Acta Informatica Pragensia 2013, 2(1), 1-17 | DOI: 10.18267/j.aip.95604

A Transitive Recommendation System for Information Technology Communities

Waleed M. Al-Adrousy1, Hesham A. Ali2, Taher T. Hamza1
1 Department of Computer Science, Faculty of Computers and Information, Mansoura University, Egypt
2 Department of Computer Engineering and Systems, Faculty of Engineering, Mansoura University, Egypt

Social networks have become a new trend for research among computer scientist around the world. Social network had an impact on users' way of life. One of social network usages is recommendation systems. The need of recommendation systems is arising when users try to know best choice for them in many items types (books, experts, locations, technologies, etc.). The problem is that a single person can't try all alternatives in all possibilities life goals to compare. Thus, a person has to use his friends' expertise to select better option in any item category. This process is the main idea of "Recommendation Systems". Recommendation systems usually depend on users-to-items ratings in a network (graph). Two main challenges for recommendation systems are accuracy of recommendation and computation size. The main objective of this paper is to introduce a suggested technique for transitive recommendation system based on users' collaborative ratings, and also to balance loading of computation. All this has to be applied on a special type of social network. Our work studied the transitivity usage in connections to get a relation (path) as a recommendation for nodes not directly connected. The target social network has eight types of nodes. So, there are techniques that are not suitable to this complex type of network. Those we can present a new support for recommending items of several types to users with several types. We believe that this functionality hasn't been fully provided elsewhere. We have suggested using single source shortest path algorithm combined with Map Reduce technique, and mathematically deduced that we have a speeding up of algorithm by 10% approximately. Our testing results shows an accuracy of 89% and false rejection of 99% compared to traditional algorithms with less configuration parameters and more steady count of recommendations.

Keywords: Social network, Recommender systems, Collaborative filtering, Parallelism, Web mining, Graph theory

Received: February 9, 2013; Revised: June 2, 2013; Accepted: June 15, 2013; Published: June 29, 2013  Show citation

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Al-Adrousy, W.M., Ali, H.A., & Hamza, T.T. (2013). A Transitive Recommendation System for Information Technology Communities. Acta Informatica Pragensia2(1), 1-17. doi: 10.18267/j.aip.9
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References

  1. PAPAGELIS, M., PLEXOUSAKIS, D., KUTSURAS, T. Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences. Trust Management, 2005, pp. 224-239. Go to original source...
  2. CORMEN, T. H., LEISERSO, CH. E., RIVEST, R. L., STEIN, C. Introduction to Algorithms, Third Edition. The MIT Press, 2009.
  3. MEGHANATHAN, N. Survey of Topology-based Multicast Routing Protocols for Mobile Ad hoc Networks, International Journal of Communication Networks and Information Security (IJCNIS), vol. 3, no. 2, pp. 124-137, 2011.
  4. JENSEN, J. B., GUTIN, G. The Bellman-Ford-Moore algorithm. Section 2.3.4 in Digraphs: theory, algorithms and applications. Springer, 2009.
  5. Jung Library [online]. [cit. 2013-05-21]. URL: http://jung.sf.net/
  6. LingPipe online documentation [online]. [cit. 2013-05-21]. URL: http://alias-i.com/lingpipe-3.9.3/docs/api/com/aliasi/classify/PrecisionRecallEvaluation.html
  7. CHOI, S. S., Cha, S. H., TAPPERT, C. A survey of binary similarity and distance measures. Journal of Systemics, Cybernetics and Informatics 8.1, pp. 43-48, 2010.
  8. JI, CH., BUYYA, R. Mapreduce programming model for. net-based cloud computing. Euro-Par 2009 Parallel Processing. Springer Berlin Heidelberg, pp. 417-428, 2009. Go to original source...
  9. BREESE, J. S., HECKERMAN, D., EMPIRICAL, C. K. Analysis of predictive algorithms for collaborative filtering. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI-98), San Francisco, Morgan Kaufmann, pp. 43-52,1998.
  10. MELVILLE, P., MOONEY, R. J., NAGARAJAN, R. Content-boosted collaborative filtering for improved recommendations. In Proceedings of the Eighteenth National Conference on Artificial Intelligence, (AAAI/IAAI-02), AAAI Press, Menlo Parc, CA, USA, pp. 187-192, 2002.
  11. XU, B., BU, J., CHEN, C., CAI, D. An exploration of improving collaborative recommender systems via user-item subgroups, In Proceedings of the 21st international conference on World Wide Web - WWW '12, April 16-20, 2012, Lyon, France, p. 21-30, 2012. Go to original source...
  12. CEYLAN, U., BIRTURK, A. Combining Feature Weighting and Semantic Similarity Measure for a Hybrid Movie Recommender System. The 5th SNA-KDD Workshop '11 (SNA-KDD'11), August 21, 2011, San Diego CA USA.
  13. SARWAR, B. M., KARYPIS, G., KONSTAN, J. A., RIEDL, J. T. Application of dimensionality reduction in recommender systems: A case study. In WebKDD Workshop at the ACM SIGKKD, 2000. Go to original source...
  14. JANE, J. Y. L., HSU, Y. J. LEE, T. Y. News Feed Filtering with Explanation Using Textual Concepts and Social Contacts. The 5th SNA-KDD Workshop '11 (SNA-KDD'11), San Diego CA USA, August 21, 2011.
  15. JAMBOR, T., WANG, J., LATHIA, N. Using Control Theory for Stable and Efficient Recommender Systems. In Proceedings of International World Wide Web Conference Committee (IW3C2), Lyon, France.April 16-20, 2012. Go to original source...
  16. MARINHO, L. B., NANOPOULOS, A., THIEME, L. S. Social Tagging Recommender Systems, chapter in Recommender Systems Handbook, pp. 615-644, 2011. Go to original source...
  17. LIN, J., SCHATZ, M. Design patterns for efficient graph algorithms in MapReduce. Proceedings of the Eighth Workshop on Mining and Learning with Graphs. ACM, pp.78-85, 2010. Go to original source...
  18. DONIS, M. Parallel Programming with Microsoft® Visual Studio® 2010 Step by Step. Microsoft Press, 2011.
  19. CELYAN, U., BIRTURK, A. Combining Feature Weighting and Semantic Similarity Measures for Hybrid Movie Recommender System. The 5th SNA-KDD workshop '11, San Diego, CA USA, August 2011.
  20. PURTELL, T. J., MACLEAN, D., KEAT, S. An Algorithm and Analysis of Social Topologies from Email and Photo Tags. The 5th SNA-KDD workshop '11, San Diego, CA USA, August 2011.
  21. MORALESM, G. D., GIONIS, A., SOZIO, M. Social Content Matching in Map Reduce. The 37th international conference on very large databaese, Seattle, Washington. August-September 2011.
  22. VOGEL, Lars. MapReduce Introduction [online]. [cit. 2013-05-29]. URL: http://www.vogella.com/articles/MapReduce/article.html
  23. ZHANG, Y., ZHOU, J., CHENG, J. Preference-Based Top-K Influential Nodes Mining in Social Networks, in 2011I EEE 10th International Conference on Trust, Security and Privacy in Computing and Communications, 2011, pp. 1512-1518. Go to original source...
  24. PU, P., CHEN, L., HU, R. A user-centric evaluation framework for recommender systems. Proceedings of the ACM RecSys 2010 Workshop on User-Centric Evaluation of Recommender Systems and Their Interfaces (UCERSTI), Barcelona, Spain, Sep 30, 2010, i, pp. 14-21. Go to original source...
  25. GOMAH, A., ABDEL-RAHMAN, S., BADR, A., FARAG, I. An Auto-Recommender Based Intelligent E Learning System. International Journal of Computer Science and Network Security, 2011, 11, (1), pp. 67-70.
  26. LOPS, P., DE GEMMIS, M., SEMERARO, G.: Content-based Recommender Systems: State of the Art and Trends, chapter in: Recommender Systems Handbook (Springer Science and Business Media, LLC), 2011, pp. 73-105. Go to original source...
  27. BURKE, R., FELFERNIG, A., GKER, M.: Recommender systems: An overview. AI Magazine, Association for the Advancement of Artificial Intelligence, 32, (3), pp. 13-18, 2011. Go to original source...
  28. SANTOS, O., BOTICARIO, J. Requirements for Semantic Educational Recommender Systems in Formal E-Learning Scenarios. Open access Algorithms, 2011, 4, (2), pp. 131-154. Go to original source...
  29. RAM, A., AI, H., RAM, P., SAHAY, S. Open Social Learning Communities. International Conference on Web Intelligence, Mining and Semantics, WIMS-11, Sogndal, Norway, May 2011, Article No. 2. Go to original source...
  30. P. LOPS, DE GEMMIS, M., SEMERARO, G. Content-based Recommender Systems : State of the Art and Trends, Springer Science and Business Media, LLC, 2011, pp. 73-105. Go to original source...
  31. DESROSIERS, C., KARYPIS, G. A comprehensive survey of neighborhood-based recommendation methods, chapter in Recommender Systems Handbook, 2011. Go to original source...
  32. EKANAYAKE, J., PALLICKARA, S., FOX, G. Mapreduce for data intensive scientific analyses. eScience, 2008. eScience'08. IEEE Fourth International Conference on. IEEE, 2008. Go to original source...
  33. LEE, K. H., et al. Parallel data processing with MapReduce: a survey. ACM SIGMOD Record 40.4 (2012): pp.11-20, 2012. Go to original source...
  34. DELIP, R., YAROWSKY, D. Ranking and semi-supervised classification on large scale graphs using map-reduce. Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing. Association for Computational Linguistics, 2009.
  35. MANNING, C. D., RAGHAVAN, P., SCHÜTZE, H. Introduction to information retrieval. Vol. 1. Cambridge: Cambridge University Press, 2008. Go to original source...
  36. DEAN, J., SANJAY, G. MapReduce: simplified data processing on large clusters. Communications of the ACM 51.1, pp.107-113, 2008. Go to original source...
  37. Werneck Paiva. [online]. [cit. 2013-04-29]. URL: http://blog.werneckpaiva.com.br/2011/08/como-funciona-o-map-reduce-usado-pelo-google/

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