A Case Study on TikTok Affiliate Marketing Optimization Using Content-Based Filtering: Data Mining Application on @arpa_ads Creator Account
Bibliographic record
Abstract
This study addresses the critical challenge of content-product mismatch in TikTok affiliate marketing, where generic promotional strategies result in low conversion rates due to audience attention saturation.Through a case study of the @arpa_ads creator account, we apply a data-driven approach using Content-Based Filtering (CBF) within the Knowledge Discovery in Databases (KDD) framework to optimize product-video alignment.The methodology processes textual data from 2,035 video captions and hashtags across 527 affiliate products, employing Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction and Cosine Similarity for relevance calculation.Model evaluation demonstrates stable performance with Precision, Recall, and F1-Score values of 0.58, indicating moderate effectiveness in identifying relevant content matches.The analytical findings are operationalized through an interactive Tableau dashboard, providing actionable insights for strategic decision-making.This research validates a practical, replicable framework for enhancing affiliate marketing effectiveness on social commerce platforms, while acknowledging limitations inherent to single-account case study designs.The system successfully bridges the gap between data mining techniques and real-world marketing applications in the dynamic TikTok ecosystem.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".