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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/579259
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dc.contributor.authorLasmy
dc.contributor.authorChowanda A
dc.contributor.authorHerman R. T
dc.contributor.authorNotoatmojo B
dc.date.accessioned2023-11-06T03:18:54Z-
dc.date.available2023-11-06T03:18:54Z-
dc.date.issued2016-07
dc.identifier.issn0128-7702
dc.identifier.otherukmvital:116560
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/579259-
dc.descriptionIn 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.
dc.language.isoen
dc.publisherUniversiti Putra Malaysia Press
dc.relation.haspartPertanika Journals
dc.relation.urihttp://www.pertanika.upm.edu.my/regular_issues.php?jtype=3&journal=JSSH-24-S-7
dc.rightsUPM
dc.subjectCustomer purchase behaviour
dc.subjectHidden Markov Model
dc.subjectFacebook datasets
dc.subjectStrategic management
dc.subjectComputational prediction model
dc.titleTowards automatic customer purchase behaviours prediction through a social media lens using the hidden markov model
dc.typeJournal Article
dc.format.volume24
dc.format.pages169-176
dc.format.issueSpecial Issue
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