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Recommendation
system plays a vital role in 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 rapidly, as well as the number of web sites and
webpages per web site. Predicting the needs of a web user as she visits web sites 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 that combines
two or more 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 where
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 ofcontiguous bands
over a wide electromagnetic spectrum for each pixel. The
rich spectral information allows for distinguishing
materials with subtle spectral discrepancy, but
it usually leads to the “curse of
dimensionality”. To address this, an improved firefly algorithm based band
selection method 8 was used.

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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 (2.1), where t is the maximum iterations, ? is
the step size and ? is the light absorbance of m number of
fireflies. The moment states are then evaluated and the bands are selected. In
order to avoid employing an actual classifier within the band searching process
to greatly reduce computational cost, criterion functions that can gauge class
separability are preferred which provided better results. Firefly algorithm
also had a faster convergence even at the size of the
data is larger To improve the accuracy of similarity measure, a nature
inspired algorithm which is based in the behaviour of
Fireflies wereintroduced 10.We consider 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
basedon 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.                               

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