clustering based collaborative filtering using incentivized PPT 4.pptx

clustering based collaborative filtering using incentivized

1. PRAKASAM ENGINEERING COLLEGE :: KANDUKUR Affiliated to JNTU Kakinada Department Of Computer Science And Mca CLUSTERING BASED COLLABORATIV FILTERING USING AN INCENTIVIZED/PENALIZED USER MODEL A Project submitted to JNTUK in the partial fulfillment for the award of Master of computer Applications Submitted by GUMMALLA RAMYA (22F91F0025) Under Guidance of D r.K.Subba Reddy , M.Tech

2. CLUSTERING BASED COLLABORATIV FILTERING USING AN INCENTIVIZED/PENALIZED USER MODEL

3. 1. Abstract Introduction 3. Literature Survey 4. Existing System & Disadvantages 5. Proposed System & Advantages 6. Problem Statement 7. Hardware & Software Requirements 8. System Architecture 9. Modules 10. UML diagrams 11. Simulation Results 12. Conclusion 13. Future Enhancement 14. References

4. Your proposal for a clustering – based CF method with an incentivized /penalized user model is intriguing. Focusing on maximizing recall and F1 score by utilizing user preferences within clusters is a smart approch . It seems like your method could significantly improve recommendation accuracy. Have you begain implementing and testing your idea yet . ABSTRACT

5. we provide in-depth theoretical analysis to prove that our schemes are capable to satisfy the differential privacy constraint. To evaluate the effectiveness, we have implemented our schemes in mobile payment experiments. Experimental results illustrate that the relevance between the consumption amount and online bank amount is reduced significantly, and the privacy losses are less than 0.5 in terms of mutual information.

6. People are likely to have an increasing difficulty in finding their favourite content. E ffectively since extensive collections of video, audio, papers, art, etc. H ave been created both online and offline. For example, over hundreds of feature films and hundreds of thousands of books have been produced and published every year in the US . However, one person would read at most about 10,000 books in his/her life, and then he/she must choose his/her favourite books among them. INTRODUCTION

7. On the one hand, recommender systems have been developed and used in diverse domain (e.g., th movie industry, the music industry, and so on ) by helping people to select appropriate content based on individual preferences.

8. S.NO TYPE/TITLE AUTHORS MAIN OBJECTIVE 1 toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions G. Adomavicius and A. Tuzhilin This project presents an overview of the field of recommender systems and describes the current generation of recommendation methods 2 Amazon.com recommendations: Item-to- item collaborative ltering G. Linden, B. Smith, and J. York Recommendation algorithms are best known for their use on e-commerce Web sites, where they use input about a customer's 3 Matrix factorization techniques for recommender systems Y. Koren , R. Bell, and C. Volinsky We show through examples that the embedding of the algorithm in the user experience dramatically affects the value LITERATURE SURVEY

9. CF is one of the most popular techniques used by recommender systems, but has some shortcomings vulnerable to data sparsity and cold-start problems. If the data sparsity problem occurs with insufficient information about the ratings of users on items, then the values of predicted preference become inaccurate. Moreover, new users or items cannot be easily embedded in the CF process based on the rating information. There have been a plenty of challenges tackling these two problems. On the other hand, some of studies focused on how to improve prediction accuracy of CF- aided recommender systems. EXISTING SYSTEM

10. In the existing work, the system has some drawbacks such as dependence on human ratings, performance decrement when data are sparse, and disability of recommendation for new users (i.e., cold-start users) and items. The system is less effective due to lack of content based recommendations. DISADVANTAGES

11. An easy-to-implement CBCF method using the IPU model is proposed to further enhance the performance related to UX. To design our CBCF method, we first formulate a con- strained optimization problem, in which we aim to maximize the recall (or equivalently F 1 score) for a given precision . The system numerically finds the amount of incentive/penalty that is to be given to each item according to the preference tendency by users within the same cluster. PROPOSED SYSTEM

12. ADVANTAGES The main advantages of model-based CF are an improvement of prediction performance and the robustness against the data sparsity . The Memory based CF is implemented which easy to implement and effective to manage .

13. Processor - Pentium –IV RA - 4 GB (min) Hard Disk - 20 GB Key Board - Standard Windows Keyboard Mouse - Two or Three Button Mouse Monitor - SVGA HARDWARE REQUIREMENTS

14. SOFTWARE REQUIREMENTS Operating System - Windows XP Coding Language - Java/J2EE( JSP,Servlet ) Front End - J2EE Back End - MySQL

15. ARCHITECTURE

16. ADMIN : Admin architecture is crucial for maintaining the operational health and efficiency of IT systems. WEB DATABASE : A web database is a dataset containg labeled exmaples of both real and deepfake media is used to train the model. Accepting all user queries : This module extracts relevant features from the input data, which could include facial landmarks, texture patterns,or motion characteristics.

17. VIEW USER DATA DETAILS :After the model makes predictions,post -processing techniques may be applied to further refine the results,such as filtering out false positives or improving the confidence scores. REGISTERING THE USER : the final output of the system,which could be a binary classificaton indicating whether the input is real or fake, along with confidence scores or probability estimates . REMOTE USER : When considering system architecture for remote users,it’s important to design a solution that ensures accessiblity .

18. User Admin Request & Response MODULES

19. USER

20. In this module, there are n numbers of users are present. User should register before doing some operations . And register user details are stored in user module. After registration successful he has to login by using authorized user name and password. Login successful he will do some operations like View your Details & Search users based on Domain sign ,Search for Posts & View specified post and recommend to other by feeding your interest on the Posts, View my search History ,View recommends based on Domain ,View user interests on the post.

21. ADMIN

22. In this module , the Admin has to login by using valid user and password . After login successful he can do some operations such as Add Domain ,Add Posts based on Domain, List All Posts with ranks , List All Recommended Posts based on Domain , List All reviewed Posts , List Users and authorize ,List All Search History ,View Similar Domain users based on Domain sign, View Similar user services.

23. REQUEST&RESPONSE

24. In this module, the admin can view the all the friend request and response. Here all the request and response will be stored with their tags such as Id, requested user photo, requested user name, user name request to, status and time & date. If the user accepts the request then status is accepted or else the status is waiting.

25. ALGORITHMS Algorithm 1: Proposed CBCF using the IPU model 1 c ≥ γ then 2 if rˆu,i ≥ β then 3 Recommend item i to user u; 4 else Drop item i ; 5 else 6 if rˆu,i ≥ α then 7 Recommend item i to user u ; 8 else Drop item i ; 9 end

26. Algorithm 2: CBCF using the IPU model 1 Clusters C ∈ {C1, · · · , Cc }; 2 Initialize the n × m rating matrix RCBCF ; 3 Rˆ ← a function of rating prediction with RCBCF ; 4 Initialize the threshold values α, β, and γ ; 5 for u ← 1 to n do 6 Iu ← items of missing ratings in the test set for user u ; 7 rˆu,Iu ← predicted rating values of Iu ; 8 for i ← 1 to | Iu | do 9 Ctmp ← a cluster to which user u belongs; C¯I 10 tmp ← average rating on item i in Ctmp ; 11 if rˆu,i ≥ α then 12 Recommend item i to user u; else if rˆu,i ≥ β && C¯I 13 tmp ≥ γ then 14 Recommend item i to user u ; 15 else Drop item i; 16 end 17 end

27. Use Case Class Diagram Sequence Diagram Data Flow Diagram UML DIAGRAMS

28. A use case diagram with "read dataset, preprocess, split, train algorithm, predict result " depicts the high-level interactions between users (actors) and the system to accomplish tasks related to data processing and machine learning. This use case diagram provides a clear overview of how users interact with the system to perform tasks such as data ingestion, preprocessing, model training, and prediction. USECASE DIAGRAM

29. Blue print of your System or Subsystem. A type of static structure diagram that describes the structure of a system by showing the system's classes, their attributes, operations and the relationships among objects. A class diagram in Unified Modeling Language (UML) provides a structural view of a system by representing classes, their attributes, methods, and relationships between them. CLASS DIAGRAM

30. A sequence diagram in Unified Modeling Language (UML) is a kind of interaction diagram. It shows how processes operate with one another and in what order. It is a construct of a Message Sequence Chart. Sequence diagrams are sometimes called event diagrams, event scenarios, and timing diagrams. SEQUENCE DIAGRAM

31. The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of input data to the system, various processing carried out on this data, and the output data is generated by this system. The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. DATA FLOW DIAGRAM

32. RESULT

33. User Details

34. In this project, we proposed a CBCF method using the IPU model in recommender systems by carefully exploiting different preferences among users along with clustering. Specifically, in the proposed CBCF method, we formulated a constrained optimization problem in terms of maximizing the recall (or equivalently F1 score) for a given precision. As a main result, it was demonstrated that the proposed CBCF method using the IPU model brings a remarkable gain in terms of recall or F1 score for a given precision. A possible direction of future research in this area includes the design of a new clustering-based CF method by exploiting the properties of modelbased CF approaches (e.g., matrix factorization). CONCLUSION

35. To the best of our knowledge, this is the first effort about online consumption protection and boundary issue under differential privacy. Many challenging issues still remain, including protecting the location of shopping, handling the data transmission protection issue, and developing techniques for protecting the mobile applications, which we plan to address in our future work. FUTURE ENHANCEMENT

36. G. Adomavicius and A. Tuzhilin , ``Toward the next generation of recom - mender systems: A survey of the state-of-the-art and possible extensions,'' IEEE Trans. Knowl . Data Eng. , vol. 17, no. 6, pp. 734_749, Jun. 2005. G. Linden, B. Smith, and J. York,``Amazon.com recommendations: Item- to-item collaborative _ ltering ,'‘ IEEE Internet Comput . , vol. 7, no. 1, pp. 76_80, Jan./Feb. 2003. Y. Koren , R. Bell, and C. Volinsky , ``Matrix factorization techniques for recommender systems,'' Computer , vol. 42, no. 8, pp. 30_37, Aug. 2009. REFERENCES