A Robust Artificial Intelligence Enabled Methodology to Predict Online Consumers Behaviour Using Hybrid Deep Learning Strategy
Bibliographic record
Abstract
Marketing and consumer behavior predictions are two areas where Artificial Intelligence (AI) is finding widespread use. Evaluating the accuracy of AI -based consumer behavior prediction is the focus of this article. Evidence from research in this area suggests that AI can help sift through mountains of data pertaining to customer performance. It includes looking at things like customer surveys, purchasing history, and behaviour. Furthermore, this sector may make use of deep learning techniques for consumer behavior forecasting. All sorts of advertising and marketing choices can benefit from this data. The Hybrid Learning for Behavioral Prediction (HLBP) method, first proposed in this study, integrates AI -assisted learning with categorization logic for processing. In order to assess the efficacy of the suggested program, this model is cross-validated with the traditional learning model known as Recurrent Neural Network (RNN). Even more encouraging is the possibility that AI can aid businesses in fine-tuning their advertising strategies. As a result, advertising may be more precisely targeted, and marketing budgets can be more efficiently used. Here, AI in a marketing context may provide customized experiences for consumers, such making better product recommendations and enhancing the shopping experience overall. In order to maximize the effectiveness of online advertising efforts, artificial intelligence can be employed. This research delves into the potential of artificial neural networks to identify customer behavior utilizing data collected from conventional surveys. Neural networks outperform classical discriminant analysis in most cases, demonstrating their strong discriminate potential.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".