Categorizing preferencesCategorizing preferences of customers or users as they forge relationships (content-based recommendations)
Forging relationships between users Forging relationships between users in order to fill the lack of knowledge about the preferences of User A with the knowledge about User B (collaborative filtering)
Cold start problemNegotiating the “cold start problem” by combining content recommenders and collaborative filtering. We also utilize bandit algorithms.
Use cases of recommendation systems, e.g.:– Product recommendations in online shops – Support in forming a range of products or services – Filtering and sorting of search results – Partially automated customer support
Data-driven Business ConsultingDigital Transformation, Industry 4.0, Innovation management, Requirement & Readiness Assessment
Machine Learning / Artificial IntelligenceDeep Learning, Natural Language Processing, Predictive Maintenance, Anomaly Detection, Recommendation Engines
Big Data Architecture, Development and Operations for NoSQL ecosystemsArchitecture and implementation of data infrastructures, Data Lakes, Data Pipelines, IoT Platforms, BI to Big Data Migration (Hadoop, Cassandra etc.)
