Web Graphs

7/31/2019 Web Graphs

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By

B.Vandana Reddy

B.Revathi

Ch.RadhikaMadhavi

Project Guide: Mr. Pranay


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CONTENTS

Introduction

Modules

Existing System

Drawbacks of the existing system Proposed System

Architecture

System Requirements

Advantages of the new system Reference


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Modules

1. Posting the opinion

2. Image Recommendation Technique

3. Rating Prediction

4. Ranking Approach

5. Collaborative Filtering

6. Query Suggestion


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Introduction

There is a huge explosion of various contents

generated on the Web in the present run and

recommendationtechniques have becomeincreasingly indispensable to select the best one.

Innumerable different kinds of recommendations

are made on the Web every day, including imagesrecommendations, query suggestions, etc. which

can be modeled in the form of graphs


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We first propose a novel diffusion methodwhich propagates similarities between differentrecommendations

We then illustrate how to generalize differentrecommendation problems into our graphdiffusion framework.

The proposed framework can be utilized inmany recommendation tasks on the WorldWide Web, including query suggestions,image recommendations, etc.

Providing the Web user with an ease ofselection and a ranking graph is the goal of thesystem.


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Existing System

In present mining system, we have differentrecommendation algorithms for differentrecommendation tasks.

But actually, most of these recommendation problemshave some common features, where a generalframework is needed to unify the recommendationtasks on the Web.

Moreover, most of the existing methods arecomplicated and require tuning a large number ofparameters.


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Disadvantages

It is becoming increasingly harder to find therelevant content and also which isrecommended by the users.

Designing different recommendationalgorithms for different recommendation tasksis tedious and inefficient due to their similarimplementation.


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Proposed System

Recommender Systems, a technique thatautomatically predicts the interest of anactive user by collecting rating informationfrom other similar users or items is used.

The proposed method consists of twostages: generating candidate queries and

determining generalization/specializationrelations between these queries in ahierarchy.


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The method initially relies on a small set oflinguistically motivated extraction patterns

applied to each entry from the query logs,

then employs a series of Web-based

precision-enhancement filters to refine and

rank the candidate attributes.


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Architecture


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Advantages

It is a general method, which can be utilizedto many recommendation tasks on the Web.

It can provide latent semantically relevantresults to the original information needed.

The designed recommendation algorithm isscalable to very large datasets.


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System Requirements

HARDWARE REQUIREMENTS:

System : Pentium IV 2.4 GHz

Hard Disk : 40GBRam : 512 MB

SOFTWARE REQUIREMENTS:

Microsoft visual studio 2008

SQL server 2005


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References

The Journal by H. King, I. Lyu,

The Chinese University of Hong Kong

Web Mining and Recommendation

Systems by Guandong and Amit

Prabhakar


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Web Graphs

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Transcript of Web Graphs