Graph-based Knowledge Modeling and Analytics for Capturing and Predicting Customer Behaviour
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Understanding customer behaviour is a challenging problem. While the customer produces a large amount of data with each touch point, most of the proposed models focus on one data source in their predictive analysis approaches. This research proposes a customer profile model based on 360 customer view. To this end, we first model a simplified data model and the basic entities based on the existing models. Then, we perform extensive feature engineering techniques, including extracting new features and transforming features to enhance their behaviour in the predictive model. Through the experimentations, we show that the models based on graphs achieve good performance. To this end, we propose a graph-based neural network capable of multitasking without sacrificing the task's performance. We examine three tasks to predict customer intentions. The final results reveal that the set of features with customer information from different data sources positively influences the predictive algorithms' performance. Table 4.4. performance comparison
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it