Proactive moderation

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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 …

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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  • 1. PROACTIVE MODERATION AND APERSONALISED SYSTEM FOR FRAUDPRODUCT DETECTIONUnder the Esteemed Guidance ofMS.G. JYOTHI(Assistant Professor)ByK.SUNIL (10L35A1202)P. RAMA LAKSHMI (09L31A1232)PRABHA TETA (09L31A1234)J.KARTHIK (09L31A1219)
  • 2. ABSTRACTThe E-business sector is rapidly evolving and the needs for web marketplaces that anticipate the needs of the customers and the trust towards aproduct are equally more evident than ever. While people are enjoying thebenefits from online trading, criminals are also taking advantages to conductfraudulent activities against honest parties to obtain illegal profits. Thereforethe requirement for predicting user needs and trust providence in order toimprove the usability and user retention of a website can be addressed bypersonalizing and using a fraud product detection system.
  • 3. Hence fraud-detection systems are commonly needed to be appliedto detect and prevent such illegal or untrusted products. In this, wepropose an online model framework which takes online feature selection,coefficient bounds from human knowledge and multiple instanceslearning into account simultaneously. By empirical experiments on areal-world we show that this model can potentially meet user needs,calculate the trust for a product and significantly reduce customercomplaints.
  • 4. INTRODUCTIONFraud detection and web personalization are the two key technologiesneeded 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 thetrustworthy products they want or need”.
  • 5. Name : Proactive Moderation and A personalized System for FraudProduct DetectionPurpose : To make user available time with trust worthy productswithout Spending much of the time in knowing aboutthe productInputs : Ratings, FeedbackOutputs : Trusty worthy products are made availableSecurity : Usernames and password to each userUser Interface : Buttons and links on the screen allow the user to control thesystem.REQUIREMENT SPECIFICATIONThe following are the functional and non functional Requirements5
  • 6. PROCEDUREThe phases of this process are: Collection of dataThe data to analyze is all about whether to trust the product or not sothe data will be• Feedback from customer about the product• Where the product has not meet the customer needs likepoor services/manufacturingproduct mismatchnot deliveredProduct damaged
  • 7.  Analysis of the collected dataThe ways that are employed in order to analyze the collected dataincludeRule-based features:Human experts with years of experience created many rules todetect whether a user is fraud or not. It checks whether the product hasbeen or complained as untrusting or fraud.The trust for particular product(X) can be calculated byTrust(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 aqueue for further investigation by human experts and the cases whosefraud score are below are determined as clean by the human expert. Decision making/Final RecommendationThe decision or the final Recommendation after analysis part is todecide whether to ban the product or to trust the product. If the productis banded by the admin then no user can view or buy the productproviding the user only the trustworthy products.
  • 9. ANALYSISExisting System Proposed SystemSimplifying access to informationis not doneImproves the productivity bysimplifying access to informationMore time is required to decidewhether to trust the product or not.Reduces the time to decide whetherto trust the product or not.Involves Fraudulent Activities forillegal profitsFraudulent Activities are reducedDelivers to the right person but notalways the good contentdelivers the right content to the rightperson to maximize immediate andfuture business opportunities
  • 10. DESIGN Admin User Seller Complaint filing Fraud churnThe 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 diagramsADMINThe admin acts as an intermediator between seller and thecustomer. An Admin is responsible to maintain the website informationgiving a trust to the customers. If the admin feels all the products fromparticular seller mostly are not trusted he can also remove the seller andhis related products.
  • 12. Use case Diagram for AdminLoginLogoutView SellersAdminManage Sellers
  • 13. Logincontinue/block the productView ComplaintsSet trust/untrustedAdminLogout
  • 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 asprice ,basic information etc… A Seller performs the following actions : This is represented in the following UML diagramsSELLERThe Seller module includes different sellers who wish tosell their products. The seller needs to be approved by administratorafter a seller submits his registration. A Seller can add or delete ormodify information about different items.
  • 15. Sequence Diagram for userLoginOffers to ProductsLogoutView ProductsSellerEdit 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 diagramsCUSTOMERAfter successful registration, customer will be provided with agallery of different products which include the product name, Price, Sellersname etc. While buying a product a customer can view the percent oftrustworthiness towards the product given by other users. After purchasing acustomer can also file complaint on that product where he feelsuncomfortable
  • 17. Sequence Diagram for user LoginLoginView ProductsPurchase ProductsLogoutView OffersCustomerfile Complaint
  • 18. databaseCustomer Gui validate userregister userclicks on registerEnter detailsuser detailsuser createdsave usercustomer registered successfullyshow messagelogin(usrnm,pwd)validate userdetailscheck user detailsuser detailsvalidate useruser validlogin successful
  • 19. COMPLAINT FILING• Buyers claim loss if they are recently deceived by fraudulent sellers.• The Administrator views the complaints and the percentage of varioustype complaints.• Through complaints values the administrator set the trust ability of theproduct as Untrusted or banned.FRAUD CHURN• Admin takes the decision whether to continue the seller to sell theproducts or not.• When some products are labeled as fraud by human experts, it is verylikely that the seller is not trustable and the products too.• The fraudulent seller along with his/her cases will be removed fromthe website immediately once detected.
  • 20. CODING<%String tpid=request.getQueryString();Stringsold=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);
  • 21. while(rs.next()){pname=rs.getString("proname");cname=rs.getString("comname");sold=rs.getString("sold");del=rs.getString("deliver");miss=rs.getString("missmatch");serv =rs.getString("service");dam =rs.getString("damage");}
  • 22. 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;%>
  • 23. SCREENSHOTS
  • 24. User Home Page
  • 25. Adding Products
  • 26. Complaint
  • 27. CONCLUSIONWe build online model for fraud product detection while concentratingon customer needs. By empirical experiments on a real world online frauddetection data, we show that our proposed online probit model framework,which combines online feature selection, bounding coefficients from expertknowledge and multiple instance learning, can significantly improve overbaselines . This can be easily extended to many other applications, such asweb spam detection, content optimization and so forth Websites that delivershighly personalized and trusted experiences top the traffic and revenuerankings across the globe.