A GNN-Transformer hybrid that models anonymised user behaviour sequences and user-content-platform relationships, then outputs interest scores, Top-K content/category preferences and confidence values — under realistic constraints: distributed data, inconsistent event definitions, sparse categories and short interaction histories.
The system processes anonymised behaviour events and relationship structure, then produces business-readable outputs a downstream team can act on.
| Component | Project detail | Purpose |
|---|---|---|
| Data Inputs | Anonymised browsing, click, save, add-to-cart, enquiry and order behaviour; content category; timestamp; campaign source; device type; conversion flag. | Represent the multi-platform behaviour environment without relying on personally identifiable data. |
| Baseline Models | BPR-MF, LightGBM, DeepFM, GRU4Rec, SASRec, LightGCN and GraphSAGE-GAT. | Create fair reference points before evaluating the hybrid architecture. |
| Hybrid Models | GNN embedding + Transformer encoder; dynamic graph Transformer; gated mixture-of-experts fusion. | Combine graph relationship signals, sequence dynamics and tabular business features. |
| Evaluation | AUC, LogLoss, NDCG@10, Recall@20, Precision@K, MRR, HitRate@K, cohort metrics, cold-start performance, training time and GPU memory. | Avoid relying on a single number and evaluate stability across user groups. |
| Outputs | Interest score, Top-K categories, confidence value, offline batch scoring script, API prototype, logs and technical report. | Translate research findings into a usable technical delivery package. |
A client-facing algorithm research project focused on predicting evolving user interests from anonymised multi-platform behaviour data. It investigates seven baseline models and three GNN-Transformer hybrid approaches, using time-based validation, ablation studies and platform/cohort analysis.
The work includes data processing scripts, synthetic data generation, public-data sanity checks, baseline code, hybrid model experiments, result tables, API prototype design, technical reporting and consultation on future deployment. It reflects the type of work PWB Research is built for: technically uncertain, data-dependent, compute-intensive AI research that needs careful experimentation before production decisions are made.
We can turn it into a research plan and a working technical path.
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