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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