Recommendation Engine, Its base technologies and Its primary modules.

1. Recommendation
Engine

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. Objectives
 Information Filtering System
 Recommendation engine recommends
- User based
- Item based
- Slop based
 Run On Cloud Environment

4. Introduction
 Engine - Gives Suggestion Based on
movies,songs,videos,websites,books,images and also
social elements.
 Applicable for E-business.
 Useful for both Customers and online Retailers
 Recommendation engine is being used at
Amazon, Youtube, Facebook,Twitter

5. Scope
 Our system will only provide Recommendation service
only.
 Recommendation will be genrated based on user’s
historical activity like purchase pattern as well as
rating and like.
 Recommendation will be either stored on database
,file or directly retrieved to retailers web application.

6. Problems with existing System
 Take more Time to generate recommendations
 No real time recommendation for large data

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. Recommendations-Type
 User Based Recommendation

9. Recommendations-Type
 Item Based Recommendation

10. Proposed System Architecture

11. Technologies to be used
 Hadoop
 Mahout
 Graphlab
 Google prediction
 Google Storage
 Google App engine

12. Modules of System
 User Module
 Admin Module
 Recommendation Module
 File management Module
 Search Module

13. Integration of Technologies
 Mahout based Recommendation
 Graph based Recommendation
 Google prediction Based Recommendation

14. Technology: HADOOP
 Hadoop is a top-level Apache project being built
and used by a global community of contributors.
 Hadoop project develops open-source software for
reliable, scalable, distributed computing.
 It enables applications to work with thousands of
nodes and peta bytes of data.
 Hadoop also support Map/Reduce Algorithm.
 It provides HDFS file system that stores data on
the compute nodes.

15. Hadoop

16. Graphlab
 It is New Parallel Framework for Machine
Learning Algorithm .
 Now a day ,Designing and implementing efficient
and 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
Data Graph
Shared Data Table
Scheduling
Update Functions and
Scopes
GraphLab
Model

18. CPU 1 CPU 2 CPU 3 CPU 4
MapReduce – Map Phase
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Embarrassingly Parallel independent computation
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No Communication needed

19. CPU 1 CPU 2 CPU 3 CPU 4
MapReduce – Map Phase
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20. CPU 1 CPU 2
MapReduce – Reduce Phase
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Fold/Aggregation

21. Graphlab in Recommendation
 Graphlab provide better way in recommendation
engine.
 Its just first load fits simple dataset file.
 In graphlab we can also implement various algortihm
like k-means clustering ,fuzzy logic, pagerank and etc.
 Its first translated dataset into Matrix form.
 And then according to different algorithm it
generated recommendated output.

22. Google Prediction Service
 Google cloud service used for Building smart
Application.
 Having Machine learning Algorithms.
 Related to Artificial Intelligence.

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 Application
server
 Google Cloud Storage :
 Enable Data to store on Google Cloud database.

24. Google Prediction Service

25. Technology : MAHOUT
• Apache Mahout is open source project by the Apache
Software Foundation (ASF).
• The primary goal of Mahout is creating scalable
machine-learning algorithms.
• Several Map-Reduce in Mahout enabled clustering
implementations, including k-Means, fuzzy k-Means,
Canopy, Dirichlet, and Mean-Shift.
• Mahout have fix datasets which generally take as data
input.
• Amzon EC2 are working with Hadoop and Mahout.

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. Application of recommendation
 Yahoo!
 Facebook
 Twitter
 Baidu
 eBay
 LinkedIn
 New York Times
 Rackspace
 eHarmony
 Powerset
Recommendation
Engine

28. Future enhancement
 Integration with Web Application like Jsp , Servlet
 Integration with Database like
Hive, Hbase, Mongodb, Couch db
 Cloud based recommendation Service
 Integration of Mahout , Graphlab and Google prediction
based recommendation services.
 Mobile application integration

29. Thank You