Monika. N.S, Lavanya. G.P ,, K.Vaishnavi,Kavya.M and Kanaiya.V.K


The Internet is growing rapidly and huge amount of data is collected in it. So data mining is very necessary. Recommending appropriate product items to the target user is challenging for continuous success of E-commerce. Most existing E-commerce recommender system aims to recommend the right product to the consumer, assuming the properties of each product are fixed. Through E-commerce, user can browse, compare and select the product items that they like in a most convenient manner, which brings great facility to the E-commerce user. There are varieties of products in each E-commerce company which are ready to be selected, compared and purchased by target user. Therefore, for the continuous success of E-commerce companies, the product should be recommended appropriately to the target user. Nowadays, many of the E-commerce system has adopted various techniques for recommendations e.g. collaborative filtering(CF) based technique, this help to realize which product has to be recommended. The reason why we put forward a Structural Balance Theory (SBT) based recommendation is that, due to the sparsity of big rating data in E-commerce, similar friends and similar product items may be absent from the user-product purchase network, which lead for big challenge to recommend appropriate product items to the target user. Our system provides user specific recommendation based on enemy of an enemy is a friend concept.


Structural Balanced Theory (SBT), Collaborative filtering (CF).

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