Network operators are overloaded with numerous recommendations coming from vendors, some of which come from automated recommender systems. Such automated recommendations may or may not apply to a customer’s specific environment, often lack an assessment of priority within the context of the other recommendations, and may or may not apply to an individual customer’s scenario. To address these challenges, techniques are presented herein that provide a novel approach to generating and ranking recommendations coming from a dynamic recommender system where rankings are based on enriched context from, for example, live data on real networks, activities performed by real customers, etc. Such techniques enhance the operational features of existing networks by recommending popular items to new customers, identifying critical items that can be proactively addressed in order to provide additional services, and reducing Technical Assistance Center (TAC) cases when patches exist for common issues.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.