Data Mining · NLP · 2026
Food Delivery Pattern Analysis
Association rules, PageRank and BERT sentiment turned into smart meal recommendations.
5
Techniques
PageRank
Graph ranking
BERT
NLP model
Recommender
Output
The problem
Delivery platforms such as Talabat, Uber Eats and Deliveroo sit on huge order logs and review text, but rarely connect the two into a single view of what customers actually want.
The solution
A combined data mining and NLP pipeline that discovers which meals travel together, ranks the most influential dishes in an order graph, reads the sentiment of reviews, and merges all three signals into recommendations.
Association rule mining
Apriori and FP-Growth surface frequent itemsets and the rules behind them.
- Burger ➜ Fries, Pizza ➜ Cola, Pasta ➜ Garlic Bread, Shawarma ➜ Pepsi
- Support, confidence and lift used to keep only meaningful rules
- Rules feed bundle offers and cross-sell suggestions
Graph analysis & PageRank
- Orders modelled as a graph of meals connected by co-purchase
- PageRank ranks the most central and popular dishes
- Link analysis exposes clusters of meals ordered by the same segments
BERT sentiment & recommendations
- Customer reviews classified with a BERT transformer
- Sentiment score blended with rule confidence and PageRank weight
- Final recommender proposes meals customers are likely to order and to enjoy
Results & impact
- ✓Clear, explainable ordering patterns instead of black-box suggestions
- ✓Popularity ranking that reflects order structure, not just raw counts
- ✓Recommendations that account for how customers felt, not only what they bought
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