In this era of big data , Recommender systems are becoming increasingly important for businesses because they can help companies offer personalized product recommendations to customers. There have been many acknowledged recognized successes of consumer-oriented recommender systems, particularly in e-commerce. However, when it comes to Business to-Business (B2B) market space, there has been less research and real-time application of such systems.

In our case study, we present a hybrid approach of building a context-sensitive recommender system incorporating semantic knowledge in the form of domain ontology and a custom user- user collaborative filtering model in a B2B space. Using Engineering Products transaction data of an Instrumentation company, we demonstrate that this recommendation algorithm offers improved personalization, diversity and cold start performance compared to standard Collaborative Filtering based recommender system.