Ephemeral State-Dependent Recommendation for Digital Content

Practice- and Policy-oriented Abstract Building upon recent advances in consumption theories, we propose an ephemeral state-dependent framework for digital content recommendations. The framework accentuates a critical, yet understudied, interplay between a firm’s recommendation strategy (assimilation or diversification) and a consumer’s ephemeral state (fixation or foraging). The framework adaptively recommends either assimilated or diversified content based on a consumer’s ephemeral state. Through a randomized field experiment, we provide compelling evidence that state-dependent schemes can enhance engagement and revenue. Although the congruent scheme (i.e., assimilation when fixation, diversification when foraging) generally outperforms the incongruent one, contributing a 7.3% ($19.73 million) annual revenue lift for the platform, our findings underscore the necessity for nuanced personalization. Specifically, consumers with broader, more fluid preferences benefit more from the incongruent scheme (i.e., assimilation when foraging, diversification when fixation), challenging the prevailing assumption that congruence is always optimal. Our research not only adds to the theoretical understanding of consumer behavior in digital content consumption, but also offers actionable insights for designing more effective recommender systems. By accounting for consumer heterogeneity and considering the broader implications of our proposed recommendation framework, including spillover effects, our findings have the potential to influence industry practices and future academic inquiry in this rapidly evolving field.

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Publication Details

Journal
Information Systems Research
Published
2025-02-24
DOI
https://doi.org/10.1287/isre.2022.664
Citations
3
Primary Topic
Recommender Systems and Techniques
Type
article
Field-Weighted Citation Impact
9.74
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article

Ephemeral State-Dependent Recommendation for Digital Content

Lanfei Shi, Natasha Zhang Foutz, Yongjun Li, Liu Jin
3 citations
Information Systems Research
Recommender Systems and Techniques
9.74
article

Ephemeral State-Dependent Recommendation for Digital Content

Lanfei Shi, Natasha Zhang Foutz, Yongjun Li, Liu Jin
article en
3 citations

Abstract

Practice- and Policy-oriented Abstract Building upon recent advances in consumption theories, we propose an ephemeral state-dependent framework for digital content recommendations. The framework accentuates a critical, yet understudied, interplay between a firm’s recommendation strategy (assimilation or diversification) and a consumer’s ephemeral state (fixation or foraging). The framework adaptively recommends either assimilated or diversified content based on a consumer’s ephemeral state. Through a randomized field experiment, we provide compelling evidence that state-dependent schemes can enhance engagement and revenue. Although the congruent scheme (i.e., assimilation when fixation, diversification when foraging) generally outperforms the incongruent one, contributing a 7.3% ($19.73 million) annual revenue lift for the platform, our findings underscore the necessity for nuanced personalization. Specifically, consumers with broader, more fluid preferences benefit more from the incongruent scheme (i.e., assimilation when foraging, diversification when fixation), challenging the prevailing assumption that congruence is always optimal. Our research not only adds to the theoretical understanding of consumer behavior in digital content consumption, but also offers actionable insights for designing more effective recommender systems. By accounting for consumer heterogeneity and considering the broader implications of our proposed recommendation framework, including spillover effects, our findings have the potential to influence industry practices and future academic inquiry in this rapidly evolving field.

Information Systems ResearchVol. 36(4)
University of Science and Technology of China (CN), Beijing Jiaotong University (CN), University of Virginia (US)
Openalex Percentile: Top 3%
Recommender Systems and Techniques
9.74
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