Abstract

The distinctive webpage

recommendation for individuals is evident these days.

Web servers are

loaded with recommendation systems that analyse and recommend webpages for the

users. They use data implicitly obtained as a result of Web

browsing patterns of the users for recommending webpages.

The existing system

collects the Web logs and generates a cluster of

similar users and recommends pages to the user by actively analysing

it in online. However the time for analysing it in online is more. To optimize

this and increase the correctness of recommendation systems, a method

that applies Firefly based algorithm for recommending Web

pages along with Naive Bayes clustering is designed. User Web logs

are initially clustered in offline by using Naive Bayes clustering

technique. To find the similarity between the

active user queries with other users in the

cluster Firefly algorithm based similarity measure is used. The

proposed approach uses a probability based

clustering which eliminates the odd records while forming clusters.

Firefly algorithm meticulously searches the generated web

logs present in the cluster of the active user and recommends the top

pages. Firefly algorithm utilizes time efficiently, thus it is used for

processing in online. When pages are obtained, they are

ranked and the top pages that are more relevant to

the query are recommended.

The efficiency of the system can be evaluated

using measures like precision, recall-Score, Matthews’s correlation and

Fallout rate. The proposed approach is expected to improve time

utilization in online process as well as recommends

more accurate Webpages.

Introduction- Web

page recommendation system is a sub-domain of recommendation systems that

recommends a set of Web pages to the users based on their past browsing

patterns. It is done by applying special mining techniques on the data that are

previously gathered from the users which in turn discovers and extract

information from Web documents and services. The major concern is to find

reliable and efficient recommendation algorithms. Recommendation system

typically produces the result by following one of the two ways – through

collaborative and content based filtering.

A. Collaborative

Filtering

Most

recommendation system has wide use of collaborative filtering for recommending

items. This method lies on collecting and processing the information’s on

user’s behaviours or activities and then predicting the items relating to their

similarity with other users. Collaborative filtering approach builds a

structure from the users past behaviours and decisions of other similar users.

This model is used to predict user interested items. Since collaborative

filtering is independent of machine analysable contents, it is capable of

recommending for complex items accurately without “understanding” of the item

itself.

B. Content

Based Filtering

Content based

filtering is a widely used approach for designing recommendation systems. This

technique is lies on a definition of item along with a user’s preferred

profile. In a content based recommendation systems, the keywords are considered

as user’s interest. It utilize a series of distinct property of an item for

obtaining and recommending items with same properties. These approaches are

continually combined as Hybrid Recommendation Systems. These algorithms try to

recommend items based on examining the items that are liked by a user in the

past or in the present. In general, various items of candidate set are compared

with items that are rated by the user in the past and the best matching items

are recommended.

Literature survey

Recommendation

system has a major role of recommending personalized items for the users

based on their interest in a web services. The web

also contains a rich and dynamic information’s. The amount

of information on the web is growing dynamically,

as well as the number of web sites and webpages per web site. Predicting the behaviours

and needs of a web user has gained importance. Many webpage

recommendation system were developed in the

past, since they compute recommendations in

online process, their time utilization should

be efficient. A system 4 that uses support vector

machine (SVM) learning based model was

developed for computing similarity between two items

which performed better than latent

factor approach for group recommendations. Since the

matrix representation was followed, the

data sparsity problem was solved.

However, the system was not able

to stably scale when size of the group

dynamically increased.

Hybrid

recommender systems which combines more number of recommendation techniques was designed

5. It eliminates any weakness which exist when only

one recommender system is used. There are several ways in which the

systems can be combined, such as weighted hybrid

recommender in which the score

of a recommended item is computed from the results

of all of the available recommendation techniques present

in the system. However, data sparseness was still a problem, the system may

generate week recommendations if few users have rated the

same items and also the system doesn’t

overcome the cold start problem.

Hyperspectral

sensors can acquire hundreds of contiguous bands over a wide

electromagnetic spectrum for each pixel. To reduce computational

cost and eliminate an actual classifier within the band searching process, an

improved firefly algorithm based band selection method 8 was used. The

Firefly algorithm is an evolutionary optimization algorithm proposed

by Yang 13. After the initializations of parameters, the brightness is calculated

with the objective function .Then the moment states were

evaluated and the bands are selected. Firefly algorithm also had

a faster convergence even at the size of the

data is larger.

Further, to improve

the accuracy of similarity measure, firefly algorithm based similarity measures

are also introduced 10.It considered separate effects for ratings of

users with similar opinions and conflicting opinions. In order

to generate initial population of fireflies, half of population randomly

generated and the other half of population are randomly generated. Mean

absolute error was chosen as objective function to measure recommendation accuracy which

is obtained by difference between predicted rating and real rating.

An optimal

similarity measure via a simple linear combination of values and ratio of

ratings for user-based collaborative filtering provides better results. It

increased speed of finding nearest neighbours of active user and reduce

its computation time. Similarity function equation based on Firefly algorithm

was simpler than the equation used in traditional metrics

therefore, the proposed method provided recommendations faster than traditional

metrics.

Graph colouring problems are

generally discrete. Algorithms to discrete problems are

quite complex. A new algorithm based on Similarity and

discretize firefly algorithm directly without any other hybrid

algorithm was developed 11. It was adoptable to dynamic graph

sizes.

A system for

assigning an electronic document to one or more predefined categories

or classes based on its textual context and use of agglomerative

clustering algorithm was developed 6. This type of

clustering along with sample correlation coefficient as

similarity measure, allowed high indexing term space reduction factor with

a gain of higher classification accuracy.

In order to

minimize noise and outlier data, a modified DBSCALE algorithm using Naïve Bayes

has been designed 7. This algorithm is basically a prospect based

utility. This function is used to

estimate the outlier cluster

data and increase the correctness rate of algorithm on given

threshold value. Since Naïve Bayes is a probability based function,

it removes outlier cluster data and increases the correctness rate according to

threshold value. It also computes maximum posterior hypothesis for outlier

data. In order to minimize noise and outlier data, a modified DBSCALE algorithm

using Naïve Bayes has been designed 7. This algorithm is basically a prospect

based utility.

This function is

used to increase the correctness rate of algorithm on given

threshold value and to estimate the

outlier cluster data. Since Naïve Bayes is

a probability based function, it removes outlier cluster data and

increases the correctness rate according to

threshold value. It also computes maximum posterior

hypothesis for outlier data. The memory

based collaborative system uses matrix

based computation and solves data sparsity problem but, scalability

of the system cannot be stable when size of the group dynamically increases.

Hybrid system could be helpful in overcoming

the scalability issue but it again leads to cold start problem.

To

eliminate outliers as well as overcoming other two problems Naive Bayes clustering,

a probability based method was used in past.

Firefly algorithm has a faster convergence and searches all

possible subsets with better time utilization. Thus, to design an efficient

recommendation system, Naïve Bayes method can be followed for

clustering in offline. Since the time complexity should be

less, Firefly algorithm that is more

efficient in terms of time utilization, it can be used for

calculating similarity in online. Combination of these two technique

might increase the accuracy of the

recommendation system as well as results in efficient

time utilization.

III. Overview of the proposed work

Initially, the web log files are obtained from

the 1 America Online Inc. The log files consists of five

fields i.e. anonymous ID for individual user, query of each user along

with query time, list of URLs which user proceeded and its

rank in the result. These logs are collected

and grouped based on anonymous ID. The URL among all

the users are obtained and its content are downloaded and

processed. The processing of data includes removal of

stop words from the URL’s data and

keyword extraction. Similar users are clustered based on fetched

keywords by using Naïve Bayes clustering technique which provides efficient

clusters compared to clustering by the use of association rules. The created

clusters are given to online component. In online process, when an active user

gives a query, the keywords from the query is extracted. The

similarity between the extracted keywords with the other users

in the same cluster of the active user

is calculated using Firefly similarity measure. The

similarity values are sorted along with the web pages

browsed by similar users in the cluster. The top k web pages are

recommended for the active user

as a result.

IV. The proposed

work

The proposed

system follows a linear process of initially collecting the

web logs and processing them followed by clustering similar users

by Naïve Bayes clustering technique and finally generating

recommendations based on a similarity measure from firefly

algorithm.

A.

Pre-processing of Web Logs

The

web logs are collected form 1 AOL Inc. It consists of 20

million web queries from 650 thousand real users over 3

months. The data set includes anonymous ID, query, query

time, item rank and click URL. The log file contains

many number of users along with the web pages visited by

them. It is validated and separated based on anonymous ID. The user

is separated into individual file using anonymous ID. The content from

the URL are fetched and downloaded.

Those keywords are processed which undergoes stop

words removal and

stemming process. The final keywords are then

extracted. The features like keywords, Timings, Frequency, Click URL and

Revisit are fetched. The user profile is constructed using those

features. The user profile that constructed is based

on the features that are taken

form the user log files.

Timing: The timing

that the user spent on that particular URL

Frequency: The

amount of time the user visited the URL

Clickstream: The

number of click stream that are visited by user

Revisit:

Whether the user visited the web page

The keywords are

generated from the data fetched form the

URL. Timing for each URL is estimated from

the given date and time by calculating the difference

between the each URL that are searched in a single

day by having some time constraints. Frequency

is hence calculated such that number of times the user

clicked the URL. The clickstreams are those that are

clicked by the user for additional information. The timing

of revisit is calculated such that to decide whether the

user preferred it much or not. Keywords:

Keywords are those which are extracted from the URL.

The information from the URL is hence collected and processed to

obtain features of the user.

B.

Naïve Bayes Clustering

Clustering, also

known as unsupervised classification, is a descriptive task with many

applications. Clustering is decomposition or partition of a data set into

groups such that the object in one group are similar to

each other but as different as possible from the

object in other groups. Three main approach for clustering of data is partition

based clustering, hierarchical clustering and probabilistic model

based clustering. Probabilistic model based clustering is a

soft clustering were an object can be in many cluster

following a probability distribution. A clustering is useful if it produces

some interesting insight in the problem that we

are analysing. Naïve Bayes clustering is also a probabilistic clustering technique

that is based in Bayes theorem with strong independent

assumption between features. The feature variables can

be discrete or continuous. This probabilistic clustering lies on nominal and

numeric variables in the data set and its novelty lies in the use of mixture of

truncated exponential (MTE) densities to model the numeric variables. In Naïve

Bayes clustering the class is the only root variable and all

the attributes are conditionally independent given the class. The

clustering problem reduces to take a data set of instances

and a previously specified number of clusters (k), and work out

each cluster’s distribution and the population distribution between

the clusters. To obtain these parameters the expectation maximization (EM)

algorithm is used. Since Naïve Bayes clustering is

a probability based techniques. The items belongs to the

cluster if and only if it has a relation to it. This helps in

eliminating outlier data in the process of clustering. It also provides proper

clustering with less computations. The given dataset is divided into two parts,

one for the training and other for testing. For each

record in the test and train databases, the distribution of the class

variable is computed. According to the obtained distribution, a value for the

class variable is simulated and inserted in the corresponding cluster. The

log-likelihood of the new model is computed. If it is higher than the initial

model, the process is repeated. Otherwise, the process is stopped,

obtained clusters are returned.

C.

Optimisation Using Firefly Algorithm

Firefly

algorithm is an evolutionary algorithm that is based on the

behaviour of fireflies. Fireflies live in colonies and cooperate for the

survival of the colony. Generally, in order to model the behaviour of

fireflies, three assumptions will always be considered i.e. all fireflies are

homogeneous, Attractiveness of each firefly is related to its level of

brightness, rightness of firefly is determined with an exponential

objective function. Each firefly always emits a kind

of light that by which attracts other fireflies. The amount of accessed

light depends on parameters such as distance and absorption coefficient of the

surroundings. The longer the distance the lesser the amount of accessed light

will be. Also in surroundings with high light absorption coefficient such as

foggy weathers, the intensity of light decreases. The

certain issue is that every firefly regardless of its gender has

always been attracted to and moved toward the brighter firefly.

Firefly has a light intensity of its own. The key concept is, the firefly with

low light intensity is always attracted to the firefly with high light

intensity. This concept can be incorporated for calculating similarity. By

using firefly based similarity measure unique and distinguished results can be

obtained which is a useful feature for ranking. It can deal with highly non-

linear, multi-modal optimization problems naturally and

efficiently. It does not use velocities, and there

is no problem as that associated with velocity in PSO. The

speed of convergence is very high in probability of finding the global

optimized answer. It has the flexibility of integration with other optimization

techniques to form hybrid tools. It does not require a

good initial solution to start its

iteration process. Each web pages visited by

the user i are considered a firefly. The number of user visited the

particular page is assumed as the light intensity of the firefly. The objective

function is formulated based on the frequency and duration. Frequency is

calculated as the ratio to the number of visits per page to the average vests

of all pages.

The duration is

the ratio of duration of page to the total duration of all the pages visited by

the user. Thus, the objective function can be defined as in equation 5.1

Interest (i)= 2*Frequency (i)*Duration (i) Frequency (i)+Duration (i) (5.1)

The interest of all users in the cluster is calculated. Then the pages

to be recommended are found by using page rank algorithm 2 on the obtained

result. The results after applying page rank algorithm is given as the

recommended web page to the user.

D. Ranking the Web

Pages

The result, set of

web pages obtain should ranked in an order that the user might have higher

interest. Thus, they are

ranked in a sorted order based

on the interest of the active user. The association

rule checks the maximum possible combinations

which provides more accurate pages.

E.

Recommendation Process

The URL that are

to be recommended will be identified based on ranking and similarity measure.

The similarity measure is calculated among the users by comparing their similar

interest. From the obtained result of pages, page rank algorithm

is used to rank the most relevant pages to the user. Thus, resultant URL’s are

recommended to the users. Hence

the web page that is to be recommended to

the user will be more relevant. The use of Naive Bayes clustering

will eliminate the outliers and Firefly based similarity calculation will

check all the subsets of the clusters.