Big data mining for E-commerce


Personalized Referral Service and Big Data Mining for E-commerce Machine learning

Privacy-Preserving Selective Aggregation

Privacy-Preserving Selective Aggregation

Machine learning (ML) is one of the main methods to address the problem of big data mining for E-commerce. The big data produced by transaction, interaction and observation from e-commerce enterprises. e-commerce greatly provide decision-making service for marketing strategy. We take the e-commerce data of tea-device enterprise as an example, use the FP-grow algorithm for getting frequent item sets. Naive Bayesian algorithm for feature vector to implement clustering learning for precision marketing and personalized online referral services. Finally, we evaluate the feasibility of big data mining with Machine learning (ML) through the profit produced by the sale of goods.

Experimental results show that ML can also achieve precise marketing, and can further increasing about 20% marginal profit for goods. E-commerce enterprises have accumulated a large number of data, contains transactions, interaction and observation, even social networking. These data make the individualized demands becoming more and more obvious, and makes the marketing field change from “product to customer-centered”. At present, the precision marketing based on big data has brought challenges and new possibilities for the marketing strategy of the enterprise. One is to analyze the data and behavior of consumers, construct the model to reveal the relationship between personal consumption behavior and personal income.

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