SURVEY PAPER ON DYNAMIC RECOMMENDATION SYSTEM FOR E-COMMERCE
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Abstract
Recommender system is a strategy in e-commerce, which recommends items based on the user’s interest. It has the capability to predict whether a particular user would prefer an item or not based on the user’s profile. Recommender systems are useful for both e-commerce service provider and users. So it should be required for a recommendation system to provide most preferable items to the user’s interest. This paper presents a dynamic recommendation system to provide recommendations on the user’s interest. In this dynamic recommendation system first of all the web usage information is utilized to find the user’s behavior and then similar user behavior score is computed. In the second phase, product information is collected on the basis of current search information about the user through which sentiment score and social media popularity score are computed. On the other side coefficient matrix is used to calculate user’s purchasing power. These three factors- similar score behavior, sentiment score and popularity score are used to calculate the combined weight for the particular product. Then a coefficient matrix and the computed weights are used to calculate the possible recommendation of the product for the user.
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