User profiles include but are not limited to social media profile, professional online profile, e-commerce profile and search profile. Each individual user nowadays has multiple user profiles, due to the fact that these users are constantly using online and offline services. These profiles are not mutually exclusive as the search habits of a user directly showcase the user`s shopping behaviour, and so on. Due to the presence of so many profiles of a single entity, there is a wide research area which has opened up in the recent years. Companies and researchers are harnessing this gap in order to provide better user experience via integrating multiple profiles and helping them to learn from one another. In this paper, we define a framework via which the user`s social and e-commerce profiles can be combined in order to better recommend their buying patterns to companies based on the items purchased by the friends which the user`s follow closely. Mining positive and negative rules (MOPNAR), firefly, top k rules and association rule mining is used in order to mine the usage patterns, and the results shows that an accuracy of more than 70% is observed when compared with the real time buying patterns.

Profile, integrated, online, MOPNAR, firefly, e-commerce, social

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