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The E-business sector is rapidly evolving and the needs for web market places that anticipate the needs of the customers and the trust towards a product are equally more evident than ever. While people are enjoying the benefits from online trading, criminals are also taking advantages to conduct fraudulent activities against honest parties to obtain illegal profits. Therefore the requirement for predicting user needs and trust providence in order to improve the usability and user retention of a website can be addressed by personalizing and using a fraud product detection system.
Hence fraud-detection systems are commonly needed to be applied to detect and prevent such illegal or untrusted products. In this, we propose an online model framework which takes online feature selection, coefficient bounds from human knowledge and multiple instances learning into account simultaneously. By empirical experiments on a real-world we show that this model can potentially meet user needs, calculate the trust for a product and significantly reduce customer complaints.
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