We develop recommendation, prediction and intelligent modelling systems for environments where users interact across multiple platforms, content categories and time windows. Our approach combines graph learning, sequential modelling, tabular learning, hybrid architectures and rigorous experiment design.
Modern users move across websites, social platforms, marketplaces, apps and marketing channels. Their behaviour is not a static profile — it changes with time, context, content exposure, device and recent activity. We build models that represent both relational structure and temporal behaviour, treating users, content, categories, platforms and actions as a dynamic system rather than isolated events.
When data contains relationships among users, content, categories and platforms, graph modelling can reveal signals flat tables miss. We study graph-based recommendation methods such as LightGCN, GraphSAGE and GAT, plus hybrid graph-and-sequence architectures. The real challenge is not only whether graph models improve a metric — it is whether graph construction, edge filtering, neighbour sampling and memory use stay manageable as data grows.
User behaviour is time-dependent. A recent add-to-cart may matter more than a click from months ago; a sequence of category views can reveal intent before conversion. We evaluate sequential models such as GRU4Rec and SASRec to understand short-term preference shifts — and the limitations of sequence-only modelling.
The most useful system is often not one model, but a carefully designed hybrid. We combine graph embeddings with Transformer encoders, dynamic graph structures with time-decayed edges, and mixture-of-experts fusion across graph, sequence and tabular branches — then test whether the added complexity is justified by stability and interpretability.
Each complex model must earn its place against simpler, interpretable alternatives. That's what makes the research credible.
A latent-factor reference point for user-content preference learning.
Tests whether structured business features explain the target without deep sequence or graph modelling.
Evaluates sparse feature crosses and high-order feature combinations.
Test short-term behaviour sequences and Transformer-style ranking.
Test relationship structure across users, items, categories and platforms.
Combine graph, sequence and tabular signals into a single research framework.
Business, data and engineering teams each need different evidence. We evaluate every model from several angles.
Clarify the business problem, data fields, anonymisation boundaries and evaluation requirements.
Implement baselines, sanity-test, then design the hybrid architecture and experiment plan.
Parameter search, ablation testing, platform/cohort and cold-start evaluation with reproducibility checks.
Technical reports and working prototypes reviewable by both technical and business stakeholders.
We don't begin by assuming the most complex model is best. We compare simpler baselines, isolate each source of improvement, and examine trade-offs — using data versions, code commits, configuration files and logs so results stay reproducible.
We can help evaluate the technical path before you commit to a direction.
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