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Content Recommendation Based on Data Mining  in Adaptive Social Networks
 

Content Recommendation Based on Data Mining in Adaptive Social Networks

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Presentation about recommendation systems on social networks with AtePassar.

Presentation about recommendation systems on social networks with AtePassar.

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    Content Recommendation Based on Data Mining  in Adaptive Social Networks Content Recommendation Based on Data Mining in Adaptive Social Networks Presentation Transcript

    • Content  Recommenda,on  Based   on  Data  Mining  in  Adap,ve   Social  Networks Marcel  Pinheiro  Caraciolo mpc@cin.ufpe.brOrientador:  Germano  Crispim gcv@cin.ufpe.br 1
    • Agenda1.  Mee,ng  Recommenda,on  Systems2.  Content  Recommenda,on  in  Educa,onal  Social   2.1  Methodology 2.2  Current  Results3.  Expected  Results4.  Conclusions5.  References 2
    • 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
    • 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 resourcesents in a social envi- 01 g a recommender en- (%)*++%&$%, 01 -&./&% 01different recommenda- ation called Crab [15]. !"#$%&" (%)*++%&$23*&4 al domains, including 56786- suggest online learn- 599786-eir preferences, knowl-terests. The system is 8&;*,+23*&ational social network 9/<"%,/&. =%)>&/?#%4dents, helping them to !"#$%&":47*. 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 networkudent 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
    • Objec,vesDesign 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
    • Mee,ng  Recommenda,on  Systems Content  Based  Filtering Similar Text Portuguese Maths Biology Items Interpretation recommend likes Marcel Users 6
    • Mee,ng  Recommenda,on  Systems Collabora,ve  Based  Filtering Portuguese Literature Maths Physics Items like like recommends Marcel Rafael Amanda Users Similar 7
    • Mee,ng  Recommenda,on  Systems Our  Approach  -­‐  Hybrid  Recommender  System Meta recommender system architectureCustomized control over the generation ofa 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#$"%% accepted from another users <#+CC#/3-1/"% Feedback and temporal slicing: Learn from users and select limited results by time Figure 2: Meta Recommender Components Interac- tion be highly beneficial given that students do not meet phys- ically. It may result in their becoming more socially con- 8 nected, thereby enhancing their social learning environment
    • Methodology  and  Current  Results !"#$%*+,-)% ./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
    • 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
    • ConclusionsProposal of a personalized recommender systemthat incorporate different types of source of knowledgeIt can be applied in adaptive social networksBring machine learning to web educational systemsHelp the students to find an optimal path to learning resourcesHelp the researchers and designers how to designthe social network to achieve those tasks efficiently 11
    • 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.comed 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 Miningquality 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 Academic, 2003. Although we have integrated the In ProceedingsAtePassar, it Learning and Assessment. system in the oftion of [13] J. Herlocker, J. Konstan, and J. Riedl.: Explaining e that can, in Educational Technology & Society. 2007, 242-256. principle, also be used in other web-based educational systems. MoreJ.B.: The application of data-mining the area [4] Schafer, research need to be conducted in to collaborative filtering recommendations. In Ineduca- proceedings of ACM 2000 Conference on Computermprove of the recommendation systemsJ. Wang (Ed.), Encyclopedia recommender systems. In in social learning and how they can data warehousing and mining. Hershey, PA. Idea of influence the student satisfaction, motivation and Supported Cooperative Work, 2000. learning process. 44 - 48. Group 2005 [14] B. Schafer, J. Konstan, and J. Riedl.: [5] Farzan, R., Brusilovsky, P.: Social Navigation Support Meta-recommendation systems: user-controlled in a Course Recommendation System. In proceedings of Bringing machine learning and artificial intelligence to edu- integration of diverse recommendations. In Proceedingsrces in of the 11th conference on Information and knowledge of the cational social networks Conference on main contributions of 4th International is one of the Adaptive Hypermedia this work, Adaptive resulting on a new generation of web- and which is Web-based Systems. Dublin, 2006. management, pages 43-51. ACM Press, 2002. of the 91-100. educational systems. The task of recommendation in web- [15] Crab. Available at: http://muricoca.github.com/crabve themation 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=gzn7I1EJmb4e 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 Contenter sys- Recommendation Service Based-on Simple Sequencingwledge Specification. In Proceedings of Advanced in Web-based meta- 12
    • AtéPassar  -­‐  hTp://atepassar.com Data  available  for  researching  -­‐  marcel@atepassar.com 13
    • Content  Recommenda,on  Based   on  Data  Mining  in  Adap,ve   Social  Networks Marcel  Pinheiro  Caraciolo marcelcaraciolo@acm.org 14