Battisti, Giulio (A.A. 2024/2025) AI and recommendation algorithms. Tesi di Laurea in Advanced marketing management, Luiss Guido Carli, relatore Marco Francesco Mazzù, pp. 135. [Master's Degree Thesis]

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Abstract/Index

Recommender systems: definitions and operation. The background: marketing, digital evolution and disruptive technologies. Recommendation systems: definitions, characteristics and types. The consumer perspective: bias and impacts of recommendation systems on consumption decisions. The evolution of recommendation algorithms. The role of depth in algorithmic recommendations. Personalization and progressive refinement mechanisms. The problem of overspecialization and its effects. Strategies for balancing depth and diversification. The importance of breadth in recommendations. The problem of variety in consumer choices. Breadth models in recommendation agents. Strategies for introducing novelty in recommendations. Factors influencing the effectiveness of breadth (personality, cognitive biases, expertise). Empirical analysis. Methodological approach: the research design of the literature analysis. Bibliometric analysis. Data analysis. The sample. Comparative evaluation of comparison scenarios. Anova analysis. Analysis of preferences. Analysis of acceptability and expectations in the use of recommendation algorithms.

References

Bibliografia: pp. 130-134.

Thesis Type:	Master's Degree Thesis
Institution:	Luiss Guido Carli
Degree Program:	Master's Degree Programs > Master's Degree Program in Management, English language (LM-77)
Chair:	Advanced marketing management
Thesis Supervisor:	Mazzù, Marco Francesco
Thesis Co-Supervisor:	Pozharliev, Rumen Ivaylov
Academic Year:	2024/2025
Session:	Autumn
Deposited by:	Alessandro Perfetti
Date Deposited:	13 Feb 2026 15:37
Last Modified:	13 Feb 2026 15:37
URI:	https://tesi.luiss.it/id/eprint/44830

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