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RecommendationEngine
Outlines Introduction Objectives Scope Problem with existing system Purpose of new system Proposed architecture Tec...
Objectives Information Filtering System Recommendation engine recommends- User based- Item based- Slop based Run On Clo...
Introduction Engine - Gives Suggestion Based onmovies,songs,videos,websites,books,images and alsosocial elements. Applic...
Scope Our system will only provide Recommendation serviceonly. Recommendation will be genrated based on user’shistorical...
Problems with existing System Take more Time to generate recommendations No real time recommendation for large data
Purpose of new System Less time for generating recommendations Applicable for Bigdata Recommendations be several algori...
Recommendations-Type User Based Recommendation
Recommendations-Type Item Based Recommendation
Proposed System Architecture
Technologies to be used Hadoop Mahout Graphlab Google prediction Google Storage Google App engine
Modules of System User Module Admin Module Recommendation Module File management Module Search Module
Integration of Technologies Mahout based Recommendation Graph based Recommendation Google prediction Based Recommendation
Technology: HADOOP Hadoop is a top-level Apache project being builtand used by a global community of contributors. Hadoo...
Hadoop
Graphlab It is New Parallel Framework for MachineLearning Algorithm . Now a day ,Designing and implementing efficientand...
17Data GraphShared Data TableSchedulingUpdate Functions andScopesGraphLabModel
CPU 1 CPU 2 CPU 3 CPU 4MapReduce – Map Phase18Embarrassingly Parallel independent computation12.942.321.325.8No Communicat...
CPU 1 CPU 2 CPU 3 CPU 4MapReduce – Map Phase19Embarrassingly Parallel independent computation12.942.321.325.824.184.318.48...
CPU 1 CPU 2MapReduce – Reduce Phase2012.942.321.325.824.184.318.484.417.567.514.934.32226.261726.31Fold/Aggregation
Graphlab in Recommendation Graphlab provide better way in recommendationengine. Its just first load fits simple dataset ...
Google Prediction Service Google cloud service used for Building smartApplication. Having Machine learning Algorithms. ...
Google Prediction Service Google Prediction API : Set of Methods for Data Analysis. Libraries support multiple language...
Google Prediction Service
Technology : MAHOUT• Apache Mahout is open source project by the ApacheSoftware Foundation (ASF).• The primary goal of Mah...
Implementation Issues to solved Lack of knowledge about hadoop,mahout,hive Memory issue Operating system support Load ...
Application of recommendation Yahoo! Facebook Twitter Baidu eBay LinkedIn New York Times Rackspace eHarmony Powe...
Future enhancement Integration with Web Application like Jsp , Servlet Integration with Database likeHive, Hbase, Mongod...
Thank You
Recommendation engine
Recommendation engine
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Recommendation engine

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Recommendation Engine, Its base technologies and Its primary modules.

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Recommendation engine

  1. 1. RecommendationEngine
  2. 2. Outlines Introduction Objectives Scope Problem with existing system Purpose of new system Proposed architecture Technologies to be used Modules of system Integration of technologies Implementation Issues to be solved Application Future Enhancement
  3. 3. Objectives Information Filtering System Recommendation engine recommends- User based- Item based- Slop based Run On Cloud Environment
  4. 4. Introduction Engine - Gives Suggestion Based onmovies,songs,videos,websites,books,images and alsosocial elements. Applicable for E-business. Useful for both Customers and online Retailers Recommendation engine is being used atAmazon, Youtube, Facebook,Twitter
  5. 5. Scope Our system will only provide Recommendation serviceonly. Recommendation will be genrated based on user’shistorical activity like purchase pattern as well asrating and like. Recommendation will be either stored on database,file or directly retrieved to retailers web application.
  6. 6. Problems with existing System Take more Time to generate recommendations No real time recommendation for large data
  7. 7. Purpose of new System Less time for generating recommendations Applicable for Bigdata Recommendations be several algorithms User based Item based Slop based Association rule mining Evaluation of recommendation
  8. 8. Recommendations-Type User Based Recommendation
  9. 9. Recommendations-Type Item Based Recommendation
  10. 10. Proposed System Architecture
  11. 11. Technologies to be used Hadoop Mahout Graphlab Google prediction Google Storage Google App engine
  12. 12. Modules of System User Module Admin Module Recommendation Module File management Module Search Module
  13. 13. Integration of Technologies Mahout based Recommendation Graph based Recommendation Google prediction Based Recommendation
  14. 14. Technology: HADOOP Hadoop is a top-level Apache project being builtand used by a global community of contributors. Hadoop project develops open-source software forreliable, scalable, distributed computing. It enables applications to work with thousands ofnodes and peta bytes of data. Hadoop also support Map/Reduce Algorithm. It provides HDFS file system that stores data onthe compute nodes.
  15. 15. Hadoop
  16. 16. Graphlab It is New Parallel Framework for MachineLearning Algorithm . Now a day ,Designing and implementing efficientand correct parallel machine learning (ML)algorithms can be very challenging. Designed specifically for ML needs Automatic data synchronization. Map phase like – Update Function . Reduce phase like – Sync Operation .
  17. 17. 17Data GraphShared Data TableSchedulingUpdate Functions andScopesGraphLabModel
  18. 18. CPU 1 CPU 2 CPU 3 CPU 4MapReduce – Map Phase18Embarrassingly Parallel independent computation12.942.321.325.8No Communication needed
  19. 19. CPU 1 CPU 2 CPU 3 CPU 4MapReduce – Map Phase19Embarrassingly Parallel independent computation12.942.321.325.824.184.318.484.4No Communication needed
  20. 20. CPU 1 CPU 2MapReduce – Reduce Phase2012.942.321.325.824.184.318.484.417.567.514.934.32226.261726.31Fold/Aggregation
  21. 21. Graphlab in Recommendation Graphlab provide better way in recommendationengine. Its just first load fits simple dataset file. In graphlab we can also implement various algortihmlike k-means clustering ,fuzzy logic, pagerank and etc. Its first translated dataset into Matrix form. And then according to different algorithm itgenerated recommendated output.
  22. 22. Google Prediction Service Google cloud service used for Building smartApplication. Having Machine learning Algorithms. Related to Artificial Intelligence.
  23. 23. Google Prediction Service Google Prediction API : Set of Methods for Data Analysis. Libraries support multiple languages. Google App Engine : Enable Application to Cloud environment Applicationserver Google Cloud Storage : Enable Data to store on Google Cloud database.
  24. 24. Google Prediction Service
  25. 25. Technology : MAHOUT• Apache Mahout is open source project by the ApacheSoftware Foundation (ASF).• The primary goal of Mahout is creating scalablemachine-learning algorithms.• Several Map-Reduce in Mahout enabled clusteringimplementations, including k-Means, fuzzy k-Means,Canopy, Dirichlet, and Mean-Shift.• Mahout have fix datasets which generally take as datainput.• Amzon EC2 are working with Hadoop and Mahout.
  26. 26. Implementation Issues to solved Lack of knowledge about hadoop,mahout,hive Memory issue Operating system support Load Balancing Configuration Data normalization Developing Clustering algorithm Configuring mahout with hadoop
  27. 27. Application of recommendation Yahoo! Facebook Twitter Baidu eBay LinkedIn New York Times Rackspace eHarmony PowersetRecommendationEngine
  28. 28. Future enhancement Integration with Web Application like Jsp , Servlet Integration with Database likeHive, Hbase, Mongodb, Couch db Cloud based recommendation Service Integration of Mahout , Graphlab and Google predictionbased recommendation services. Mobile application integration
  29. 29. Thank You

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