Abstract: An approach is provided for flock recommendation for people and activities. Flock can themselves either be people or activities. The system takes input from the social accounts associated with the person, and a personality test filled in by the user, activities data from multiple third party sources and recommends them with flocks (either persons for a particular activity or activities for a group of people). It also stores an encrypted combination of user and evidence as transaction in the block chain for every recommendation done for auditing purposes. The ranking module in one embodiment, takes the result set from the recommendation module, ranks them based on the user's preferences. It considers a lot of factors including the weightages of the edges in the knowledge graph and the user info to rank these recommendation result set and finally returns them with rank score.