Recent advancements in business strategies marked the significance of e-commerce in marketing any service of the organization. Moreover, users are quiet dependent on the average ratings of the products showcased in the marketing interface in turn these average ratings made remarkable impact on sales phenomena of the product. The average rating of a product is the aggregation of individual users ratings biased with the tendency of the user towards publishing the opinion. The optimistic user tends to give a slight high rating than a neutral judgement and vice versa with a pessimistic user. However, these biased ratings produce an aggregate value that is degraded with its trustworthiness. This paper proposed a novel approach named DBT (De-biased Tendency) Recommender to analyze the bias in product rating which recalculates the average ratings of the products by making user tendencies as part of the process. The solution implemented on a big data environment on demand of high computation complexity involved in the process. Experimental results had shown a significant improvement in the trustworthiness of the product ratings with the proposed approach.
Social Network Analytics (SNA), on the other hand, provides insights of many perspectives of the society through the sample users participated in social network. The exponential growth of the social network and correspondingly its data leads to the demand for big data computational environments. One such popular and useful big data in SNA is GIS (Geographical Information System) data that provides geographical location data of the record generated in social networks. In order to profile a user in social networks according to GIS data, the existing methodologies uses the centroid measures such as mean, median of the GIS data available for the user. These methods failed to mention and solve the serious issues such as cold user as well does not able to consider the weightage of the data item in analytics. The proposed method in this paper focuses to define the weightage of a data item in the available GIS data according to the application and also proposed a method to identify and solve cold user problem. The experiments carried on cutting edge technologies of the big data analytics shown significance of the proposed method in profiling user w.r.t. GIS data. The results of the proposed user profiling is measured with Within Sum of Squares (WSS) measure and compared over exiting profiling methodologies shown consistent improvement for any number of clusters formed over benchmark datasets.
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