Utilizing Collaborative Filtering and Recommender Service in the Student Advising and Course Registration System

Copyright © 2013 IJECCE, All right reserved

Utilizing Collaborative Filtering and Recommender

Service in the Student Advising and Course Registration

System

Dr. Zahee A.M. Abu Sarhan

Ass. Professor, Computer Information System Department Applied Science Private University

Email: [email protected]

Abstract – In this paper the modified collaborative filtering model is used to adopt the collaborative filteringCF for timetabling and academic advising, and then present the suitable algorithm for recommender system to improve student registration system. The modified models can be implemented to solve student course registration and advising issues. Student course registration is important as well as the course registration process to ensure that the students achieve their university degree requirements in a comprehensive way and structured approach without facing unnecessary delays and needless procedures.

Keywords – Advising Agent, Registration System,

Recommender Agent, Collaborative Filtering, Advising System.

I.

I

Now a daysall of the proposed algorithms and modes of collaborative filters and recommender services, manly depends on two algorithms; the first is a user-based collaborative filtering and it’s based on similarities between users; and the second is an item-based collaborative filtering and this algorithm is based on similarities between items.These algorithms were presented in many studies [1]-[3]. Shortly the collaborative filtering techniques use the assumption that users with similar tastes will rate things similarly. For example, if you liked a book with a title “software engineering” that is published on the web page, you will like another book with the title “introduction to software engineering” that is published on another web page. The task is to clearly extract the features of the items that are most predicted. The idea of these algorithms is very attractive to be adopted in e-commerce web systems, and developed in social systems [2].

The recommender system can be defined as a software tool or agent that provide users with some suggestions [2], [10]-[12].

Recommender techniques are widely used in many areas, especially in e-commerce. Lately, they are also used in e-learning projects such as suggesting resources (e.g. documents, guides, etc.) to the students. In this work, we recommend a novel strategy which uses recommender program methods for academic information exploration, especially for forecasting college student performance. To confirm this strategy, we evaluate recommender program methods with traditional regression methods such as logistic/linear regression by using academic information

for brilliant training techniques. Trial outcomes show that the suggested strategy can improve forecast outcomes.

As noted previously, all the studies of Collaborative Filtering (CF) based on similarities. And recommender systems (RS) based on carrying out CF, other words the first step of recommender system is to gather rating by a group of users about a group of items, the goal is to select a group of users with the most similar ratings, and the suggests a series of recommendation to the individual, on which this individual have not shown a preference but which have been very greatly rated by an important amount of the group of users with similar preferences to the individual.

The RS results quality greatly depends on the quality of the provided results by its CF, in other words to provide high quality recommendations generally must be invoked high quality of CF results. The early presented algorithms are unsuitable in student advising systems and course recommender systems. These algorithms must be modified to carry high quality recommendations for students.

II.

P

S

attention either by advisors or by students. The proposed Student Advising Software Agent Algorithm (SASAA) developed to achieve the solution for these issues. The presented SASAA recommends suggested courses for registration depending on the student level and course level.The SASAA helpsthe students in selecting proper courses using the University Registration System.

III.

T

C

F

M

Everyday people get a vast amount of information of the interlocutors, newspapers, news, Internet, etc. To simplify the analysis of a large amount of information, for example, selecting books were created recommendation system (hereinafter PC).

Developers one of the first personal computers (PC) [5] introduced a term which is widely used at this time - this term collaborative filtering or collaborative filtering (CF hereinafter). In [3], which was introduced this term describes an experimental system of e-mails, whose creation was motivated by the growth of e-mails by flooding, most of which are not interesting to the user. The purpose of this system is to filter unwanted messages. In addition to filtering by content (content-based filtering), is compatible with the CF. This technology is that different people communicates with one another, helping to filter messages by writing response to read documents.

This technique is often used in the creation of PC, whose mission is to recommendations of the various objects the users of such systems. PC is a popular web service on Amazon. It uses a database of the user preferences with respect to various objects in order to make recommendations to users.

In general, the CF scenario can be described as follows [6]-[8]:

There is a set of users 𝑢1, 𝑢2, 𝑢3, ⋯ 𝑢𝑛 and objects 𝑂𝑏1, 𝑂𝑏2, 𝑂𝑏3, ⋯ 𝑂𝑏𝑚 ; each user has a list of evaluated by them objects. Evaluations can fall into different scales e.g. from 1 to 10; as well as various types of scales.

If the user 𝑢𝑖wishes to obtain recommendation (or a forecast of its assessment on an invaluable object), then the known estimates for the next set preferences (or estimated on the same objects) to users 𝑢𝑖. The system then makes recommendations to the user 𝑢𝑖or forward predictive assessment of the object, based on estimates of the next 𝑢𝑖touser’s preferences.

The standard steps of these algorithms can be described as follows:

• Calculating the similarity between two users.

• Calculation of prediction by taking a weighted average from all estimations by the projected object.

This paper proposes a prediction model based on the distance between students and courses: each system student requests registration of a certain number of courses. Based on information regarding the course and student’s level, the system should send a recommendation to the student to register the appropriate courses.

IV.

T

M

C

F

M

Given two sets, a set of students S and a set of courses O. Information about students and courses can be presented using the characteristics. Student corresponds a set of characteristics - CS, Courses -Co. On the sets CSand

Cointroduced sequence of relationships (the elements of these sets are the rank values which can be only compared). In general, the dimension sets do not match, and the values of different sets belong to different ordinal scales.

Information about Students and courses can be represented by the features vectors, to describe the elements of the setsSand O as:

𝑠 ∈ 𝑆 = 𝐶1𝑆, ⋯ , 𝐶𝑛𝑆

𝑜 ∈ 𝑂 = 𝐶1𝑂, ⋯ , 𝐶𝑚𝑂

(1) where 𝐶𝑖𝑆∈ 𝐶𝑆, 𝑖 = 1, ⋯ , 𝑁 , 𝑁 = 𝐶𝑆

where 𝐶𝑗𝑂 ∈ 𝐶𝑂, 𝑗 = 1, ⋯ , 𝑀 , 𝑀 = 𝐶𝑂

In general, each student characteristic displays a separate level (the quantity of passed credit hours), the course – the level of the course (the number of course prerequisites). Between the characteristics of students and courses can be constructed compliance [9].

Let is in the system n courses, and some courses are prerequisites for other courses. The estimation can be defined as 1 or 0, if the course a1 is prerequisite to the course a2 the estimation in this case will be 1, otherwise it will be 0. Then all of the estimations can be represented as a matrix shown below:

𝑎1 𝑎2 𝑎3 … 𝑎𝑗

𝑎1 0 0 0 … 0

𝑎2 1 0 0 … 0

⋮ 1 1 0 … 0

𝑎𝑖 1 1 1 … 0

(2)

The course level can be calculated using the following formula:

Cli= nk=1aij, (3)

Where Cli– is the course ailevel? In other words the course

a2level is

𝐶𝑙2= 𝑎21+ 𝑎22+ 𝑎23+ … + 𝑎2𝑖; (4)

The student level can be defined as the follows (see table-I):

Table I: Academic levels and appropriate credit hours. St. Level Passed credit hours

1 15

2 30

3 45

4 60

5 78

6 96

7 114

Copyright © 2013 IJECCE, All right reserved

V.

T

D

B

S

C

Let it be known the function f: O → S, that can be built through the establishment of the correspondence between the characteristics of the student and the course. This function displays the course oon the area of interest by the student s. Assume that one element of set Ocorresponds to the subset of the setS. For each information system, functions 𝑓: 𝑂 → 𝑆 can be implemented differently, because it depends on the characteristics and sets of scales chosen for them.

As mentioned above, each element of the set 𝑆described by the vector of its characteristics. We define the significance of characteristics using a scale of weights. In the general case the weights will be given by following way: characteristics scale𝑆𝑡𝐿 = 𝑆𝑡𝐿1, ⋯ , 𝑆𝑡𝐿𝑘 , where

𝑆𝑡𝐿𝑘 - maximum characteristics weights 𝑆, 𝑆𝑡𝐿𝑖 ≥

𝑆𝑡𝐿𝑗, 𝑖 > 𝑗, corresponds to the scale of weights𝑊 =

𝑤1, ⋯ , 𝑤𝑘 , 𝑤𝑕𝑒𝑟𝑒 𝑤𝑘= 0, ∀𝑖 > 𝑗: 𝑤𝑖 ≤ 𝑤𝑗, ∀𝑖: 𝑤𝑖 ≥

0 . Weight 𝑤𝑖 corresponds to the importance of characteristics𝑆𝑡𝐿𝑖.

By setting the weights for the characteristics, we introduce the distance Q𝑆 for elements 𝑎, 𝑏 set 𝑂.

Let 𝜔𝑎 _{= 𝜔}

1𝑎, ⋯ , 𝜔𝑚𝑎 - A vector of weights for course𝑎, -𝜑𝑏_{= 𝜑}

1 𝑏_{, ⋯ , 𝜑}

𝑚𝑏 The weight vector for the student𝑏. To select appropriate course the distance between course level and student level should be defined as:

Q𝑆 𝑎, 𝑏 = 𝑆𝑡𝐿𝑖− 𝐶𝑙𝑖 (5)

Where – Q𝑆 𝑎, 𝑏 appropriate course, 𝑆𝑡𝐿𝑖 - Student level, one of the values should be one of three values

Q𝑆 𝑎, 𝑏 =

−𝒙, 𝑆𝑡𝐿𝑖 < 𝐶𝑙𝑖

𝟎, 𝑆𝑡𝐿𝑖 = 𝐶𝑙𝑖

𝒙, 𝑆𝑡𝐿𝑖 > 𝐶𝑙𝑖

(6)

• Q𝑆 𝑎, 𝑏 = −𝑥In this case the course level is higher than the student level and the course has prerequisites that the student didn’t passed.

• Q𝑆 𝑎, 𝑏 = 𝑥The course level is lower than the student level.

• Q𝑆 𝑎, 𝑏 = 0It is appropriate course.

Let us introduce the function of the distance between two setsSandO. The distance from point xtosubset Aregarding pseudo metrics Qcan be defined as D A, x = inf d x, y : y ∈ A .

Thus, it is possible to introduce a function of the distance from the student to the course:

Q𝑆𝑂 𝑠 ∈ 𝑆, 𝑜 ∈ 𝑂 = 𝑖𝑛𝑓 𝑑𝑠 𝑥, 𝑦 ∶ 𝑦 ∈ 𝑓 𝑜 (7)

VI.

T

P

M

R

G

The result of the recommender system or CF is a set consisting of pairs (student × course).

The output of such systems can be represented by a relations graph. Forecast relations graph which can be called a graphg = V = S, O , E , whose vertices are the elements of the sets O and S, and ribs can be presented by pairs of s ∈ S, o ∈ O .

The rib that joints element s ∈ Swith an o ∈ Oelement means that the objecto ∈ O recommended for student s ∈ S. Each rib has a weight that corresponds to the function of the distance QSO s ∈ S, o ∈ O . Each elementsi ∈ S, i = 1, ⋯ , N , can be joined with Li i =

1, ⋯ , N

ribsLito objects. The number Licorresponds to the amount requested by the student recommendations.

Thereby the task of forecasting can be described as finding such a graph connections, which the sum of rib weights seeks to the minimum. In other words, to recommend such a courses for students the mean distance between them and the students is the minimum, see fig 1.

Fig.1. Graph connections between set S and set O

Introduce the function 𝑒 ∶ 𝐺 → 𝑅 𝑒 𝑔 ∈ 𝐺 = 𝑄𝑆𝑂 𝑠𝑖 , 𝑜𝑖𝑗

𝐿_𝑖 𝑗 =1 𝑁

𝑖=1 (8)

This function assigns the relationship graph with a number equals to the average of the distance between courses and students. The value of this function is an indicator of the forecast efficiency. If the number is smaller the forecast is better.

Our task to find the minimum of the function 𝑒 𝑔 ∈ 𝐺 over the set of graph 𝐺.

𝑒 𝑔 ∈ 𝐺 → 𝑚𝑖𝑛 (9)

The assigned task can be solved by the exhaustive search the entire set of graphs𝐺. But this solution requires a huge execution time. In this paper we propose an algorithm based on the method of simulated annealing. Student profile 𝑆𝑖, 𝑖 = 1, ⋯ , 𝐿 𝑖 ,Called 𝑂′⊂ 𝑂 =

𝑜1, ⋯ , 𝑜𝐿_𝑖, Suchthat∀𝑗 = 1, ⋯ , 𝐿 ∃𝑒𝑖 𝑗 ∈ 𝐸 = 𝑠𝑖, 𝑜𝑗 , In other words, the student profile – set of allocated courses.

The neighboring graph to the graph 𝑔is a graph𝑕, In which there is only single student profile, the profile is different from the same student profile in the graph𝑔, only in a single course.

Start

Read course data Read Student Data

Choose the random graph 𝑔∈𝐺

Calculate the value of 𝑒 (𝑔) = 𝑣𝑎𝑙.

Choose a neighbor to the graph 𝑔

graph 𝑔’

Calculate the value of 𝑒 (𝑔')= 𝑣𝑎𝑙'

1

If (val > val ')

𝑔 := 𝑔'

Yes

Finish

calculate the probability 𝑃 of transition to the graph 𝑔 and take

a random number 𝑝∈ (0, 1);

𝑝 > 𝑃

𝑔 := 𝑔'

Yes No

𝑔 := 𝑔

No

1

Fig.2. Algorithm Filtering and recommending student advising and time tabling

VII.

P

R

The information system http://asu.edu.jo/asuregistration

was implemented using a module and algorithm that provided and described in this paper. The algorithm was

tested on the results obtained at registration and advising system for the summer semester.

Users of the system are the students and the advisors, objects – are the courses. After presenting the semester courses users of the system can register and create there time tables. As a result of advising and registering students based on time tables. Each system user is described by a vector of characteristics relevant there passed credit hours. Value characteristics of the users determine the level of the student. The level of the course also describes the feature vectors that correspond to the required courses. The characteristic value determines that the course is belongs to the appropriated course. Based on the vectors values produced the appropriated course selection. Obtaining the performance indicator value of the forecasts. The value of this function on the graph bonds gained in the operation of the algorithm, which is used earlier in the system that was improved more than two times.

VIII.

C

In this paper the presented model of forecasting systems CF and the way of its evaluation. This model does not require sequential students sorting and finding for each of them the nearest set of student’s levels, thus reducing calculations. Proposed the calculation proximity between students, based on the pseudo metric assessments, as well as a criterion, which is applicable in practice. The described methods are implemented and shown there practical efficiency.

A

The author is grateful to the Applied Science University, Amman, Jordan, for the full financial support granted to this research.

R

[1] Marlin, Benjamin, et al. "Collaborative filtering and the missing

at random assumption." arXiv preprint arXiv:1206.5267 (2012).

[2] Starbird, Kate, Grace Muzny, and LeysiaPalen. "Learning from

the Crowd: Collaborative Filtering Techniques for Identifying

On-the-Ground Twitterers during Mass Disruptions."

Proceedings of the Conference on Information Systems for Crisis Response and Management (ISCRAM 2012), edited by Leon Rothkrantz, JozefRistvej, and Zeno Franco. Vancouver: Simon Fraser University. 2012.

[3] Drake, Jayne K., Peggy Jordan, and Marsha A. Miller. Academic

advising approaches: Strategies that teach students to make the most of college. John Wiley & Sons, 2013.

[4] Pan, Rong, and Martin B. Scholz. "Methods and systems for

determining unknowns in collaborative filtering." U.S. Patent No. 8,185,535. 22 May 2012.

[5] Goldberg, David, et al. "Using collaborative filtering to weave an

information tapestry." Communications of the ACM 35.12 (1992): 61-70.

[6] Breese, John S., David Heckerman, and Carl Kadie. "Empirical analysis of predictive algorithms for collaborative filtering." Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., 1998.

[7] Kirkpatrick S., Gelatt C., Vecchi M. Optimization by Simulated

Copyright © 2013 IJECCE, All right reserved

[8] Konstan, Joseph A., and John Riedl. "Recommender systems:

from algorithms to user experience." User Modeling and User-Adapted Interaction 22.1-2 (2012): 101-123.

[9] Lü, Linyuan, et al. "Recommender systems." Physics Reports 519.1 (2012): 1-49.

[10] Knijnenburg, Bart P., et al. "Explaining the user experience of recommender systems." User Modeling and User-Adapted Interaction 22.4-5 (2012): 441-504.

[11] Morais, A. Jorge, Eugénio Oliveira, and AlípioMário Jorge. "A Multi-Agent Recommender System." Distributed Computing and Artificial Intelligence. Springer Berlin Heidelberg, 2012. 281-288.

[12] Rosaci, Domenico, and Giuseppe ML Sarné. "A multi-agent

recommender system for supporting device adaptivity in e-Commerce." Journal of Intelligent Information Systems 38.2 (2012): 393-418.

A

’

P

Dr. Zahi A. Abu Sarhan

Received the M.S. and Ph.D. degrees in