Abstract:
Information dissemination mining, ads recommendation, and other applications require early hot event prediction. Existing methods require long-term event observation or expensive feature extraction.
Hot and non-hot events have different temporal features, but early data is limited. BEEP, a Bayesian perspective Early stage Event Prediction model, addresses this issue.
We use two Semi-Naive Bayes Classifiers to predict hot events using temporal and structural features and distribution tests. Our methods are proven by theoretical analysis and extensive empirical evaluations on two real datasets.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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