TikTok's algorithm and the viral video phenomenon: a predictive model for detecting video virality
De Lucia, Riccardo (A.A. 2021/2022) TikTok's algorithm and the viral video phenomenon: a predictive model for detecting video virality. Tesi di Laurea in Customer intelligence & big data, Luiss Guido Carli, relatore Giuseppe Francesco Italiano, pp. 101. [Master's Degree Thesis]
PDF (Full text)
Restricted to Registered users only Download (3MB) | Request a copy |
Abstract/Index
The social media marketing and customer engagement. Social media and social media marketing. Influencer marketing. Social media engagement and viral advertising. Recommendation systems, theory and methodologies. Introduction to recommendation systems Content-based filtering for recommendation systems. Collaborative filtering. The concrete examples of Amazon, Netflix and Spotify. The cold start problem and possible solutions. Neural networks applied to social media recommendation systems. Introduction to deep learning. Artificial neural network. Convolutional neural network Neural networks in hybrid recommendation systems. Content analysis in social media platform. TikTok platform and “for you” algorithm (what is, how it works and marketing implications). TikTok founding, rising and success. TikTok marketing and Ads. The “for you” algorithm. Marketing implications. Models. Models overview. Linear regression and logistic regression. SVM model.
References
Bibliografia: pp. 86-87. Sitografia: p. 88.
Thesis Type: | Master's Degree Thesis |
---|---|
Institution: | Luiss Guido Carli |
Degree Program: | Master's Degree Programs > Master's Degree Program in Marketing (LM-77) |
Chair: | Customer intelligence & big data |
Thesis Supervisor: | Italiano, Giuseppe Francesco |
Thesis Co-Supervisor: | Querini, Marco |
Academic Year: | 2021/2022 |
Session: | Autumn |
Deposited by: | Alessandro Perfetti |
Date Deposited: | 23 Feb 2023 11:18 |
Last Modified: | 23 Feb 2023 11:18 |
URI: | https://tesi.luiss.it/id/eprint/35170 |
Downloads
Downloads per month over past year
Repository Staff Only
View Item |