INTERNATIONAL JOURNAL OF TECHNOLOGY AND COMPUTING (IJTC)

ISSN-2455-099X,

Volume 2, Issue 8 August 2016

IJTC201608006 www. ijtc.org 402

K-Nearest Neighbor (kNN) based Product Ranking

Model with Minimum Cost Evaluation

Manpreet Kaur1, Dr. Raman Chadha2 1Research Scholar, 2Professor and Head,

CGCTC, Jhanjeri, Mohali 1manu.ks90@gmail.com, Mohali, 2dr.ramanchadha@gmail.com

Abstract--The product ranking model is the model to create the ranking lists over the product listing data obtained from the e-

commerce portals. The product ranking models are utilized to produce the variety of product ranking lists in order to fulfill the

requirement to show the multiple lists over the e-commerce ranking lists. The e-commerce portals use the variety of lists to

increase the usability and the accessibility by creating the maximum visibility of the output results, which are shown for the

similar product ranking, products suggested by other users, most selling products, newly added products, etc. A perfect product

ranking model must be capable of producing the content-based and collaborative indexing over the input data. The content-based

product ranking models are utilized to produce the product ranking by evaluating the input query by the users. The collaborative

filtering is the algorithm used to create the product ranking lists according to the user’s profile and other similar users, friends,

etc. In this thesis, the proposed model has been designed by using the effective combination of the content-based and collaborative

filtering methods for the realization of the robust product ranking model.

Indexed Terms—Product ranking, e-commerce ranking, k-nearest neighbor, non-probabilistic classification

I. INTRODUCTION

E-commerce stands for Electronic commerce. It is that the

shopping for and commercialism of products and services

employing a network resembling web. E-commerce is

employed in on-line searching, bill payments, ebooks, e-

mail , etc. The businesses offer these services square

measure Amazon, flipkart, quikr, snapdeal, olacabs and

paytm. E-commerce is principally used as a result of it

reduces dealings value and improve level of client services.

It conjointly has worldwide market accessibility. Numerous

classes of e-commerce are: business-to-business, business-

to-customer, customer-to-customer or customer-to-business.

The ranking system is extremely helpful particularly in e-

commerce. A number of the advantages of ranking are:

It helps the shoppers to choose that choice to be chosen

among the multiple choices and in less quantity of your

time.

The personalised e-commerce ranking system has

redoubled the trust and loyalty relationship between

customers and web site homeowners.

Provides comparison between totally different

corporations and websites that increase competition

between the web site homeowners which ends up in

higher and improved services.

Ranking product may be a method to rank numerous

product. First off one product is graded then grouping of

product are performed and also the teams of product is

graded. Let's say let’s take a product Mobile phones, they

there are sorted consistent with their corporations like

Samsung, Nokia, Apple, HTC etc. And that they are sub

sorted consistent with their models and at every step

ranking is performed.

Semantic internet is employed to store knowledge within

the system while not the steering of human and might be

simply clear to humans in type of websites. In linguistics

internet knowledge stores are created on internet. It

develops the common framework that allows to share the

information among corporations, communities or

applications. The linguistics internet is amalgamated with

the ranking system and during this dynamic consolidation is

performed. In dynamic consolidation, if a brand new

product is entered in an exceedingly graded list then, it'll

mechanically generate the rank of the merchandise by

examination it with different product within the list and then

it updates the ranking.

II. LITERATURE REVIEW

Hepp, Martin et. Al. Has worked for the e-commerce

notably schema.org and good relations for researchers and

practitioners on the online of the info. Within the paper, the

author has given the introduction and first steerage on the

abstract structure of schema.org. They need created the

patterns for demand and possession that embody the range

of things like piece of furniture, apparels, natural

philosophy devices, cosmetics, books, etc. And have created

a full tool chain for manufacturing and overwhelming the

actual information. The author have conjointly mentioned

the subject like authentication (e.g. With webid), identity,

access control; information management problems from the

publisher and client perspective and micropayment services.

The drawback of this application is restricted to micro-data

that isn't applicable for e-commerce product ranking

system.

Sessoms, Matthew, and Kemafor Anyanwu has worked

model and algorithms for sanctionative a Package question

criterion on the linguistics net. The package question is that

the combination of multiple queries that helps to induce

resource combination on linguistics net. The taxonomic

category of such queries is “skyline package queries”. In

distinction to package queries on one relative models, the

RDF model have injected the challenge of deciding the

skyline package of ternary relations over multiple joins. The

various combination of latest operators for skyline package

queries relative question operators and RDF information

storage models have developed the four ways for analysis.

The author lacks within the use of further techniques for

optimization love prefetching additionally because the

integration of top-k techniques.

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INTERNATIONAL JOURNAL OF TECHNOLOGY AND COMPUTING (IJTC)

ISSN-2455-099X,

Volume 2, Issue 8 August 2016

IJTC201608006 www. ijtc.org 403

Malhotra, Dhairya et. Al has used the rear propagation

neural network on Intelligent net mining to upgrade website

rank. Due to the rise in data resources, the online is

developing at quicker rate. However its large size will

increase the difficulties throughout the analysis method in

extracting the desired data from net. This drawback are

often overcome by exploitation the personalised net search

however the user should provide his personal data to keep

up privacy. Within the paper, the author addresses all the on

top of mentioned problems by exploitation the rear

propagation neural network for implementing page ranking

method.

Mital, Monika et. Al. Have planned The integrative

framework within the context of e-procurement and ERP to

spot determinant of selection for saas. During this paper, the

author has tried to classify, determine and rank the size that

are influencing saas sourcing call. Exploitation extended

AHP (analytic hierarchy process) technique, the framework

is analyzed that helped in distinguishing quality and prices

so weights criteria are known exploitation the info that was

collected by eight users and nine service suppliers of saas

supported ERP and e-procurement.

III. EXPERIMENTAL DESIGN

The proposed model has been designed for the product

ranking and listings over the product data obtained from the

e-commerce portals. The multivariate ranking of the

products plays the vital role in the listing of the products.

The products are required to be listed in the multiple types

according to the requirement. The proposed model has been

primarily defined in the two major stages for the product

ranking according to the content based ranking and user’s

preference based ranking. The first phase of the ranking is

computed on the basis of the product properties and

popularity given in the form of product rating, manufacturer

rating, popularity and other similar factorization. The

proposed model evaluates the product entities on the basis

of the individual product properties as per given in the

database extracted from the online source. The proposed

model evaluates the product properties in the factorization

method called popularity, accessibility and trust (PAT)

assessment or evaluation. The proposed model design for

the content based ranking based upon the PAT features has

been enlisted in the following algorithm design:

Algorithm 1: The content based product ranking

1. Acquire the input data

2. Extract the popularity, access and trust features

for all products

3. Apply the averaging mean factor (AMF) over the

popularity, access and trust features for each

product entry

4. Add all of the AMF factors to create the

cumulative mean

5. Update the product list with the cumulative means

6. Apply the content ranking based upon the

cumulative mean value

7. Sort the product entries in the database according

to the cumulative mean value

8. Return the ranking list

Algorithm 2: Collaborative user-similarity based ranking

1. Acquire the product data with PAT features

2. Acquire the current user’s browsing history

3. Compute the weighted mean based upon the

browsing history

4. Construct the weighted mean vector from the

product ranking data

5. Acquire the all user mean vector database

(AUMVD)

6. Initiate the k-nearest neighbor model factors

7. Run the iteration for all entries in the AUMVD

a. Obtain the current user’s mean vector

b. Obtain the mean vector from AUMVD on

current index

c. Compute the squared distance between

the two vectors in (a) and (b)

d. Update the similarity data matrix (SDM)

e. Update the kNN classification result data

f. If the last entry on AUMVD

i. Return the iteration

8. Sort SDM in the descending order

9. Extract the N user list from the top

10. Acquire the overall weighted mean vector

(OWMV) from the top N users

11. Run the iteration for all entries in the product list

a. Obtain the current product vector

b. Obtain the OWMV

c. Compute the squared distance between

the two vectors in (a) and (b)

d. Update the Product Similarity Matrix

(ProSiM)

e. Update the kNN classification result data

f. If the last entry on product list

i. Return the iteration

12. Sort SDM in the descending order

13. Return the ProSim Matrix as the result

IV. RESULT ANALYSIS

The proposed model named PAT model, which has been

compared against the existing model of product ranking

based upon web parameters over the given e-commerce

product list in this simulation scenario with similar structure

and environment. The detailed analysis of the simulation

results has been conducted by analyzing the results obtained

from the existing and proposed model simulations. The

proposed model has been described efficient and effective

while evaluated on the basis of the projected resources and

entropy for the coalition of the network load and uniqueness

respectively. Minimum of the 10 percent improvement has

been observed in the favor of the proposed model when

compared on the basis of various performance parameters in

the variety of simulation scenarios.

The adverse situations have been analyzed theoretically and

the effective in the results has been observed in the

proposed model to mitigate the errors and repetitions with

the new product ranking model than the existing model. The

newly proposed scheme has been primarily evaluated for

the utilization of the resources while using the ranking

model the given product data in the simulation. The

utilization of the resources, measured in the form of

projected resources has been recorded significantly lower

than existing model, which clearly indicates the robustness

of the proposed model. The following figure 1 indicates the

robust performance of the proposed model.

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INTERNATIONAL JOURNAL OF TECHNOLOGY AND COMPUTING (IJTC)

ISSN-2455-099X,

Volume 2, Issue 8 August 2016

IJTC201608006 www. ijtc.org 404

Figure 1: Projected Resources comparison between existing and proposed model

Additionally, the entropy parameter signifies the higher

uniqueness level in the ranking data using the proposed

model than the existing model. The proposed model

evaluation over the entropy has been described in the

following figure 2. The high uniqueness of the product table

data increases the additional robustness to the ranking

models associated with the arrangement of the entries in the

perfect form over the given set of data. The result of the

proposed model has been observed nearly double than the

existing model, which can be clearly indicated by the

following figure:

Figure 2: Entropy comparison between existing and proposed models

V. CONCLUSION

The individual entity relationship based similarity

evaluation has been utilized for the content based filtering,

which utilizes the popularity, accessibility and trust (PAT)

factors based content filtering algorithm. The PAT features

have added the flexible and robust content-filtering model.

The k-nearest neighbor classification model has been

utilized for the collaborative filtering model for the

evaluation of the similarity of the other users, when the new

users are enlisted over the e-commerce engines. The

historical data of the existing users is evaluated using the

averaging factors to compute the first level test vector,

ExistingPropose

dExisting

Proposed

ExistingPropose

dExisting

Proposed

ExistingPropose

d

S1:E100 S2:E500 S3:E1000 S4:E2000 S5:E5000

Average 2.6499 1.5469 13.3108 7.7344 26.4594 15.4688 52.7602 30.9375 110.9954 77.3438

Minimum 1.8221 1.0977 7.8656 5.4883 15.0027 11.25 35.7773 21.9531 80.7805 54.8828

Maximum 3.7667 1.9648 19.7488 9.8242 38.6813 19.6484 77.2214 39.2969 160.2389 98.2422

0

20

40

60

80

100

120

140

160

180

Average Minimum Maximum

Existing Proposed Existing Proposed Existing Proposed Existing Proposed Existing Proposed

S1:E100 S2:E500 S3:E1000 S4:E2000 S5:E5000

Average 2.0207 2.4678 2.0131 2.6344 2.0712 2.7662 1.9829 2.5944 2.1466 2.5038

Minimum 1.7632 2.0544 1.609 2.155 1.7707 1.991 1.5884 1.9055 1.926 1.9444

Maximum 2.2706 2.9963 2.4393 3.1638 2.3884 3.2897 2.3225 3.0931 2.2644 3.1026

0

0.5

1

1.5

2

2.5

3

3.5

Average Minimum Maximum

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INTERNATIONAL JOURNAL OF TECHNOLOGY AND COMPUTING (IJTC)

ISSN-2455-099X,

Volume 2, Issue 8 August 2016

IJTC201608006 www. ijtc.org 405

which is further classified with the k-nearest neighbor

model for the production of the collaborative filtering. The

proposed model results have been evaluated in the form of

various performance parameters associated with the time

complexity, duplicate entry evaluation and resource usage.

The proposed model has outperformed the existing model

on the basis of all of the above listed parameters during the

performance evaluation phase

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