: Nowadays E-commerce is very popular for customers to buy products online. In order to attract customers' owners of E-commerce are employing several techniques to suggest products with varying degree of matching. These techniques are creating information overhead and increasing cognitive overload on the customer, which may create a negative impact on customer's purchase decision. Therefore, it is perhaps necessary to find out a better way of recommendation to meet customer requirements very closely in a single interface. In this paper, we propose a new model which allows customer to search product based on his/her priorities of different features of a product. This model inherently builds an ontology structure, and present a priority enriched faceted search result to the customer to get his intended product, by visiting multiple internet vendor's sites in a single interface. Taking input features and priority, the model generates a set of feature set combinations from which it extracts needful combinations that are available with different vendor sites. To present these combinations to the customer in the order of his intent, we introduce a weight association mechanism which calculates similarity weights among the combinations. The system then attempt The ordered feature set products are available in different brands sold by different vendors. Further the system attempt to assign unbiased rank to recommend a product to the user in these combinations with a best brand and vendor. For this purpose we have introduced a 40:60 approach which uses other customer rating and the number of customers participated in ranking on these combinations. Key words�E-commerce, Information Overhead, Faceted search, similarity weight

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