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)

CONTENTSAbstractRecommender SystemTypes of Recommender SystemsCriteria FollowedHybrid Recommended SystemExisting SystemProposed SystemData Flow DiagramExpected Outcome

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.

Recommender System:Recommender system receives information from the user and recommends the product that fits their needs the bestThese 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.

Algorithm Criteria:1. Quality of Predictions2. Speed/Scalability3. Easily Updated

Secondary Criteria:Cold start ability Sparse data handling

Types of Recommender Systems: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.

Criteria Followed :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.

Hybrid Recommender Systems: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

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

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:

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.

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 userNow 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:

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.

Let us consider, set of users of which target user is User1. Category of books be X and Y.

So set of near neighbors must be considered. In case of CF, the near neighbors will be User4.

Where as in case of Category-based CF the near neighbors along with User4, User5 and User3 are also the near neighbors..

USER X1 X2 X3 Y2 Y3User1 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

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.

Requirements:Hard WareoMemory: 512MBoProcessor: P4oHard Disk: 40GB

Soft WareoOS: WindowsoFront End:

ApacheTomCat (Java)

oBack End: OracleoDesign Layout:

NetBeans.

Start

User Profile

Ratings

Product Profile

Match

Recommendations Generated Display “No RecommendationsFound”

YES NO

END

Flow Chart

User

Rating

User Profile

Product

ProductProfile

Recommender System Virtual UserGeneration

RecommendationsGenerated

Data Flow Diagram

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.

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.