

    We analyze the employment effects of directing job seekers' applications toward establishments likely to recruit. We run a two-sided randomization design involving about 800,000 job seekers and 40,000 establishments, based on an empirical model that... more

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    We analyze the employment effects of directing job seekers' applications toward establishments likely to recruit. We run a two-sided randomization design involving about 800,000 job seekers and 40,000 establishments, based on an empirical model that recommends each job seeker to firms so as to maximize total potential employment. Our intervention induces a 1% increase in job finding rates for short term contracts. This impact comes from a targeting effect combining (i) a modest increase in job seekers' applications to the very firms that were recommended to them, and (ii) a high success rate conditional on applying to these firms. Indeed, the success rate of job seekers' applications varies considerably across firms: the efficiency of applications sent to recommended firms is 2.7 times higher than the efficiency of applications to the average firm. This suggests that there can be substantial gains from better targeting job search, leveraging firm-level heterogeneity.

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Discussion paper series / IZA ; no. 16781
    Subjects: recommender systems; matching; RCT; active labor market policies
    Scope: 1 Online-Ressource (circa 66 Seiten), Illustrationen
  5. Recommending for a multi-sided marketplace
    a multi-objective hierarchical approach
    Author: Wang, Yuyan; Tao, Long; Zhang, Xian Xing
    Published: [2023]
    Publisher:  [Stanford Graduate School of Business], [Stanford, CA]

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    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    Series: [Stanford University Graduate School of Business research paper ; no. 4602954]
    Subjects: recommender systems; multi-sided marketplace; food-delivery platforms
    Scope: 1 Online-Ressource (circa 70 Seiten)