Abstract: Methods and systems for recommending limited, personalized and relevant list of prospects to enterprises, in a configurable, automated, scalable and machine-learnt way. According to one embodiment, raw data about potential prospects across diverse areas is collected from various data sources. The raw data is transformed to variables containing values in a binary format, also known as interests, in accordance with a predetermined set of rules. An interest graph is created with the interests as nodes and affinity between them as edges and net affinities are calculated and stored in an interest table. The user's requirements are understood through user input and a set of user-relevant interests is captured and extended with additional similar interests from the interest table. Multiple scores are calculated for each of the potential prospects based on this set of interests. A net score for each potential prospect is calculated and highest potential prospects are finally recommended.