Presentation about recommendation systems on social networks with AtePassar.

1. Content
Recommenda,on
Based
on
Data
Mining
in
Adap,ve
Social
Networks
Marcel
Pinheiro
Caraciolo
mpc@cin.ufpe.br
Orientador:
Germano
Crispim gcv@cin.ufpe.br
1

2. Agenda
1.
Mee,ng
Recommenda,on
Systems
2.
Content
Recommenda,on
in
Educa,onal
Social
2.1
Methodology
2.2
Current
Results
3.
Expected
Results
4.
Conclusions
5.
References
2

3. The
Problem
Provide features that can enhance online social learning environment
One social software in special are recommender systems
Several approaches have been applied to web-based educational systems [1] [2]
But only a few recommender systems use data mining and explanations in
the recommendations
3

4. Objec,ves
Design a methodology for social recommender systems that incorporate different knowledge sources
It can be structured such as messages, personal profile, timeline, questions , answers
It can be non-structured study plans, disciplines, classes watched, relationships
educational resources
ents in a social envi- 01'
g a recommender en- (%)*++%&$%,'' 01'
-&./&%' 01'
different recommenda-
ation called Crab [15]. !"#$%&"' (%)*++%&$23*&4'
al domains, including 56786-'
suggest online learn-
599786-'
eir preferences, knowl-
terests. The system is 8&;*,+23*&'
ational social network 9/<"%,/&.'
=%)>&/?#%4'
dents, helping them to
!"#$%&":4'7*.'
The innovative aspect
ing on social networks
ions at the given rec- components of Architecture proposal for the recom-
It uses several Figure 1: the social network
udent more confidence mender system
arning understanding.
s use data mining and
[5]. 3. CONTENT RECOMMENDATION IN ED-
st we describe the re- UCATIONAL SOCIAL NETWORKS
engines and their use 3.1 Objectives 4

5. Objec,ves
Design an improved explanation of the recommendation to the user
Generally in the currently available systems, the recommendation only comes with an overall score
Increase the recommendation acceptance rate in order to enhance the student experience in social networks
You answered questions about X,Y, Z. You answered questions about X,Y, Z.
You have A,B,C friends in common
Your friend asked this question.
Your friends P,X and Y follows him.
You both live at the same state Pernambuco.
You have X,Y, Z followers in common
You are both interested in portuguese, maths and english.
You have X,Y, Z concursos in common
You have P,Q, R study groups in common.
Because you have difficulty in portuguese, maths and english.
Because it is a most searched course
5

6. Mee,ng
Recommenda,on
Systems
Content
Based
Filtering
Similar
Text
Portuguese Maths Biology Items
Interpretation
recommend
likes
Marcel Users
6

7. Mee,ng
Recommenda,on
Systems
Collabora,ve
Based
Filtering
Portuguese Literature Maths Physics Items
like like
recommends
Marcel Rafael Amanda Users
Similar
7

8. Mee,ng
Recommenda,on
Systems
Our
Approach
-­‐
Hybrid
Recommender
System
Meta recommender system architecture
Customized control over the generation of
a recommendation list
!"#$%*'+,-)%
!"#$%&$'()#%
It adapts in accordance to the structure of the data ./0#$-+1'/%
Recommendation of Friends ?
More weight to Collaborative Filtering %
%
%
%
!"#$%#$&'()%*&+,-$%.,#/&
!"--(0".(12%&'()%*&+,-$%.,#/&
%
Recommendation of Courses ? 2$,#/3"%456575689%
%
!"#"$%&&'%()*&+,-(.'&/,-0&+,-(.'&
:'+-1'/;%%<#+,=#%
%12%&'303#2,&('",'&2,"&34&
More weight to Content Filtering *+>')-$">,?;%%@$-3A-0#3%
%
%
Cold Start: Mitigate using Popular Recommendations B#0-%<#+'CC#/3#$%
%% %&-$-C#0#$"%%
accepted from another users
<#+'CC#/3-1'/"%
Feedback and temporal slicing:
Learn from users and select limited results by time
Figure 2: Meta Recommender Components Interac-
tion
be highly beneficial given that students do not meet phys-
ically. It may result in their becoming more socially con-
8
nected, thereby enhancing their social learning environment

9. Methodology
and
Current
Results
!"#$%*'+,-)%
./0#$-+1'/% this engine with the popular brazilian social network AtéPassar
Integrated
More than 70.000 students registered studying for the public examinations
Recommend StudyGroups, Friends,Video Classes, Questions and Concursos
More than 70.000 items available for recommend
%
%
!"--(0".(12%&'()%*&+,-$%.,#/&
%
Written in Python using a open-source framework Crab
!"#"$%&&'%()*&+,-(.'&/,-0&+,-(.'&
%12%&'303#2,&('",'&2,"&34&
%
Framework available for building recommender systems (My contribution)
It is running since January 2011
In March B#0-%<#+'CC#/3#$% was performed.
2011 , questionnaire
%% %&-$-C#0#$"%%
Liked Not Liked
-1'/"%
23%
mender Components Interac-
77%
Figure 3: AtePassar Recommender Syste
face
hat students do not meet phys- 9

10. Expected
Results
Improve the Learning Process
Analyze how the recommenders can increase the learning process in online educational social networks.
Mining all types of source in social networks in forms of recommendations
Explore the Hidden Knowledge
There are several knowledge sources in a educational social network.
All those sources as basis for discovery novel content and learning resources
Better Recommendation Understanding
The explanations can be quite helpful to better understanding of the given resource
Better learning interaction and simplification of the human/computer interaction
10

11. Conclusions
Proposal of a personalized recommender system
that incorporate different types of source of knowledge
It can be applied in adaptive social networks
Bring machine learning to web educational systems
Help the students to find an optimal path to learning resources
Help the researchers and designers how to design
the social network to achieve those tasks efficiently
11

12. plore more the learning resource materials, one of the goals
of the e-learning system. Conference, Bratislava, 2005. 229-234.
[7] Romero, C., Ventura, S.: Educational Data Mining: a
References
3.5.3 Better Recommendation Understanding
The explanations can be quite helpful for users to better
understand if the given resource is (or is not ) suitable for
Survey from 1995 to 2005. Expert Systems with
Applications. Elsevier 1:33, 2007. 135-146.
[8] Romero, C., Ventura, S.: Data mining in e-learning.
th the 5. REFERENCES Wit Press, 2006.
them. Those explanations can be beneficial in how to for-
er en- mulate Brusilovsky, P., Peylo, C.: Adaptive and to simply and
[1] a better learning interaction and hence Intelligent [9] Atepassar. Available at: http://atepassar.com
ed the shorten the human/computer Systems. International Journal
Web-based Educational interaction. [10] J. B. Schafer, J. A. Konstan, and J. Riedl.:
rought of Artificial Intelligence in Education. 13, 2003, 156 - E-commerce recommendation applications. Data Mining
quality 169. and Knowledge Discovery, 2001.
nalized 4. [2] Conole, G. & Culver, J.: The design of Cloudworks:
CONCLUSIONS [11] Shen, L.P., Shen, R.M.: Learning Content
In this Applying social networking practice to foster the sys-
paper, we presents a personalized recommender
Recommendation Service Based-on Simple Sequencing
tem that incorporate different types of sourcesand knowledge
exchange of learning and teaching ideas of designs In: Specification. In Proceedings of Advanced in Web-based
in a adaptative educational social network by3using a meta-
Computers & Education , Vol. 54 , Nr. Elsevier, 2010 Learning 2004 363-
recommender approach. We believe that this technique will
, S. 679-692 .
bring more personalized andE., Grigoriadou, M., Samarakou, [12] M. Montaner, B. L’opez, and J. Rosa.: A taxonomy of
orking [3] Gogoulou, A., Gouli, better recommendations for the recommender agents on the internet. In Artificial
students. and Chinou, D.: A Web-based Educational Setting
M.,
mmen- Intelligence Review, volume 19, pages 285-330. Kluwer
ing to Supporting Individualized Learning, Collaborative
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Learning and Assessment. system in the of
tion of [13] J. Herlocker, J. Konstan, and J. Riedl.: Explaining
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systems. MoreJ.B.: The application of data-mining the area
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educa- proceedings of ACM 2000 Conference on Computer
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in a Course Recommendation System. In proceedings of
Bringing machine learning and artificial intelligence to edu- integration of diverse recommendations. In Proceedings
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educational systems. The task of recommendation in web- [15] Crab. Available at: http://muricoca.github.com/crab
ve the
mation based education A.: Recommender System for Adaptive col-
[6] Ksristofic, can be considered as special type of [16] AtePassar Video. Available at:
nd ex- laborative learning, due to the fact that they of Informatics
Hypermedia Applications. In Proceeding help student http://www.youtube.com/watch?v=gzn7I1EJmb4
e goals and Information Technology Student Research
with similar interests to find an optimal path through the
learning material and facilitate the 229-234.
Conference, Bratislava, 2005. social network design to
[7] Romero, C., Ventura, S.: Educational Data Mining: a
be effectively be used to achieve these targets. We provide
a videoSurvey from 1995 to recommender Systemsin action at
demonstrating the 2005. Expert system with
Applications. Elsevier 1:33, 2007. 135-146.
AtePassar, which can be accessible at œ[16].
better
[8] Romero, C., Ventura, S.: Data mining in e-learning.
ble for
Wit Press, 2006.
to for-
ly and [9] Atepassar. Available at: http://atepassar.com
[10] J. B. Schafer, J. A. Konstan, and J. Riedl.:
E-commerce recommendation applications. Data Mining
and Knowledge Discovery, 2001.
[11] Shen, L.P., Shen, R.M.: Learning Content
er sys-
Recommendation Service Based-on Simple Sequencing
wledge
Specification. In Proceedings of Advanced in Web-based
meta- 12

13. AtéPassar
-­‐
hTp://atepassar.com
Data
available
for
researching
-­‐
marcel@atepassar.com
13

14. Content
Recommenda,on
Based
on
Data
Mining
in
Adap,ve
Social
Networks
Marcel
Pinheiro
Caraciolo
marcelcaraciolo@acm.org
14