This paper examines how recommendation algorithms shape consumer decision-making on digital platforms by reorganising the information environment in which consumers discover and evaluate alternatives. Drawing on prior research on recommender systems, algorithmic visibility, consumer choice, and platform engagement, it develops a conceptual account of five connected mechanisms: personalisation, visibility, repeated exposure, consideration set formation, and behavioural feedback loops. The analysis indicates that recommendation systems influence decisions primarily before purchase by prioritising selected content, increasing the familiarity and cognitive accessibility of repeatedly encountered options, and narrowing the alternatives that enter consumers’ consideration sets. A Fiji travel recommendation example illustrates how an initially unfamiliar destination may become a realistic option through repeated algorithmic exposure without determining the final choice. The paper further argues that consumer–platform interaction is recursive: user activities generate behavioural data that refine subsequent recommendations, while platform objectives such as engagement and retention also shape recommendation optimisation. Recommendation algorithms therefore neither function as neutral information tools nor exercise complete control over consumers. Rather, they structure decision environments while consumers retain agency to interpret and evaluate the options presented.

Qiurong Huang. The Influence of Recommendation Algorithms on Consumer Decision-Making on Digital Platforms through Personalisation, Visibility, and Feedback Loops. The Frontiers of Society, Science and Technology (2026), Vol. 8, Issue 3: 60-66. https://doi.org/10.25236/FSST.2026.080309.

[1] Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749.

[2] Bucher T. Want to be on the top? Algorithmic power and the threat of invisibility on Facebook[J]. New Media & Society, 2012, 14(7): 1164-1180.

[3] Gillespie T. The relevance of algorithms[J]. Media technologies: Essays on communication, materiality, and society, 2014, 167(2014): 167.  

[4] Li S, Grahl J, Hinz O. How do recommender systems lead to consumer purchases? A causal mediation analysis of a field experiment[J]. Information Systems Research, 2022, 33(4): 1411-1434.

[5] Mousavi R, Adamopoulos P, Bockstedt J. The decoy effect and recommendation systems[J]. Information Systems Research, 2023.

[6] Cotter K. Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram[J]. New Media & Society, 2019, 21(4): 895-913.

[7] Choi Y, Kim J. Consumer preferences in user- vs. item-based recommender systems for search and experience products[J]. Journal of Marketing Management, 2025.

[8] Maslowska E, Malthouse E C, Hollebeek L D. The role of recommender systems in fostering consumers’ long-term platform engagement[J]. Journal of Service Management, 2022.

[9] Chen N, Tsai H T. Steering via algorithmic recommendations[J]. The RAND Journal of Economics, 2024, 55(4): 501-518.