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A Framework for Career-Education Hybrid Recommender System using a Selective Path Delta-SimRank Algorithm

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 90 - Number 2
Year of Publication: 2014
Waleed M. Al-adrousy
Hesham A. Ali
Taher T. Hamza

Waleed M Al-adrousy, Hesham A Ali and Taher T Hamza. Article: A Framework for Career-Education Hybrid Recommender System using a Selective Path Delta-SimRank Algorithm. International Journal of Computer Applications 90(2):42-47, March 2014. Full text available. BibTeX

	author = {Waleed M. Al-adrousy and Hesham A. Ali and Taher T. Hamza},
	title = {Article: A Framework for Career-Education Hybrid Recommender System using a Selective Path Delta-SimRank Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {90},
	number = {2},
	pages = {42-47},
	month = {March},
	note = {Full text available}


Selecting proper educational courses is a major problem in the student's life. A key factor in selecting courses is asking for the experts' opinion in the real life business. However, contacting with a real expert in a field may be difficult for many students. In this research, we suggest a general framework for a social network to connect students and experts. The framework depends on a variation of Delta-SimRank algorithm. The suggested variation is called Selective Path Delta-SimRank (SPDSR). Both the SPDSR and the original Delta-SimRank apply MapReduce technique for load balancing in a network of device. The suggested SPDSR tries to enhance the performance of Delta-SimRank. The Experiments results had shown that SPDSR had reduced the processing time in 30-70% of test cases to enhance performance by 18% in average.


  • S. G. F. De Moya-anegón, Z. Chinchilla-rodríguez, E. Corera-álvarez, F. J. Munoz-fernández, and V. Herrero-solana, "Visualizing the Marrow of Science," JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, vol. 58, no. 14, pp. 2167–2179, 2007.
  • A. Quirin, O. Cordón, P. Shelokar, and C. Zarco, "Analysis of the Time Evolution of Scientograms Using the Subdue Graph Mining Algorithm," Computational Intelligence for Knowledge-Based Systems Design. Springer Berlin Heidelberg, pp. 310–319, 2010.
  • K. Börner, "Mapping Science Case Studies?: Computational Scientometrics?:," Annual review of information science and technology, vol. 37, no. 1, pp. 179–255, 2003.
  • O. R. Zaiane, "Building a Recommender Agent for e-Learning Systems," in Proceedings of the International Conference on Computers in Education (ICCE'02), 2002, pp. 55–59.
  • Y. S. Kim, A. Krzywicki, W. Wobcke, A. Mahidadia, P. Compton, X. Cai, and M. Bain, "Hybrid Techniques to Address Cold Start Problems for People to People Recommendation in Social Networks. "
  • L. Tang and H. Liu, "GRAPH MINING APPLICATIONS TO SOCIAL NETWORK ANALYSIS," in Managing and Mining Graph Data, vol. 40, C. C. Aggarwal and H. Wang, Eds. Boston, MA: Springer US, 2010, pp. 487–513.
  • F. Mödritscher, "Towards a recommender strategy for personal learning environments," in 1st Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL 2010), 2010, vol. 1, no. 2, pp. 2775–2782.
  • R. Klamma and Z. Petrushyna, "The Troll Under the Bridge?: Data Management for Huge Web Science Mediabases," GI Jahrestagung, vol. 2, pp. 923–928, 2008.
  • K. Verbert, N. Manouselis, X. Ochoa, M. Wolpers, H. Drachsler, I. Bosnic, S. Member, and E. Duval, "Context-aware Recommender Systems for Learning?: a Survey and Future Challenges," Journal of LATEX Class Files, vol. 6, no. 1, pp. 1–20, 2007.
  • P. Rodriguez, V. Tabares, N. Duque, D. Ovalle, and R. Vicari, "BROA: An agent-based model to recommend relevant Learning Objects from Repository Federations adapted to learner profile," International Journal of Interactive Multimedia and Artificial Intelligence, vol. 2, no. 1, pp. 6–11, 2013.
  • G. Jeh and J. Widom, "SimRank?: A Measure of Structural-Context Similarity," in Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2002, pp. 1–11.
  • L. Cao, B. Cho, H. D. Kim, Z. Li, M. -H. Tsai, and I. Gupta, "Delta-SimRank computing on MapReduce," in Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining Algorithms, Systems, Programming Models and Applications - BigMine '12, 2012, pp. 28–35.
  • H. Yang, A. Dasdan, R. Hsiao, and D. S. Parker, "Map-Reduce-Merge?: Simplified Relational Data Processing on Large Clusters," in Proceedings of the 2007 ACM SIGMOD international conference on Management of data. ACM, 2007, pp. 1029–1040.
  • G. -J. Qi, M. -H. Tsai, S. -F. Tsai, L. Cao, and T. S. Huang, "Web-Scale Multimedia Information Networks," Proceedings of the IEEE, vol. 100, no. 9, pp. 2688–2704, Sep. 2012.
  • R. Agrawal, A. Gupta, Y. Prabhu, and M. Varma, "Multi-Label Learning with Millions of Labels?: Recommending Advertiser Bid Phrases for Web Pages," in International WorldWideWeb Conference Committee (IW3C2), 2013, pp. 13–23.
  • D. Eppstein and J. Wang, "A steady state model for graph power laws," arXiv preprint cs/0204001, pp. 1–8, 2002.
  • G. L. Ciampaglia, "User Participation and Community Formation in Peer Production Systems presented by," della Svizzera, Italy, 2011.
  • M. E. J. Newman, "The structure and function of complex networks," SIAM review, vol. 45, no. 3, pp. 167–256, 2003.
  • D. Liben-nowell, "The Link-Prediction Problem for Social Networks," Journal of the American society for information science and technology, vol. 58, no. 7, pp. 1019–1031, 2007.
  • M. Martinez and H. Aldrich, "Networking strategies for entrepreneurs: balancing cohesion and diversity," International Journal of Entrepreneurial Behaviour & Research Volume List, vol. 17, no. 1, pp. 7–38, 2011.
  • C. Res, C. J. Willmott, and K. Matsuura, "Advantages of the mean absolute error ( MAE ) over the root mean square error ( RMSE ) in assessing average model performance," Climate Research, vol. 30, no. 1, pp. 79–82, 2005.