The document proposes developing a recommender system using a movie lens dataset. It discusses using a collaborative filtering approach that divides users into virtual users based on product categories rated. This category-based collaborative filtering is intended to improve the performance and efficiency of calculating nearest neighbors compared to traditional collaborative filtering. Key phases include categorizing products, dividing user ratings, generating virtual users, analyzing virtual users, finding nearest neighbors, and generating recommendations by combining results for virtual users. The proposed system aims to more efficiently provide personalized recommendations to users.

1. Developing A Recommender System
On The Basis of
Movie Lens Data Set
Under the Guidance of:
Mr. M VenuGopal Reddy
(Associate Prof.)BATCH VIII
Sindhu Valavoju (10016T0920)
Saigurudatta P.V (10016T0945)
Tharun Katanguru (10016T0905)
Mounika Parsha (10016T0904)
Vikram Konatham (10016T0951)

2. Abstract
Recommender System
Types of Recommender Systems
Criteria Followed
Hybrid Recommended System
Existing System
Proposed System
Data Flow Diagram
Expected Outcome

3. ABSTRACT:
The basic approach that has been proposed to develop a
recommender system is COLLOBORATING FILTERING.
In CF recommendations for a user are computed based on the k
nearest neighbours.
A Virtual User is a service that when executed iteratively,
generate traffic from their location to target.
Initially, the rating given by a user is divided into categories
based on the products which are considered to be given by a
VIRTUAL USER.
The recommendations of corresponding virtual users of target
user are combined for recommendation.
This increases the performance of the recommender system and
also efficiency in calculating ‘k’ neighbours.

4. Recommender system receives information from the
user and recommends the product that fits their needs
the best
These recommender systems have become a key
component of the modern E-Commerce applications.
Collaborating Filtering approach has been proposed to
build a recommender system.
Data set contains three files, movies.dat, ratings.dat
and users.dat. Also included are scripts for generating
subsets of the data to support rating predictions.
Eg: amazon.com uses recommender system to suggest
books to the users.

5. Algorithm Criteria:
1. Quality of Predictions
2. Speed/Scalability
3. Easily Updated
Secondary Criteria:
Cold start ability
Sparse data handling

6. Content-based (Eg: movielens.org)
make suggestions based on a computation of the utility
of each object for the user.
Utility-based (Eg: last.fm.com )
functional knowledge: how a particular item meets a
particular need.
Knowledge-based (Eg: whattorent.com )
objects defined by their associated features.

7. Recommended System typically produce list of
Recommendations in two ways.
COLLABORATIVE FILTERING : It is a process
of filtering information or pattern using techniques
involving collaborations among multiple users.
CONTENT-BASED FILTERING : It is based on
characteristics of items that are going to be
recommended. In particular various items are
compared with items previously rated by the user and
best match is recommended.

8. Combine multiple methods in order to take advantage of
strengths and alleviate drawbacks
1.Weighted
▫Scores/votes of several recommendation techniques combined
together to produce a single recommendation
2.Switching
▫System switches between recommendation techniques
depending on the current situation
3.Mixed
▫Recommendations from several different recommenders
presented at the same time

9. 4.Feature combination
▫Features from different recommendation data sources thrown
together into a single recommendation algorithm
5.Cascade
▫One recommender refines the recommendations given by
another
6.Feature augmentation
▫Output from one technique is used as an input feature to
another
7.Meta-level
▫The model learned by one recommender is used as input to
another

10. Collaborating filtering approaches are of two types:
1.Memory based CF: These systems compute
recommendations based on the previous history of the user.
2.Model based CF: They compute predictions on the basis of
modeling the user and item
In CF ,Recommendations for a target user is computed based on
the ratings of ‘k’ nearest neighbors.
It has three steps:
1.Data Representation,
2.Neighbourhood Formation,
3.Generating recommendations.
Existing System:

11. The comparison is done at user-user level. Hence
there is a chance of missing the near neighbours.
User may or may not request for all categories of
items
Inefficient due to:
Improving the performance of the Collaborative
filtering approach is the main research issue.

12. It improves the memory-based CF approach by exploiting
categories of the product.
Here the rating given by the user is divided into sub groups based
on the category of the products.
The ratings of each sub-group is considered to be given by a
virtual user
Now the recommendations for the target user can be computed
by using Collaborating Filtering at category-level, this
comparison is done to find the near neighbors.
This filtering is named as Category-Based Collaborative
Filtering.
Similar to CF it also has 3-steps.
Proposed System:

13. Data Representation: In CCF a user is fragmented in to
virtual users based on category of used data.
Neighborhood Generation: Neighborhood is formed by
processing ratings of all corresponding virtual users.
Generating Recommendations: We have to generate
recommendations for the corresponding virtual user.
These are combined to generate recommendations
finally to target user.
1.Random Approach: We have to combine all
recommendations of all the users and then pick top N
recommendations for the target user.
2.Raking Approach: We select top P ranked virtual users
and then follow random approach to find top N
recommendations.

14. Letusconsider,setof usersof whichtargetuserisUser1.Categoryof
booksbeXandY.
Sosetof nearneighborsmustbeconsidered. Incaseof CF,thenear
neighborswill beUser4.
Whereasincaseof Category-basedCFthenearneighborsalongwith
User4, User5and User3arealsothenearneighbors..
USER X1 X2 X3 Y2 Y3
User1 1 0 1 1 0
User2 1 1 0 0 0
User3 0 1 1 1 0
User4 1 0 1 0 1
User5 0 1 0 1 0

15. Project Modules:
 Collection of Data set :
Includes Movies, ratings and users data sets.
 Categorization of Products:
The products are classified based on their genre.
 Division of ratings:
Classifying the quality of ratings that are given by user.
 Creation of Virtual user:
Virtual users are created for each category.
 Analyzing the Virtual user.
Ratings of Virtual users are now compared to find neighbors.
 Finding ‘k’ nearest neighbors.
Top recommenders are selected a nearest neighburs.
 Generation of recommendations using CCF.
Based on the frequency count recommendations are
generated.
 Coding.
 Documentation.

16. Requirements:
 Hard Ware
o Memory: 512MB
o Processor: P4
o Hard Disk: 40GB
 Soft Ware
o OS: Windows
o Front End:
ApacheTomCat (Java)
o Back End: Oracle
o Design Layout:
NetBeans.

17. Start
User Profile
Ratings
Product Profile
Match
Recommendations Generated
Display “No Recommendations
Found”
END
Flow Chart

18. User
Rating
User Profile
Product
Product
Profile
Recommender System
Virtual User
Generation
Recommendations
Generated
Data Flow Diagram

19. Expected Outcome:
We made an effort to improve the performance the CF
approach which is being used to build
recommendation systems.
We have proposed a framework in which each user is
divided into virtual users based on the categories of
the products rated.
The proposed approach divides each user into
corresponding virtual users, computes
recommendations for each virtual user and combines
these recommendations to give recommendations to
the target user in efficient manner to CF.

20. Advantages:
Efficiency of the system can be increased by the
proposed approach than that of Collaborating
Filtering.
Performance of the system can be increased in
calculating the nearest neighbors using the concept of
virtual users in the item-based, model-based and
other approaches of CF.