Mining
Web Graphs for Recommendations
CONTENTS
Title Page
No.
- Introduction
1.1 Overview of the System
- Abstract
- Description of the Problem
3.1 Existing System
3.2 Proposed System
3.3 System Environment
3.4
System Requirement
4.
System
Analysis
4.1System Description 4.2 Data Flow Diagram
- System Design
5.1
Input Design
5.2
Output Design
5.3 Code
Design
6. Development of System and Testing
6.1 System Maintenance
6.2 System testing
7. Implementation
8. Conclusions
Bibliography
Appendix
1. Introduction
INTRODUCTION
With
the diverse and explosive growth of Web information, how to organize and
utilize the information effectively and efficiently has become more and more
critical. This is especially important for Web 2.0 related applications since
usergenerated information is more free-style and less structured, which
increases the difficulties in mining useful information from these data
sources. In order to satisfy the information needs of Web users and improve the
user experience in many Web applications, Recommender Systems, have been well
studied in academia and widely deployed in industry. Typically, recommender systems
are based on Collaborative Filtering [14], [22], [25], [41], [46], [49], which
is a technique that automatically predicts the interest of an active user by
collecting rating information from other similar users or items. The underlying
assumption of collaborative filtering is that the active user will prefer those
items which other similar users prefer [38]. Based on this simple but effective
intuition, collaborative filtering has been widely employed in some large,
well-known commercial systems, including product recommendation at Amazon1,
movie recommendation at Netflix2, etc. Typical collaborative filtering
algorithms require a user-item rating matrix which contains user-specific
rating preferences to infer users’ characteristics. However, in most of the
cases, rating data are always unavailable since information on the Web is less
structured and more diverse.
2. Abstract
Abstract:
As the exponential explosion of various
contents generated on the Web, Recommendation techniques have become increasingly
indispensable. Innumerable different kinds of recommendations are made on the
Web every day, including music, images, books recommendations, query
suggestions, etc. No matter what types of data sources are used for the
recommendations, essentially these data sources can be modeled in the form of
graphs. In this paper, aiming at providing a general framework on mining Web
graphs for recommendations, (1) we first propose a novel diffusion method which
propagates similarities between different recommendations; (2) then we
illustrate how to generalize different recommendation problems into our graph
diffusion framework. The proposed framework can be utilized in many
recommendation tasks on the World Wide Web, including query suggestions, image
recommendations, etc. The experimental analysis on large datasets shows the
promising future of our work.
Description of the Problem
Existing System:
The last
challenge is that it is time-consuming and inefficient to design different
recommendation algorithms for different recommendation tasks. Actually, most of
these recommendation problems have some common features, where a general
framework is needed to unify the recommendation tasks on the Web. Moreover, most of existing methods are complicated
and require tuning a large number of parameters.
Disadvantages:
It is becoming increasingly
harder to find relevant content and what user recommends the actual thing.
Proposed System:
In order to satisfy the
information needs of Web users and improve the user experience in many Web
applications, Recommender Systems. This is a technique that
automatically predicts the interest of an active user by collecting rating
information from other similar users or items. The underlying assumption of
collaborative filtering is that the active user will prefer those items which
other
Similar
users prefer the proposed method consists of two stages: generating candidate queries
and determining “generalization/specialization” relations between these queries
in a hierarchy. The method initially relies on a small set of linguistically
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.
Advantages:
(1) It is a general method,
which can be utilized to many recommendation tasks on the Web.
(2) It can provide latent
semantically relevant results to the original information need.
(3) This model provides a natural treatment for personalized
recommendations.
(4) The designed recommendation algorithm is scalable to very large
datasets.
3.4 SYSTEM
REQUIREMENTS
Hardware Requirements
Processor : Pentium
III / IV
Hard Disk : 40
GB
Ram : 256 MB
Monitor : 15VGA Color
Mouse : Ball / Optical
Keyboard : 102
Keys
Software Requirements
Operating System : Windows XP professional
Front End : Microsoft Visual Studio .Net 2005
Technology : ASP.net
Language : Visual C#.Net
Back End : SQL Server 2000
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