the design and implementation of feature

8/10/2019 The Design and Implementation of Feature

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The Design and Implementation of Feature-Grading

Recommendation System for E-Commerce


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Abstract

we present a novel approach named Feature-Grading which is a comprehensive algorithm used

to make recommendation of commodities in e-commerce business. It is a technique based on the

integration of feature mining, sentimental analysis, and the records of customer historical

behaviors. The overall process of Feature-Grading can be separated into 5 key steps: 1.Extracting

overall feature set of a group category of commodities; 2.Extracting modifier set and negative

words set; 3.Acquiring specific feature set and feature assessment set; 4.Acquiring specific

feature weight set; 5.Acquiring item weight set. After these 5 steps, we are able to grade and rank

all the items with an acquired grading equation. Then the needed as well as top ranking items can

be recommended. Moreover, we utilize the real information of mobiles and their reviews from

the famous e-commerce website Amazon.cn as our experimental data and discuss some

important results which reveal that the Feature-Grading really works well. At last, we also briefly

introduce the prototype recommendation system we developed on the basis of Feature-Grading.


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Introduction

the e-commerce, there are two major approaches for customers to meet items face-to-face. One is

called Customer-active which isachieved by customers themselves through some search

engines. The other way is accomplished by merchants with a kind of recommendation system to

recommend commodities. We call it Items-active. For Customer-active, what a customer

enters in search engines reveals what he/she wants. Existing search engines for commodities

utilizes the similar techniques as those for normal web pages which is based on key-words

matching, meaning that items saved in the database should be tagged with enough key words.

Most of such key words, however, are manually appended by merchants. This mechanism is very

low-efficient. It is easy to neglect some vital features as well. If there is a system which can

automatically mine out the key features, (i.e. the key words), of a group category of items, then it

is possible to complete the marking process with less manual operation so as to improve

comprehensive efficiency. This should be our first mission, since the mining of features not only

benefits the existing Customer-active searching approaches, but also acts as the fundamental of

our proposed recommendation algorithm.


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Aims and objectives:

Feature-Grading Recommendation System for E-Commerce In this system these

algorithms is that the challenges of providing recommendation in the domains where no

sufficient historical data exist for measuring similarity between products or users. We present

feature-based recommendation algorithms that overcome the limitations of the existing top-N

recommendation algorithms. The experimental evaluation of the proposed algorithms in the real

life data sets shows a great promise. The pilot project deploying the proposed feature-based

recommendation algorithms in the on-line retail web site shows 75% increase inthe

recommendation revenue .

SQL Injection

This approach is used for preventing the SQL Injection attack.

The new approach is made for SQLIA To track the SQL injection is based on signature

based method includes 3 modules to address security problem related to input validation.

SQL injection is an injection attack that exploits security vulnerability occurring in the database

layer of an application.

The objectives of the project are enhancing data mine capability of the audit module to analyze

user access and operation mode, optimizing every program module and guarding against

database intrusion operation for ensuring web database security operation.

Some sql Injection for examplesThe standard sql query format is:

Select * from table where UserName=ram and Password=ravi;

Malicious user inject the following sql injection in this field as

UserName: ram

Password : anything or 1=1

Select * from table where UserName=ram and Password=anything or 1=1;


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Literature survey

The explosive growth of the world-wide-web and the emergence of e-commerce has led to the

development of recommender systems . Recommender systems are personalized information

filtering technology used to either predict whether a particular user will like a particular item

(prediction problem) or to identify a set of N items that will be of interest to a certain user (top-N

recommendation problem). In recent years, recommender systems have been used in a number of

different applications such as recommending products a customer will most likely buy; movies,

TV programs, or music a user will find enjoyable; identifying web-pages that will be of interest;

or even suggesting alternate ways of searching for information. An excellent survey of different

recommender systems for various applications can be found in Over the years, various

approaches for building recommender systems have been developed that utilize either

demographic, content, or historical information . Among them, collaborative filtering (CF),

which relies on historical information, is probably the most successful and widely used technique

for building recommender systems. The first system to generate automated recommendations

was the GroupLens system it pro- vided users with personalized recommendations on Usenet

postings. The recommendations for each individual were obtained by identifying a neighborhood

of similar users and recommending the articles that this group of users found useful. Two

approaches have been developed for building CF-based top-N recommender systems. The first

approach, referred to as user-based , relies on the fact that each person belongs in a larger group

of similarly-behaving individuals. As a result, items (e.g., products, movies, books, etc.)

frequently purchased/liked by the various members of the group can be used to form the basis of

the recommended items. The second approach, known as model-based analyzes the historical

information to identify relations between the different items such that the purchase of an item (or


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a set of items) often leads to the purchase of another item (or a set of items),and then use these

relations to determine the recommended items.


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Problem statement

The explosive growth of the world-wide-web and the emergence of e-commerce has led to the

development of recommender systems [16]. Recommender systems are personalized information

filtering technology used to either predict whether a particular user will like a particular item

(prediction problem) or to identify a set of N items that will be of interest to a certain user (top-N

recommendation problem).


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Project Requirements

RReessoouurrcceess((HHaarrddwwaarree&&SSooffttwwaarreeUUsseedd))

Hard ware Specification:

Processor : Intel P-4 or Higher.

Processor Speed : 250 MHz to 833MHz.

RAM : 256MB.or above.

Hard Disk : 2GB to 30GB.

Key Board : 104 keys. or other


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Software Specification:

Operating System : Windows 98/XP/vista /seven.

Database : Microsoft Sql Server2005 .

Language : ASP.NET/C#


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Feature Grading Algorithm

Extract overall of

class

Extracting

negative word

set

Specific feature

set and feature

assessment

Acquiring specific

Feature Weight

set

Acquiring item

weight set

Recommendation


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DFD 1.0

Customer Feature

Set

Feature-

Grading

Recommendation


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Use Case Diagram:

Customer Feedback

Customer Feature Set

Find negative Word set

Recommendation

Acquiring Weigh Set


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Activity Diagram :

Customer Feedback

Customer Feature Set

Find negative Word set

Recommendation

Acquiring Weigh Set


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Conclusion

a recommendation algorithm called Feature-Grading and further introduced its corresponding

prototype system. We mainly focused on the design of 5 process of this algorithm and also

discussed some key results after experiments. These results revealed that the Feature-Grading

method works very well. It overcome some drawbacks of existing recommendation systems and

extended their ability as well. Our future efforts will be spent on the improvements of

sentimental analysis of reviews. We plan to expand the handle range from simple sentence to

compound sentence, including transitional sentence, comparative sentence, and imperative

sentence and so on.


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REFERENCES

1.

C. Anley. Advanced SQL Injection In SQL Server Applications. White paper, NextGeneration Security Software Ltd., 2002.

2. S. W. Boyd and A. D. Keromytis. SQLrand: Preventing SQL Injection Attacks. In Proc.of the 2nd Applied Cryptography andNetwork Security Conf. (ACNS 04), pages 292

302, Jun. 2004.

3. G. T. Buehrer, B. W. Weide, and P. A. G. Sivilotti. Using Parse Tree Validation toPrevent SQL Injection Attacks. In Proc. of the 5th Intl. Workshop on Software

Engineering and Middleware (SEM 05), pages 106113, Sep. 2005.

4.

W. R. Cook and S. Rai. Safe Query Objects: Statically Typed Objects as RemotelyExecutable Queries. In Proc. of the 27th Intl. Conference on Software Engineering (ICSE

2005), pages 97106, May 2005.

5. T. O. Foundation. Top ten most critical web application vulnerabilities, 2005. http:

//www.owasp.org/documentation/topten.html.

6. C. Gould, Z. Su, and P. Devanbu. JDBC Checker: A Static Analysis Tool for SQL/JDBC

Applications. In Proc. of the 26th Intl. Conference on Software Engineering (ICSE 04)

Formal Demos, pages 697698, May 2004.

7.

V. Haldar, D. Chandra, and M. Franz. Dynamic Taint Propagation for Java. In Proc. ofthe 21st Annual Computer Security Applications Conference, pages 303311, Dec. 2005.

8. W. G. Halfond and A. Orso. AMNESIA: Analysis and Monitoring for NEutralizing SQL-

Injection Attacks. In Proc. of the IEEE and ACM Intl. Conference on AutomatedSoftware Engineering (ASE 2005), pages 174183, Long Beach, CA, USA, Nov. 2005.

9. W. G. Halfond, J. Viegas, and A. Orso. A Classification of SQL-Injection Attacks andCountermeasures. In Proc. of the Intl. Symposium on Secure Software Engineering, Mar.

2006.

10.

M. Howard and D. LeBlanc. Writing Secure Code. Microsoft Press, Redmond,Washington, Second Edition, 2003.


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11.P. Resnick, N. Iacovous, M. Suchak, P. Bergstrom, and J. Riedl, GroupLens: An Open

Architecture for Collaborative Filtering of

Netnews, In Proceedings of CSCW94, Chapel Hill, NC.

[12] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl., Analysis of Recommendation Algorithms

for E-Commerce, In Proceedings of

ACM E-Commerce, 2000.

[3] W. Hill, L. Stead, M. Rosenstein, and G. Furnas, Recommending and Evaluating Choices in

a Virtual Community of Use, In Proceedings of CHI95.

14.2. WEBSITES

www.Google.com

www.wikepadia.com
http://www.google.com/http://www.google.com/http://www.wikepadia.com/http://www.wikepadia.com/http://www.wikepadia.com/http://www.google.com/

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