This document outlines a recommendation engine system that will provide recommendations to users based on their activity histories and preferences. It discusses objectives to build recommendation algorithms that are user-based, item-based, and slope-based. The proposed system will use Hadoop, Mahout, Graphlab, Google Prediction, Google Storage, and Google App Engine. It will have modules for users, administration, recommendations, file management and search. Technologies will be integrated to enable Mahout-based, graph-based, and Google Prediction-based recommendations on large datasets in real-time.

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
18
Embarrassingly Parallel independent computation
1
2
.
9
4
2
.
3
2
1
.
3
2
5
.
8
No Communication needed

19. CPU 1 CPU 2 CPU 3 CPU 4
MapReduce – Map Phase
19
Embarrassingly Parallel independent computation
1
2
.
9
4
2
.
3
2
1
.
3
2
5
.
8
2
4
.
1
8
4
.
3
1
8
.
4
8
4
.
4
No Communication needed

20. CPU 1 CPU 2
MapReduce – Reduce Phase
20
1
2
.
9
4
2
.
3
2
1
.
3
2
5
.
8
2
4
.
1
8
4
.
3
1
8
.
4
8
4
.
4
1
7
.
5
6
7
.
5
1
4
.
9
3
4
.
3
22
26
.
26
17
26
.
31
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