Recommendations based on Capability in E-Learning Systems ising abc with pso to Improve Learning Performance

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Recommendations based on Capability in E-Learning Systems ising abc with pso to Improve Learning Performance

Maganti Venkatesh¹ , Dr. S Sathyalakshmi²

1Research Scholar, Dept. of CSE, Hindustan Institute of Technology & Science, Chennai

2Professor, Dept. of CSE, Hindustan Institute of Technology & Science, Chennai

Abstract:

Web-based learning is often called online learning or e-learning because it includes online course content. Searching for patterns of Web access, web access patterns, web structures, and web content dynamics is known as Web Mining. The recommender system given for personalized recommendations is on a set of objects and their utility to a certain domain which begins from the available information on objects and users. One of the most popular analysis of data is Clustering which divides data into relevant clusters. In this work, a Memetic Swarm Clustering (MSC) is proposed, which is based on Artificial Bee Colony (ABC) along with the Particle Swarm Optimization (PSO) and K-Means algorithm. It makes use of the algorithm K-means clustering for categorizing the users on the basis of their interests. The results of the experiment proved that the proposed MSC method improved performance.

Keywords: Web Mining, Recommender system, K Means Clustering, Greedy Search Clustering in Recommender System (GSCRS), Artificial Bee Colony (ABC) Algorithm, and Particle Swarm Optimization (PSO) Algorithm.

___________________________________________________________________________

1. INTRODUCTION

With rapid advances in educational technologies and web-based service, e-learning is gaining popularity moving beyond traditional correspondence methods[1]. Discussion forums via email, videoconferencing, and live lectures (video streaming) are all possible through the web. Web-based courses or e-learning may also provide static pages such as printed course materials. One of the values of using the Internet to access course materials is that web pages

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may contain hyperlinks to other parts of the web, thus enabling access to a vast amount of web-based information.

The rapid development of information technology and the fast growth of the Internet have facilitated an explosion of information which has accentuated the information overload problem. For effective e-learning the content should be designed such that the user’s learning experience engages with the knowledge of the subject taught. In recent years, recommender systems have proven to be an effective technique to deal with this problem and become extremely common in a variety of applications. They predict users’ potential future likes and interests by using users’ past preferences data. The quality of the results of a recommender system is determined mainly by the recommendation algorithms it adopts. Designing an excellent algorithm is crucial to the performance of a recommender system [2].

Accordingly, various kinds of recommendation algorithms have been proposed, including Collaborative Filtering (CF), content-based filtering, and hybrid recommendation approach.

CF is the most popular information filtering technique which usually works by searching a large group of users and finding a smaller set with tastes similar to the target user. Content- based filtering method tries to recommend items to the active user similar to those rated positively in the past. It is based on the correlation between the content of the objects and the users' preferences. Hybrid recommendation approach is usually used to solve the cold-start problem, by combining collaborative content data in such a way that even a new object that has never been rated before can be recommended.

Recommendation system can efficiently make a personalized recommendation with the growth of data. And some merchants use this system to save time and to provide convenient services for customers [3]. Some well-known e-commerce sites from home and abroad like Taobao, Weipinhui, Jingdong, etc., all use personalized recommendation algorithms on their websites to provide personalized recommendation services for customers. The facts prove that to be a success. However, with the development of E-commerce and the increasingly complicated user context, how to fulfill personalized needs has become a new trend in personalized recommendation service studies. Personalized recommendations for an individual user are then obtained by using the user's past preferences as “sources” in a given network and propagating them to yet unevaluated objects.

Recommender systems often combine the characteristics of using the collective traits of different category of learners, suggest preferences based on these traits, and give the feeling

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of being in control, to the learner. The Learning process is almost learner-driven in this instance. However, suitable guidance is also provided at various points. These attributes help the recommender systems to have features that have the potential to leverage the learning processes [4]. There is an understandable difference in the way different learners accept instruction and how they assimilate the acquired knowledge to benefit from similar types of instruction [5]. It is understandable that instructional material should be prepared and be adaptable to match the abilities of learners, their learning styles and knowledge

This work proposes Memetic Swarm Clustering (MSC) based on the Personalized Bee Recommender for e-learning (PBReL), our previous investigation, for a personalized recommendation system to improve the web learning performance. The PBReL is based on Artificial Bee Colony (ABC) optimization which builds a structure of recommendation by using K-Means clustering. The rest of the investigation has been organized: Section 2 discusses the similar work available in the literature. Section 3 explains the methods used.

Section 4 explains the experimental results, and Section 5 concludes this work.

2. LITERATURE SURVEY

The massive growth of the web has now influenced all e-learning resources. The focus of this work was devising a recommendation system to address the problems in cold start providing a diverse recommendation for the learner. Anandakumar et al. [6] had made a proposal for an Improved Neighborhood-based Collaborative filtering along with the Hybrid Genetic algorithm and the Particle Swarm Optimization (PSO), which was duly implemented.

The techniques had been employed to improve diversity and convergence. The results of this proved that the proposed method could outperform all the current algorithms by standards of measures of accuracy and were also able to alleviate the problems of sparsity and cold-start to generate a list of recommendation that was diverse.

Djellali et al. [7] had investigated a new method of feature selection that employed two different hybrid approaches that were based on the Artificial Bee Colony along with the Particle Swarm Optimization (ABC-PSO) and the ABC that had a Genetic Algorithm (ABC- GA). For achieving a proper balance between exploitation and exploration, there was a novel improvement that had been integrated into the ABC algorithm. For this work, the PSO contributed to the ABC in the phase of the employed bees, and the operators of the GA mutation are applied in the Scout and the Onlooker phase. This hybrid ABC-GA method was found to be very competitive compared to the other existing methods such as the ABC, the

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PSO, and the GA. In the case of minimal features that classify the datasets of lung cancer, the DLBCL, hepatitis, colon, and the WDBC this was very useful. The results were carried out, and it showed the mutations operators to be effective in their accuracy and the Particle Swarm for their small feature size.

Tag clustering was found to be very helpful in searching and also in arranging all Web 2.0 resources and was also vital to achieving systems of social tagging. Kumar et al., [8] applied these techniques of clustering to a social e-learning tagging system (http://www.pumrpelearning.com); it also proposed a new hybrid Tolerance Rough Set-based PSO (TRS-PSO) used for the clustering tags. In this stage, the technique proposed had been contrasted using an algorithm of benchmark clustering K-means along with a method or grouping that was based on the PSO. There was some exploratory investigation that had represented the traits of this methodology, which is suggested.

Kanungo et al., [9] had also developed a new population-based metaheuristic known as the Elitist based Teaching Learning Based Optimization (ETLBO) used in data clustering. This was hybridized using the K-means algorithm (ETLBO-K-means) for obtaining optimal cluster centers with effective values of fitness. The proposed method's performance was compared to that of the other techniques with real-life datasets and synthetic datasets as their benchmark. Simulation with comparison was made to prove the effectiveness and also the efficiency of this method.

Kuo et al., [10] had further presented another Dynamic Clustering (DC) approach that was based on the PSO and the Immune Genetic (IG) (DCPIG) algorithm, that could cluster data into suitable clusters by means of data characteristics using a set of clusters that were pre- specified. The DCPIG algorithm which was proposed had been compared to the other DC algorithms by making use of the Vowel, the Glass, the Iris, and the Wine benchmark data sets. The results of the experiment proved that the DCPIG was able to achieve better accuracy and stability compared to the other algorithms. It could also be applied to the real-world problems that consider the clustering of customers for the cyber-flower shop. Finally, it had recommended various other products, as well as services to the customers based on the results of clustering.

Ke et al., [11] had proposed a new intelligent system that made use of density-based clustering along with the Genetic Algorithm (GA) for recommending Point-of-Interests (POIs) solution used in planning tourism. These density-based clustering will identify

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candidates for the POIs. A Skyline method will then decide the superior POI from that of a candidate POI utilizing several attributes. The GA further optimizes a recommendation solution. For this, the contribution was to get a new tourism POI solution from various candidate POIs that were based on the preferences of users or tourists. There was a new implementation of an experimental system which was in progress. This was used for the opening of data from the Google map in the future, which was proof that the new system and its mechanism was effective.

Although there were many clustering mechanisms traditionally used for purposes of clustering in the current work, using bio-inspired techniques had to be explored to generate some optimal recommendations. A novel clustering ensemble that was bio-inspired had been introduced by Logesh et al., [12] which had swarm intelligence with models of fuzzy clustering. This recommendation was evaluated based on real-world datasets that were large in the number using Yelp along with TripAdvisor for the accuracy of recommendation along with the stability by means of metrics of standard evaluation. The results obtained had illustrated the performance that was advantageous compared to the peer works in recent times.

Khribi et al., [13] presented an on-line automatic recommendation system for active learners based on their recent navigation history. The mechanism exploits similarities and dissimilarities among user preferences and content of the learning resources for personalizing the recommendation. In the initial stage, the users profile are mined and then the content used by the user for learning. Based on these profiles, the algorithm uses Content & Collaborative filtering to recommend relevant links to the active learner.

3. METHODOLOGY

Clustering is a problem that is well-known in engineering and statistics which helps in learning the manner in which vectors are arranged (measurements) into groups (clusters).

Clustering is very important as an application area in various fields such as vector quantization, statistical analysis of data, and data mining. This K-means algorithm has been based on a very simple observation of optimal placement, which is in the center of the centroid of its associated cluster. The attractiveness of K-means is that it was flexible and straightforward. In spite of the fact that the other algorithms were available, the K-means still remains attractive owing to the property of convergence [14].

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The greedy algorithm, Greedy Search Clustering in Recommender System (GSCRS), is the one which constructs the object X step by step selecting the option which is locally best.

In the case of a greedy-based clustering, the random point is chosen, which will be its first cluster centre. By finding the point which is the farthest from the cluster centre will add this.

This will be repeated until it has k centres. For this section, the PSO-based K-means clustering, the PBReL and proposed MSC were the methods discussed.

3.1 Particle Swarm Optimization (PSO) based K-Means Clustering

The Particle Swarm Optimization (PSO) had been inspired by the social behaviour of the ﬂocks of birds. This feature selection has in itself an intrinsic discrete binary space of search.

There was an original binary BPSO that had been presented by Kennedy and Eberhart in order to permit the PSO in operating the binary problem spaces. For this version, the particles will fly within a binary search space which takes its values of 0 or 1 for position vectors. The PSO method further presented the advantage of a high rate of convergence with a low cost of computation. The PSO only adjusts for a few of the parameters and is also very efficient. The PSO had a better ability of global optimization with a lower time of computation compared to the GA, which was used widely in the process of feature selection. However, falling within the local optimum has made it the main drawback in employing the Particle Swarms [6, 15].

A Supervised K-means approach is a combination of Particle Swarm Optimization with the traditional K-means algorithm. The fitness of cluster weights was then obtained employing running K-means that had a corresponding Euclidean metric with the grouping of the data records for a particular class label which was based on its distance measure. While using the PSO, the particle's position will be the aggregate of its center position. With a dataset

X  { , ,...., x x

¹ ²

x

_N

}

, wherein the

x

ⁱ

denotes a new data pattern within an m- dimensional feature space, every particle will be of a dimensionK m , K will be the cluster numbers used to partition a data record X.

Algorithm of PSO based K-means (PSO-KM) algorithm

Step 1: Initializing of a population of all particles having small random positions, X^p and the velocitiesV^p

, of a pth particle on the problem space of the K m dimension. Here the

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position of every particle ( X^p

) will correspond to all the cluster centers of size K m _{and the} velocity which represents the actual rate of change found in the position of the particle [16].

Step 2: Initializing of the parameters of the PSO, such as the various learning factors (

c c

¹

,

²

) and the inertia weight (

w

^max

, w

^min

).

Step 3: Start its iterative procedure and further set its iteration count as t =1.

Step 4: Run the steps below in a K-means algorithm for each particle.

 Calculation of the measure of Euclidean distance, d_pi, between the pth cluster center (or particle) and the i^th data point.

 Assigning of every data object xi to its nearest cluster centreX^p .

Step 5: Once the grouping of data objects which is based on a criterion of minimum distance, the fitness function is evaluated, and this denotes the classification accuracy and its maximization as per Equation (1):

. of samples classified correctly Function=

no.of samples in data set Fitness No

Total ₍₁₎

Step 6: Comparing the evaluation with the previous best value of the particle, which is the best value, the

P

^best

, based on the fitness value. In case the current position (the cluster center location) will be better than the pbest, after which the current position is assigned to the

P

^best

, and if not retain the pbest at its previous value. The process will be carried out for every particle found in the population.

Step 7: Once the updating is complete

P

^best

, choose its best ﬁt value (that has the maximum ﬁtness value) which is among particles as in

P

^best

and then assign to be

G

^best

. The

G

^best

will be one single particle in dimension K m , where K is the number of all clusters identiﬁed in the partitioning of the database.

Step 8: The position and velocity of every particle will be updated by using equations (2) and (3) as shown below:

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( 1) ( ) ( ) ( ) ( ) ( )

1 1 2 2

( 1) ( ) ( 1)

* ( ) ( )

t t t t t t

p p p p p

t t t

p p p

V w V c rand Pbest X c rand Gbest X

X X V



 

    

 

(2&3)

All constants

c

¹

and

c

²

that are termed as the learning or acceleration factors are the weighing of various stochastic terms which pull every particle to the

P

^best

and the

G

^best

positions. A typical value of that of

c

¹

and

c

²

are taken to be 2.0, rand1, which is a random number that is generated between the values 0 and 1. There is an inertia weight (w) which decreases linearly and has been implemented by beginning at

W

^max

=0.9 and by ending at

W

^min

=0.4.This further assists in expanding search space to ensure every particle scan is able to explore newer areas, thus indicating a global search. An inertia weight w will be computed for every iteration as per Equation (4):

max min

max . ,

. no. of Iteration

W W

W W Iter count t

Max

   

(4)

Step 9: Now check for the criterion of convergence that can be an ideal fitness value or the maximum number of the iterations. In case it is converged, return

G

^best

as its optimal cluster centers or increment its iteration count t = t + 1 and then loop it to Step 4.

Some advantages of PSO are: the training speed is fast, finding an optimal solution is high, and the algorithm is simple. Some advantages of PSO are: it is easy to fall into local optimal solutions and poor handling of discrete optimization problems.

3.2 Proposed Personalized Bee Recommender for e-learning (PBReL)

The Artificial Bee Colony Algorithm had been put forward in the year 2005 by Karaboga [17] in Turkey. This simulates all artificial bees to identify the best source of nectar using swarm intelligence. In the case of the ABC, each solution will reflect another independent artificial bee, and each numerical value for the target function will be equivalent to the source of the nectar. As in the case of the other swarm intelligence, the artificial intelligence of the bees will be dependent on their cooperation with one another. Information exchange in the artificial bee colony has a critical role to play in cooperation. This type of exchange will be accomplished either by an odour or a waggle dance.

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An ABK algorithm will stand for the ABC with the K-means algorithm [18]. ABC is well known for the techniques of optimization, whereas the K-means is well-known for the efficiency of clustering. Thus, developing a new method that combines the effectiveness of both of these algorithms is very tedious. This ABC algorithm has three different phases. The employed bee phase, onlooker bee phase, and finally, the scout bee phase. From among the three, the employed and the onlooker phases are inevitable, and the scouting phase is random.

There is a new solution from the K-means that has been generated based on the employed bee solutions for the problem of optimization. An addition of one more solution from the K- means on completion of each cycle can enhance the ABC algorithm and its reach to the next level. The proposed approach will define a new dataset D for the processing. The main area which requires more concentration, which is the fitness function for this algorithm [19].

For this purpose, the data points were grouped into two different clusters in accordance with the least distance value. For this, the

f

_ivalues of clusters were computed from the data points and their distance values. A fitness function is then calculated as per the sum of distance_ivalues

0

tan min( , ) tan

1 , if f 0 1

1 ( ), if f 0

i i i

n

i i

i

i i

dis ce i j

f dis ce

fitness f

abs f



 

  

  





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For this, fitness will be calculated for the data, and this is given as the input. The fitness is then computed to find the relevant data from its subsequent population. The solution that has the best fitness will then be retained while others are rejected.

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The content-based (CB) and collaborative filtering (CF) is used for obtaining recommendations. CB filtering is applied for looking out of functionalities of a Nutch search engine. A term vector of its search engine for computing the links of recommendation is built and is ranked based on cosine similarity of content. Then the CF by means of sliding window pages to the clusters for classifying an active learner in the learner’s group is used. On completing CF, Association Rules (ARs) belonging to a corresponding group is used. Rules formed are sorted by their confidence thus improving the quality of recommendation and generating the objects to the learners.

3.3 Proposed Memetic Swarm Clustering (MSC)

Some advantages of ABC are: the global search ability is strong, the convergence speed is fast. Some disadvantages of ABC are: it is easy to fall into the local optimum, the search speed slows down later. The hybridization of ABC-PSO was anticipated for proposed Memetic Swarm Clustering (MSC) to stimulate the behavior that was competitive between the PSO particles and the onlooker bees. Firstly, the initial employed bees will be copied into the particles of the swarm PSO along with their fitness. Next, the employed bees and the particles will cooperate, and every solution gets evaluated and then compared. The flowchart of the proposed Memetic Swarm Clustering (MSC)is shown in Figure 1.

Pseudocode of PBReL 1. Initialize dataset

2. Apply random selection for employed bees 3. Calculate Distance matrix

4. Find the fitness

5. Start Onlooker bee phase.

6. Find a new solution for onlookers using GSCRS 7. Find fitness of onlooker bee

8. Select the bee with Least fit value

9. Apply K-means operator in the selected bee to find Scout bee 10. Add Scout bee into Employed bee

11. Repeat Step 2–Step 10

12. Determine all optimal centre points 13. End

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Figure 1 Flowchart of Proposed Memetic Swarm Clustering (MSC)

Initialize the Hybrid PSO and ABC which are initial center points of K Means

Determine gbest of the PSO and best of the ABC

Apply the recombination procedure to the gbest and best solution

K-means algorithm for each particle

Update the Velocity &Position of the Particles

Determine personal best of the Particles

Determine gbest of the population (best centroids)

Employed bee phase of ABC

Onlooker bee phase of ABC

Apply K-means operator in the selected bee to find Scout bee

Determine best of the population (best centroids)

Report the Best Result Is the termination condition met?

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Now the best is chosen to be the gbest in the PSO, and the onlooker bee and its neighbor was taken for the ABC [20]. For obtaining the best, the gbest fitness value and the best solution for the ABC were computed. The choice for both solutions had been calculated by employing fitness values. For the proposed MSC, a relation between the PSO and the ABC was made. The relationship between both the algorithms was the emergence of another parameter known as “the best”. The variable further contributes towards the improvement of the ability of exploitation by means of a discrete use of its global information and improves the ability of the PSO in disposing of the local minima.

4. RESULTS AND DISCUSSION

For this work, the recommendation threshold @ support = 0.05 has been used. For the purpose of this section, both precision and coverage have been shown in figure 2 to 9 for various window size. Table 1 and 2 shows the parameters of PSO and ABC, respectively.

Table 3 to 8 shows the precision and coverage values.

Table 1: Parameters of PSO

Parameter Value

Population Size 100

Maximum No. of Iteration 20

Inertia Weight Factor Wmax=0.9, Wmin=0.4

Acceleration Constant C1 = 2 and C2 = 2

Error Gradient 1e-06

Table 2: Parameters of ABC

Parameters Values

Number of Bees 200

Maximum number of cycles (MCN) 500

Number of Iterations for Onlooker Bees 200

Number of Food Sources 25

Random Scouts 1

Upper bound 10

Lower bound -10

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Table 3: Precision for Window Size= 1 Recommendation

Threshold @ Support = 0.05

GSCRS PBReL PSO-KM Proposed

MSC

0.1 0.45 0.5 0.51 0.52

0.2 0.5 0.55 0.55 0.57

0.3 0.57 0.63 0.63 0.66

0.4 0.63 0.69 0.69 0.73

0.5 0.66 0.73 0.73 0.76

0.6 0.67 0.73 0.74 0.77

0.7 0.7 0.77 0.77 0.81

0.8 0.74 0.81 0.81 0.85

0.9 0.73 0.82 0.81 0.86

1 0.76 0.84 0.85 0.9

Figure 2 Precision for Window Size= 1

From the figure 2, It is observed that with window size=1, the proposed MSC has shown higher precision than GSCRS, PBReL and PSO-KM by 14.4%, 3.92% and 1.94%

respectively for recommendation threshold 0.1. It is observed that with window size=1, the proposed MSC has shown higher precision than GSCRS, PBReL and PSO-KM by 14.1%, 4.03% and 4.03% respectively for recommendation threshold 0.5 .It is observed that with window size=1, the proposed MSC has shown higher precision than GSCRS, PBReL and PSO-KM by 16.9%, 6.9% and 5.7% respectively for recommendation threshold 1.

0.4 0.5 0.6 0.7 0.8 0.9 1

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Precision

Recommendation Threshold @ Support=0.05

GSCRS PBReL PSO-KM Proposed MSC

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GSCRS PBReL PSO-KM Proposed MSC

0.1 0.46 0.51 0.51 0.54

0.2 0.52 0.57 0.57 0.6

0.3 0.61 0.67 0.67 0.7

0.4 0.63 0.71 0.71 0.75

0.5 0.69 0.76 0.76 0.8

0.6 0.7 0.77 0.78 0.81

0.7 0.74 0.8 0.8 0.84

0.8 0.73 0.81 0.81 0.85

0.9 0.76 0.85 0.84 0.89

1 0.77 0.84 0.85 0.89

Figure 1 Precision for Window Size = 2

From the figure 3, It is observed that with window size=2, the proposed MSC has shown higher precision than GSCRS, PBReL and PSO-KM by 16%, 5.7% and 5.7%

0.4 0.5 0.6 0.7 0.8 0.9 1

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Precision

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MSC

0.1 0.47 0.51 0.52 0.53

0.2 0.5 0.54 0.55 0.56

0.3 0.62 0.69 0.68 0.72

0.4 0.65 0.71 0.72 0.74

0.5 0.7 0.77 0.77 0.82

0.6 0.75 0.82 0.83 0.87

0.7 0.79 0.86 0.86 0.9

0.8 0.8 0.89 0.9 0.92

0.9 0.81 0.9 0.89 0.92

1 0.82 0.88 0.91 0.92

Figure 4 Precision for Window Size = 3

From the figure 4, It is observed that with window size=3, the proposed MSC has shown higher precision than GSCRS, PBReL and PSO-KM by 12%, 3.85% and 1.9%

0.4 0.5 0.6 0.7 0.8 0.9 1

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Precision

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Table 6: Coverage for Window Size = 1 Recommendation

MSC

0.1 0.84 0.81 0.78 0.74

0.2 0.77 0.73 0.7 0.67

0.3 0.68 0.65 0.63 0.59

0.4 0.52 0.5 0.48 0.46

0.5 0.43 0.41 0.4 0.38

0.6 0.32 0.31 0.3 0.29

0.7 0.28 0.27 0.26 0.25

0.8 0.2 0.19 0.18 0.17

0.9 0.14 0.13 0.13 0.13

1 0.11 0.1 0.11 0.1

Figure 5 Coverage for Window Size = 1

From the figure 5, It is observed that with window size=1, the proposed MSC has shown lower coverage than GSCRS, PBReL and PSO-KM by 12%, 9% and 5.3%

respectively for recommendation threshold 0.1. It is observed that with window size=1, the proposed MSC has shown lower coverage than GSCRS, PBReL and PSO-KM by 12.3%, 7.59% and 5.13% respectively for recommendation threshold 0.5. It is observed that with window size=1, the proposed MSC has shown lower coverage than GSCRS, PBReL and PSO-KM by 9.5%, no change and 9.52% respectively for recommendation threshold 1.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Coverage

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MSC

0.1 0.73 0.7 0.68 0.66

0.2 0.58 0.55 0.53 0.52

0.3 0.53 0.5 0.49 0.47

0.4 0.44 0.42 0.41 0.4

0.5 0.33 0.32 0.31 0.3

0.6 0.24 0.23 0.22 0.21

0.7 0.22 0.21 0.2 0.19

0.8 0.17 0.16 0.15 0.14

0.9 0.11 0.11 0.11 0.11

1 0.09 0.09 0.09 0.09

From the figure 6, It is observed that with window size=2, the proposed MSC has shown lower coverage than GSCRS, PBReL and PSO-KM by 10.1%, 5.9% and 2.99%

respectively for recommendation threshold 0.1. It is observed that with window size=2, the proposed MSC has shown lower coverage than GSCRS, PBReL and PSO-KM by 9.52%, 6.45% and 3.28% respectively for recommendation threshold 0.5. It is observed that with window size=2, the proposed MSC has no change in coverage value as GSCRS, PBReL and PSO-KM for recommendation threshold 1.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Coverage

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MSC

0.1 0.54 0.52 0.49 0.48

0.2 0.45 0.43 0.41 0.4

0.3 0.4 0.39 0.37 0.35

0.4 0.39 0.37 0.37 0.35

0.5 0.25 0.24 0.23 0.22

0.6 0.2 0.19 0.18 0.17

0.7 0.18 0.17 0.16 0.15

0.8 0.15 0.14 0.13 0.13

0.9 0.08 0.08 0.08 0.08

1 0.08 0.08 0.08 0.08

From the figure 7, It is observed that with window size=3, the proposed MSC has shown lower coverage than GSCRS, PBReL and PSO-KM by 11.8%, 8% and 2.1%

respectively for recommendation threshold 0.1. It is observed that with window size=3, the proposed MSC has shown lower coverage than GSCRS, PBReL and PSO-KM by 12%, 8.7%

and 4.44% respectively for recommendation threshold 0.5. It is observed that with window size=3, the proposed MSC has no change in coverage value as GSCRS, PBReL and PSO-KM for recommendation threshold 1.

0 0.1 0.2 0.3 0.4 0.5 0.6

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Coverage

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4.1 Comparison Results

In this section, the results of Proposed MSC and content & collaborative filtering method proposed by Khribi et al. [13] is compared. For the purpose of this section, both precision and coverage have been shown in figure 10 to 16 for various window size.

Table 9: Precision for Proposed MSC and Content & Collaborative Filtering Recommendation

Window size=1 Window size=2 Window size=3 Proposed

MSC

Content &

Collaborative Filtering

Proposed MSC

Content &

0.1 0.52 0.5 0.54 0.52 0.53 0.51

0.2 0.57 0.55 0.6 0.58 0.56 0.54

0.3 0.66 0.62 0.7 0.67 0.72 0.69

0.4 0.73 0.71 0.75 0.71 0.74 0.72

0.5 0.76 0.71 0.8 0.77 0.82 0.78

0.6 0.77 0.71 0.81 0.78 0.87 0.84

0.7 0.81 0.78 0.84 0.79 0.9 0.85

0.8 0.85 0.81 0.85 0.81 0.92 0.85

0.9 0.86 0.81 0.89 0.83 0.92 0.88

1 0.9 0.82 0.89 0.83 0.92 0.88

From the figure 10, it is observed that with window size=1, the Proposed MSC has shown higher precision by 3.92% for recommendation threshold 0.1, by 6.8% for

0.4 0.5 0.6 0.7 0.8 0.9 1

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Precision

Recommendation Threshold @ Support = 0.05 Proposed MSC Content & Collaborative filtering

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recommendation threshold 0.5 and by 9.3% for recommendation threshold 1 when compared with content & collaborative filtering respectively.

From the figure 11, it is observed that with window size=2, the Proposed MSC has shown higher precision by 3.77% for recommendation threshold 0.1, by 3.82% for recommendation threshold 0.5 and by 6.97% for recommendation threshold 1 when compared with content & collaborative filtering respectively.

From the figure 13, it is observed that with window size=3, the Proposed MSC has shown higher precision by 3.84% for recommendation threshold 0.1, by 5% for

0.4 0.5 0.6 0.7 0.8 0.9 1

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Precision

0.4 0.5 0.6 0.7 0.8 0.9 1

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Precision

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recommendation threshold 0.5 and by 4.44% for recommendation threshold 1 when compared with content & collaborative filtering respectively.

Table 10: Coverage for ABC-PSO and Content & Collaborative Filtering Recommendation

Window size=1 Window size=2 Window size=3 Proposed

MSC

Content &

0.1 0.74 0.71 0.66 0.64 0.48 0.46

0.2 0.67 0.65 0.52 0.5 0.4 0.39

0.3 0.59 0.56 0.47 0.45 0.35 0.33

0.4 0.46 0.44 0.4 0.39 0.35 0.34

0.5 0.38 0.37 0.3 0.29 0.22 0.21

0.6 0.29 0.28 0.21 0.2 0.17 0.16

0.7 0.25 0.24 0.19 0.18 0.15 0.14

0.8 0.17 0.16 0.14 0.13 0.13 0.13

0.9 0.13 0.12 0.11 0.11 0.08 0.08

1 0.1 0.1 0.09 0.09 0.08 0.08

Figure 14 Coverage for Window Size= 1

From the figure 14, it is observed that with window size=1, the Proposed MSC has shown lower coverage by 4.13% for recommendation threshold 0.1, by 2.67% for

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Coverage

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recommendation threshold 0.5 and by same value for recommendation threshold 1 when compared with content & collaborative filtering respectively.

From the figure 15, it is observed that with window size=2, the Proposed MSC has shown lower coverage by 3.07% for recommendation threshold 0.1, by 3.38% for recommendation threshold 0.5 and by same value for recommendation threshold 1 when compared with content & collaborative filtering respectively.

From the figure 16, it is observed that with window size=3, the Proposed MSC has shown lower coverage by 4.25% for recommendation threshold 0.1, by 4.65% for

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Coverage

0 0.1 0.2 0.3 0.4 0.5 0.6

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Coverage

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recommendation threshold 0.5 and by same value for recommendation threshold 1 when compared with content & collaborative filtering respectively.

5. CONCLUSION

The recommendation systems apply personalization for making the system to have a new and specialized experience in e-learning for users. The ABC has a great ability to search for a global optimum but at the same time suffers from the non-direct use of the global best solution. The ABC further stores this in every iteration. The hybrid of the PSO and the ABC has now resolved the problem mentioned above. The MSC method proposed was a combination of the K-means clustering that was based on the hybrid ABC-PSO algorithms, and this was able to find better portions of the cluster for improving the performance of learning. It improves the capacity of the K-means to find the clustering of the global optimum that is in the problems of non-linear partition clustering. The results proved that with window size=1, the proposed MSC has shown higher precision than GSCRS, PBReL and PSO-KM by 14.4%, 3.92% and 1.94% respectively for recommendation threshold 0.1. It is observed that with window size=1, the proposed MSC has shown higher precision than GSCRS, PBReL and PSO-KM by 14.1%, 4.03% and 4.03% respectively for recommendation threshold 0.5.

It is observed that with window size=1, the proposed MSC has shown higher precision than GSCRS, PBReL and PSO-KM by 16.9%, 6.9% and 5.7% respectively for recommendation threshold 1.

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