PRO-MINING: PRODUCT RECOMMENDATION USING WEB-BASED OPINION MINING

Effective recommendation is the key to success of any online marketing strategy. In this paper, we discuss the design and development of a novel recommendation system for consumer products. The proposed system helps the customer to select product of his choice. When a prospective customer passes a query (name of the item) as an input to our system, our system returns a ranked list of suitable brands, models available for a particular item as an output. For this purpose, our system performs meta-searching to find top products on the basis of collected opinion/information from user blogs, customer reviews, and official websites of the producing companies. Then, the available data along with links to its sources is passed to a group of users and their feedback is obtained. Since different users may provide different ranking of products on basis of their views/understanding of opinion data, we perform rank aggregation to obtain a consensus ranking of products. The products are then returned in the order of the aggregated ranking. In this paper, we also present a novel rank aggregation method for aggregation of partial lists.

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