Abstract: The present disclosure discloses system and method for generating hyper-personalized recommendations from the enterprise and its partner/(s) product offerings space for the users based on dynamic persona creation, influenced by a dynamic context capture and context-chaining mechanism triggered by the past and current activities of a user or the time of occurrence of an event. The User data may be processed to generate a user persona, which may further vary dynamically based on his current and historic subscription/consumption/association data. The context trigger enables the system to generate recommendations even before the need relevant to the context is realized by the user. Thus, the system is capable of generating real-time dynamic, context-driven, hyper-personalized recommendations, based on the enterprise requirements.

Abstract: This disclosure relates generally to information life cycle management, and more particularly to system and method for providing autonomous contextual information life cycle management. In order to visualize the data, initially, textual interaction may be captured from a social-networking platform. Upon capturing, business entities may be identified from the textual interaction. Subsequently, a system entity corresponding to each business entity may be identified. Subsequently, a key-value pair having a pre-defined condition corresponding to each system entity may be generated. Further a query may be generated based on the key-value pair. Upon generating the query, a context may be derived associated to the textual interaction. Based on the context and the query, data may be extracted from a data repository. Subsequent to the extraction of the data, the data may be visualized in a graphical form using one or more predefined visualization templates.

Abstract: The present disclosure discloses system and method for generating hyper-personalized recommendations from the enterprise and its partner/(s) product offerings space for the users based on dynamic persona creation, influenced by a dynamic context capture and context-chaining mechanism triggered by the past and current activities of a user or the time of occurrence of an event. The User data may be processed to generate a user persona, which may further vary dynamically based on his current and historic subscription/consumption/association data. The context trigger enables the system to generate recommendations even before the need relevant to the context is realized by the user. Thus, the system is capable of generating real-time dynamic, context-driven, hyper-personalized recommendations, based on the enterprise requirements.