Personalisation of Generic Library Search Results Using Student Enrolment Information
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Published
Oct 14, 2015
Marwah Alaofi
Grace Rumantir
Abstract
This research explores the application of implicit personalisation techniques in information retrieval in the context of education. Motivated by the large and ever-growing volume of resources in digital libraries, coupled with students’ limited experience in searching for these resources, particularly in translating their information needs into queries, this research investigates the potential of incorporating student enrolment information, that is, published information on the units/subjects they are enrolled in, to identify students’ learning needs and produce personalised search results. We propose, implement, and evaluate a personalisation approach that makes use of the collection of units a student is enrolled in to generate a student profile used to estimate the relevance of the library resources. To do this, we propose the use of a Final Relevance Score (FRS) measure, which assigns a relevance score for each query-dependent resource based on its similarity to both the student profile and the submitted query, with α parameter controlling the effect of both. To examine the effectiveness of this approach and whether it truly produces any improvement over the library generic approach, this approach was translated into an application called PersoLib and evaluated by a group of 16 students who were doing foundation units in the Masters of Information Technology course at Monash University. The evaluation results show that the personalisation approach significantly outperforms the library generic approach. This shows the potential of incorporating student enrolment information to create a more effective search environment in which students’ search results are not only driven by the submitted query, but also by the units they are enrolled in.
How to Cite
Alaofi, M., & Rumantir, G. (2015). Personalisation of Generic Library Search Results Using Student Enrolment Information. Journal of Educational Data Mining, 7(3), 68–88. https://doi.org/10.5281/zenodo.3554727
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Keywords
educational digital libraries, information retrieval for education, implicit search personalisation, student information needs
References
AKBAR, M., SHAFFER, C. A., WEIGUO, F., & FOX, E. A. (2014). Recommendation based on Deduced Social Networks in an educational digital library. Paper presented at the 2014 IEEE/ACM Joint Conference on Digital Libraries (JCDL).
AMINI, B., IBRAHIM, R., OTHMAN, M. S., & RASTEGARI, H. (2011). Incorporating scholar's background knowledge into recommender system for digital libraries. Paper presented at the 2011 5th Malaysian Conference in Software Engineering (MySEC), Johor Bahru, Malaysia.
ASHRAF, T., & GULATI, P. A. (2010). Digital Libraries: A Sustainable Approach. In T. Ashraf, J. Sharma & P. Gulati (Eds.), Developing Sustainable Digital Libraries: Socio- Technical Perspectives (pp. 1-18). Hershey, PA: IGI Global.
BRUSILOVSKY, P., FARZAN, R., & JAE-WOOK, A. (2005). Comprehensive personalized information access in an educational digital library. Paper presented at the Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries, 2005. JCDL '05.
CALLAN, J., SMEATON, A., BEAULIEU, M., BRUSILOVSKY, P., CHALMERS, M., RIEDL, J., . . . TOMS, E. (2003). Personalisation and Recommender Systems in Digital Libraries: Joint NSF-EU DELOS Working Group Report.
CARMEL, D., ZWERDLING, N., GUY, I., OFEK-KOIFMAN, S., HAR'EL, N., RONEN, I., . . . CHERNOV, S. (2009). Personalized social search based on the user's social network. Paper presented at the the 18th ACM conference on Information and knowledge management, Hong Kong, China.
JÄRVELIN, K., & KEKÄLÄINEN, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst., 20(4), 422-446. doi: 10.1145/582415.582418
JOHN, G. H., & LANGLEY, P. (1995). Estimating continuous distributions in Bayesian classifiers. Paper presented at the Proceedings of the Eleventh conference on Uncertainty in artificial intelligence.
JOMSRI, P., SANGUANSINTUKUL, S., & CHOOCHAIWATTANA, W. (2012). A Personalized Reranking Technique for Academic Paper Searching Based on User Profiles. International Journal of Digital Content Technology and its Applications(JDCTA), 6(16). doi: 10.4156/jdcta.vol6.issue16.62
KAHN, R. E., & CERF, V. G. (1988). The Digital Library Project Volume I: The World of
Knowbots, (DRAFT): An Open Architecture For a Digital Library System and a Plan For Its Development. Reston, VA: Corporation for National Research Initiatives.
KARWEG, B., HUETTER, C., & BOHM, K. (2011). Evolving social search based on bookmarks and status messages from social networks. Paper presented at the the 20th ACM international conference on Information and knowledge management, Glasgow, Scotland, UK.
KIM, J. Y., COLLINS-THOMPSON, K., BENNETT, P. N., & DUMAIS, S. T. (2012). Characterizing web content, user interests, and search behavior by reading level and topic. Paper presented at the the fifth ACM international conference on Web search and data mining, Seattle, Washington.
KLAŠNJA-MILIĆEVIĆ, A., VESIN, B., IVANOVIĆ, M., & BUDIMAC, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 56(3), 885-899. doi: 10.1016/j.compedu.2010.11.001
LESK, M. (1997). Practical digital libraries: books, bytes, and bucks. San Francisco, CA: Morgan Kaufmann Publishers Inc.
MATTHIJS, N., & RADLINSKI, F. (2011). Personalizing web search using long term browsing history. Paper presented at the the fourth ACM international conference on Web search and data mining, Hong Kong, China.
MCKEOWN, K. R., ELHADAD, N., & HATZIVASSILOGLOU, V. (2003). Leveraging a common representation for personalized search and summarization in a medical digital library. Paper presented at the the 3rd ACM/IEEE-CS joint conference on Digital libraries.
POMERANTZ, J., CHOEMPRAYONG, S., & EAKIN, L. (2008). The development and impact of digital library funding in the United States. Advances in Librarianship, 31, 37-92. doi: 10.1016/S0065-2830(08)31002-2
POTEY, M. A., PAWAR, S. P., & SINHA, P. K. (2013). Re-ranking for personalization using concept hierarchy in DL environment. Paper presented at the 15th International Conference on Advanced Computing Technologies (ICACT).
SHEN, X., TAN, B., & ZHAI, C. (2005). Implicit user modeling for personalized search. Paper presented at the the 14th ACM international conference on Information and knowledge management, Bremen, Germany.
TANG, T., WINOTO, P., & MCCALLA, G. (2014). Further Thoughts on Context-Aware Paper Recommendations for Education. In N. Manouselis, H. Drachsler, K. Verbert & O. C. Santos (Eds.), Recommender Systems for Technology Enhanced Learning (pp. 159- 173): Springer New York.
TEEVAN, J., DUMAIS, S. T., & HORVITZ, E. (2005). Personalizing search via automated analysis of interests and activities. Paper presented at the the 28th annual international ACM SIGIR conference on Research and development in information retrieval, Salvador, Brazil.
WATERS, D. J. (1998). What are digital libraries. CLIR (Council on Library and Information Resources) Issues, 1(4), 5-6. Retrieved from http://www.clir.org/pubs/issues/issues04.html
ZHOU, D., LAWLESS, S., & WADE, V. (2012). Web Search Personalization Using Social Data. In P. Zaphiris, G. Buchanan, E. Rasmussen & F. Loizides (Eds.), Theory and Practice of Digital LibrariesLecture Notes in Computer Science (Vol. 7489, pp. 298-310): Springer Berlin Heidelberg. Retrieved from http://dx.doi.org/10.1007/978-3-642- 33290-6_32. doi: 10.1007/978-3-642-33290-6_32
AMINI, B., IBRAHIM, R., OTHMAN, M. S., & RASTEGARI, H. (2011). Incorporating scholar's background knowledge into recommender system for digital libraries. Paper presented at the 2011 5th Malaysian Conference in Software Engineering (MySEC), Johor Bahru, Malaysia.
ASHRAF, T., & GULATI, P. A. (2010). Digital Libraries: A Sustainable Approach. In T. Ashraf, J. Sharma & P. Gulati (Eds.), Developing Sustainable Digital Libraries: Socio- Technical Perspectives (pp. 1-18). Hershey, PA: IGI Global.
BRUSILOVSKY, P., FARZAN, R., & JAE-WOOK, A. (2005). Comprehensive personalized information access in an educational digital library. Paper presented at the Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries, 2005. JCDL '05.
CALLAN, J., SMEATON, A., BEAULIEU, M., BRUSILOVSKY, P., CHALMERS, M., RIEDL, J., . . . TOMS, E. (2003). Personalisation and Recommender Systems in Digital Libraries: Joint NSF-EU DELOS Working Group Report.
CARMEL, D., ZWERDLING, N., GUY, I., OFEK-KOIFMAN, S., HAR'EL, N., RONEN, I., . . . CHERNOV, S. (2009). Personalized social search based on the user's social network. Paper presented at the the 18th ACM conference on Information and knowledge management, Hong Kong, China.
JÄRVELIN, K., & KEKÄLÄINEN, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst., 20(4), 422-446. doi: 10.1145/582415.582418
JOHN, G. H., & LANGLEY, P. (1995). Estimating continuous distributions in Bayesian classifiers. Paper presented at the Proceedings of the Eleventh conference on Uncertainty in artificial intelligence.
JOMSRI, P., SANGUANSINTUKUL, S., & CHOOCHAIWATTANA, W. (2012). A Personalized Reranking Technique for Academic Paper Searching Based on User Profiles. International Journal of Digital Content Technology and its Applications(JDCTA), 6(16). doi: 10.4156/jdcta.vol6.issue16.62
KAHN, R. E., & CERF, V. G. (1988). The Digital Library Project Volume I: The World of
Knowbots, (DRAFT): An Open Architecture For a Digital Library System and a Plan For Its Development. Reston, VA: Corporation for National Research Initiatives.
KARWEG, B., HUETTER, C., & BOHM, K. (2011). Evolving social search based on bookmarks and status messages from social networks. Paper presented at the the 20th ACM international conference on Information and knowledge management, Glasgow, Scotland, UK.
KIM, J. Y., COLLINS-THOMPSON, K., BENNETT, P. N., & DUMAIS, S. T. (2012). Characterizing web content, user interests, and search behavior by reading level and topic. Paper presented at the the fifth ACM international conference on Web search and data mining, Seattle, Washington.
KLAŠNJA-MILIĆEVIĆ, A., VESIN, B., IVANOVIĆ, M., & BUDIMAC, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 56(3), 885-899. doi: 10.1016/j.compedu.2010.11.001
LESK, M. (1997). Practical digital libraries: books, bytes, and bucks. San Francisco, CA: Morgan Kaufmann Publishers Inc.
MATTHIJS, N., & RADLINSKI, F. (2011). Personalizing web search using long term browsing history. Paper presented at the the fourth ACM international conference on Web search and data mining, Hong Kong, China.
MCKEOWN, K. R., ELHADAD, N., & HATZIVASSILOGLOU, V. (2003). Leveraging a common representation for personalized search and summarization in a medical digital library. Paper presented at the the 3rd ACM/IEEE-CS joint conference on Digital libraries.
POMERANTZ, J., CHOEMPRAYONG, S., & EAKIN, L. (2008). The development and impact of digital library funding in the United States. Advances in Librarianship, 31, 37-92. doi: 10.1016/S0065-2830(08)31002-2
POTEY, M. A., PAWAR, S. P., & SINHA, P. K. (2013). Re-ranking for personalization using concept hierarchy in DL environment. Paper presented at the 15th International Conference on Advanced Computing Technologies (ICACT).
SHEN, X., TAN, B., & ZHAI, C. (2005). Implicit user modeling for personalized search. Paper presented at the the 14th ACM international conference on Information and knowledge management, Bremen, Germany.
TANG, T., WINOTO, P., & MCCALLA, G. (2014). Further Thoughts on Context-Aware Paper Recommendations for Education. In N. Manouselis, H. Drachsler, K. Verbert & O. C. Santos (Eds.), Recommender Systems for Technology Enhanced Learning (pp. 159- 173): Springer New York.
TEEVAN, J., DUMAIS, S. T., & HORVITZ, E. (2005). Personalizing search via automated analysis of interests and activities. Paper presented at the the 28th annual international ACM SIGIR conference on Research and development in information retrieval, Salvador, Brazil.
WATERS, D. J. (1998). What are digital libraries. CLIR (Council on Library and Information Resources) Issues, 1(4), 5-6. Retrieved from http://www.clir.org/pubs/issues/issues04.html
ZHOU, D., LAWLESS, S., & WADE, V. (2012). Web Search Personalization Using Social Data. In P. Zaphiris, G. Buchanan, E. Rasmussen & F. Loizides (Eds.), Theory and Practice of Digital LibrariesLecture Notes in Computer Science (Vol. 7489, pp. 298-310): Springer Berlin Heidelberg. Retrieved from http://dx.doi.org/10.1007/978-3-642- 33290-6_32. doi: 10.1007/978-3-642-33290-6_32
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