Multi-Dimensional Customer Data Analysis in Online Auctions

In this paper, we designed a customer-centered data warehouse system with five subjects： listing, bidding, transaction, accounts, and customer contact based on the business process of online auction companies. For each subject, we analyzed its fact indexes and dimensions. Then take transaction subject as example, analyzed the data warehouse model in detail, and got the multi-dimensional analysis structure of transaction subject. At last, using data mining to do customer segmentation, we divided customers into four types： impulse customer, prudent customer, potential customer, and ordinary customer. By the result of multi-dimensional customer data analysis, online auction companies can do more target marketing and increase customer loyalty.

Research on Items Suitable for Online Auction

Based on the classification to the attribute information of auctioned commodity, the valuation signal of auctioned commodity is categorized into two groups: codable valuation signal and un-codable valuation signal. A valuation signal model is developed and used to reason under the environment of common valuation auction. The results show that the value of commodity auctioned online tends to be lower and the commodity with bigger factor that can be described online is more suitable for online auction.

A Differentially Private Auction Mechanism in Online Social Networks

The growing popularity of users in online social network gives a big opportunity for online auction.The famous Information Diffusion Mechanism(IDM)is an excellent method even meet the incentive compatibility and individual rationality.Although the existing auction in online social network has considered the buyers’information which is not known by the seller,current mechanism still can not preserve the privacy information of users in online social network.In this paper,we propose a novel mechanism based on the IDM and differential privacy.Our mechanism can successfully process the auction and at the same time preserve clients’price information from neighbours.We achieved these by adding virtual nodes to each node and Laplace noise for its price in the auction process.We also formulate this mechanism on the real network and the random network,scale-free network to show the feasibility and effectiveness of the proposed mechanism.The evaluation shows that the result of our methods only depend on the noise added to the agents.It is independent from the agents’original price.

Dynamically integrated regression model for online auction data

We propose a dynamically integrated regression model to predict the price of online auctions,including the final price.Different from existing models,the proposed method uses not only the historical price but also the information from bidding time.Consequently,the prediction accuracy is improved compared with the existing methods.An estimation method based on B-spline approximation is proposed for the estimation and the inference of parameters and nonparametric functions in this model.The minimax rate of convergence for the prediction risk and large-sample results including the consistency and the asymptotic normality are established.Simulation studies verify the finite sample performance and the appealing prediction accuracy and robustness.Finally,when we apply our method to a 7-day auction of iPhone 6s during December 2015 and March 2016,the proposed method predicts the ending price with a much smaller error than the existing models.