Modeling Adoptions and the Stages of the Diffusion of Innovations
Yasir Mehmood,Nicola Barbieri,Francesco Bonchi +2 more
- pp 420-429
TLDR
A stochastic model which decomposes a diffusion trace (sequence of adoptions) in an ordered sequence of stages is proposed, where each stage is intuitively built around two dimensions: users and relative speed at which adoptions happen.Abstract:
We study the data mining problem of modeling adoptions and the stages of the diffusion of an innovation. For our aim we propose a stochastic model which decomposes a diffusion trace (sequence of adoptions) in an ordered sequence of stages, where each stage is intuitively built around two dimensions: users and relative speed at which adoptions happen. Each stage is characterized by a specific rate of adoption and it involves different users to different extent, while the sequentiality in the diffusion is guaranteed by constraining the transition probabilities among stages. An empirical evaluation on synthetic and real-world adoption logs shows the effectiveness of the proposed framework in summarizing the adoption process, enabling several analysis tasks such as the identification of adopter categories, clustering and characterization of diffusion traces, and prediction of which users will adopt an item in the next future.read more
Citations
More filters
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Proceedings ArticleDOI
Analysis of Technology Innovation Diffusion Model and Diffusion Characteristics on Educational Technology: Case Study of MOOC
TL;DR: In this paper , the authors selected MOOC as the research object and explored the educational technology diffusion, diffusion characteristics, and diffusion process of MOOC based on the innovation diffusion theory.
References
More filters
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
Diffusion of Innovations
TL;DR: A history of diffusion research can be found in this paper, where the authors present a glossary of developments in the field of Diffusion research and discuss the consequences of these developments.
Journal ArticleDOI
Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.