Data Science
Recommendation Systems
4–10 weeksPersonalized recommendationDemo
★Best match for this person% = how well it fits
More clicks
+27%
vs. no personalization (CTR)
Top-5 hits
94%
evaluation (precision@5)
Catalog coverage
92%
includes new products
The best product, content, or next action for each user is not the same — and showing it correctly can transform conversion, retention, and customer value.
We build recommendation systems that combine historical behavior, item similarity, and contextual signals to suggest the right thing at the right moment: from catalog recommendations to next-best-action logic in sales or collections. Technically: collaborative filtering (matrix factorization, ALS, deep learning), content-based models, hybrid systems, bandits for exploration-exploitation, and offline evaluation (precision@K, recall@K, NDCG) with temporal validation to prevent data leakage.
Use cases
- —You want to show personalized products, content, or services by user
- —You need a next-best-action logic for sales, collections, or support
- —You want to increase cross-sell and up-sell with behavior-based suggestions
- —You have a large catalog that your users cannot explore efficiently
- —You want to personalize communication sequences based on customer history and profile
Deliverables
- —Recommendation model with offline evaluation (precision@K, recall@K, NDCG)
- —Approach comparison: collaborative, content-based, and hybrid
- —Real-time or batch recommendation API based on required latency
- —Periodic retraining system with degradation detection
- —Catalog coverage analysis and new-item handling strategy (cold start)
- —Architecture documentation and evaluation criteria