Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/579259
Title: Towards automatic customer purchase behaviours prediction through a social media lens using the hidden markov model
Authors: Lasmy
Chowanda A
Herman R. T
Notoatmojo B
Keywords: Customer purchase behaviour
Hidden Markov Model
Facebook datasets
Strategic management
Computational prediction model
Issue Date: Jul-2016
Description: In this research article, we present our work on building omputational prediction models to dynamically predict users’ purchase behaviours by implementing Hidden Markov Models (HMM). The models can be used by decision makers in a company to develop a strategy (e.g. marketing, products development) based on the prediction results. We evaluate the model using our datasets of Facebook. We collected the data by utilising Facebook API. Furthermore, we implemented a Hidden Markov Model (HMM) algorithm to the datasets to provide a dynamic prediction of customers’ purchase behaviours over time. In the preliminary evaluation, we implemented our model to the datasets with t=2. In our datasets, we found that the category, electronics, was the most favourite topic to discuss, share and like regarding electronics. Interestingly, we found that a positive direction for its trend appeared in the second run of the model.
News Source: Pertanika Journals
ISSN: 0128-7702
Volume: 24
Pages: 169-176
Publisher: Universiti Putra Malaysia Press
Appears in Collections:Journal Content Pages/ Kandungan Halaman Jurnal

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