Proactive moderation
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The document outlines a framework for proactive moderation and fraud detection in e-business that addresses the need for trustworthy products by predicting user needs and trust levels. It details the system's process for collecting and analyzing customer feedback to discern product trustworthiness, involving features such as human expert rule application and a decision-making mechanism for product recommendations. Empirical experiments suggest that the proposed model can enhance user experience by significantly reducing customer complaints and fraudulent activities.
Proactive moderation
- 1. PROACTIVE MODERATION AND A PERSONALISED SYSTEM FOR FRAUD PRODUCT DETECTION Under the Esteemed Guidance of MS.G. JYOTHI (Assistant Professor) By K.SUNIL (10L35A1202) P. RAMA LAKSHMI (09L31A1232) PRABHA TETA (09L31A1234) J.KARTHIK (09L31A1219)
- 2. ABSTRACT 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.
- 3. 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.
- 4. INTRODUCTION Fraud detection and web personalization are the two key technologies needed in various e-business applications to, • Manage customer organization relationships • Promote products • Manage Web site content • Provide knowledge to the user about the product. The objective of this application is to “provide users with the trustworthy products they want or need”.
- 5. Name : Proactive Moderation and A personalized System for Fraud Product Detection Purpose : To make user available time with trust worthy products without Spending much of the time in knowing about the product Inputs : Ratings, Feedback Outputs : Trusty worthy products are made available Security : Usernames and password to each user User Interface : Buttons and links on the screen allow the user to control the system. REQUIREMENT SPECIFICATION The following are the functional and non functional Requirements 5
- 6. PROCEDURE The phases of this process are: Collection of data The data to analyze is all about whether to trust the product or not so the data will be • Feedback from customer about the product • Where the product has not meet the customer needs like poor services/manufacturing product mismatch not delivered Product damaged
- 7. Analysis of the collected data The ways that are employed in order to analyze the collected data include Rule-based features: Human experts with years of experience created many rules to detect whether a user is fraud or not. It checks whether the product has been or complained as untrusting or fraud. The trust for particular product(X) can be calculated by Trust(X)=100-Fraud(X) Fraud(X)=No of complaints(X)/(No of users(X)*0.01)
- 8. Selective labeling: If the fraud score is above a certain level, the case will enter a queue for further investigation by human experts and the cases whose fraud score are below are determined as clean by the human expert. Decision making/Final Recommendation The decision or the final Recommendation after analysis part is to decide whether to ban the product or to trust the product. If the product is banded by the admin then no user can view or buy the product providing the user only the trustworthy products.
- 9. ANALYSIS Existing System Proposed System Simplifying access to information is not done Improves the productivity by simplifying access to information More time is required to decide whether to trust the product or not. Reduces the time to decide whether to trust the product or not. Involves Fraudulent Activities for illegal profits Fraudulent Activities are reduced Delivers to the right person but not always the good content delivers the right content to the right person to maximize immediate and future business opportunities
- 10. DESIGN Admin User Seller Complaint filing Fraud churn The system can be broadly divided into the following modules:
- 11. • Login • Authorize Sellers • Manage sellers • View complaints of the customer • Decision to trust/block the products An Admin performs the following actions : This is represented in the following UML diagrams ADMIN The admin acts as an intermediator between seller and the customer. An Admin is responsible to maintain the website information giving a trust to the customers. If the admin feels all the products from particular seller mostly are not trusted he can also remove the seller and his related products.
- 12. Use case Diagram for Admin Login Logout View Sellers Admin Manage Sellers
- 13. Login continue/block the product View Complaints Set trust/untrusted Admin Logout
- 14. • Can add a new Product • Can delete a product • Can place New Offers to the product • Can modify information related to the product such as price ,basic information etc… A Seller performs the following actions : This is represented in the following UML diagrams SELLER The Seller module includes different sellers who wish to sell their products. The seller needs to be approved by administrator after a seller submits his registration. A Seller can add or delete or modify information about different items.
- 15. Sequence Diagram for user Login Offers to Products Logout View Products Seller Edit information
- 16. • Register/Login • View Products • View Offers • Purchase Products • Give Complaint A customer performs the following actions : This is represented in the following UML diagrams CUSTOMER After successful registration, customer will be provided with a gallery of different products which include the product name, Price, Sellers name etc. While buying a product a customer can view the percent of trustworthiness towards the product given by other users. After purchasing a customer can also file complaint on that product where he feels uncomfortable
- 17. Sequence Diagram for user Login Login View Products Purchase Products Logout View Offers Customer file Complaint
- 18. databaseCustomer Gui validate userregister user clicks on register Enter details user details user created save user customer registered successfully show message login(usrnm,pwd) validate userdetails check user details user details validate user user valid login successful
- 20. COMPLAINT FILING • Buyers claim loss if they are recently deceived by fraudulent sellers. • The Administrator views the complaints and the percentage of various type complaints. • Through complaints values the administrator set the trust ability of the product as Untrusted or banned. FRAUD CHURN • Admin takes the decision whether to continue the seller to sell the products or not. • When some products are labeled as fraud by human experts, it is very likely that the seller is not trustable and the products too. • The fraudulent seller along with his/her cases will be removed from the website immediately once detected.
- 21. CODING <% String tpid=request.getQueryString(); String sold=null,del=null,miss=null,serv=null,dam=null,pname=null,cname=null; ResultSet rs=null; try { Connection con = databasecon.getconnection(); Statement st = con.createStatement(); String qry="select * from offers where pid='"+tpid+"'"; rs =st.executeQuery(qry);
- 23. int sold1=Integer.parseInt(sold); int del1=Integer.parseInt(del); int miss1=Integer.parseInt(miss); int serv1=Integer.parseInt(serv); int dam1=Integer.parseInt(dam); int sum=del1+miss1+serv1+dam1; Double sum1=sum/((0.01)*(sold1)); //System.out.println(sum1); double t=50.0; Double tru=100-sum1; %>
- 24. SCREENSHOTS
- 25. User Home Page
- 26. Adding Products
- 27. Complaint
- 28. CONCLUSION We build online model for fraud product detection while concentrating on customer needs. By empirical experiments on a real world online fraud detection data, we show that our proposed online probit model framework, which combines online feature selection, bounding coefficients from expert knowledge and multiple instance learning, can significantly improve over baselines . This can be easily extended to many other applications, such as web spam detection, content optimization and so forth Websites that delivers highly personalized and trusted experiences top the traffic and revenue rankings across the globe.