Abstract
In an offline evaluation of recommender systems, data sets have been extensively used to measure the performance of recommender systems through statistical analysis. However, many data sets are domain and application dependent and cannot be engaged in different domains. This paper presents the construction of data sets for the offline evaluation of a scholar’s recommender system that suggests papers to scholars based on their background knowledge. We design a cross-validation approach to reduce the risk of false interpretations by relying on multiple independent sources of information. Our approach addresses four important issues including the privacy and diversity of knowledge resources, the quality of knowledge, and the timely knowledge. The resulting data sets represent the instance of scholar’s background knowledge in clusters of learning themes, which can be used to measure the performance of the scholar’s recommender system.
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References
Burke, R.: Knowledge-based recommender systems. In: Encyclopedia of Library and Information Systems, pp. 180–200. Marcel Dekker (2000)
Shani, G., Gunawardana, A.: Evaluating Recommendation Systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 257–297. Springer, Heidelberg (2011)
Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative Filtering Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)
Drachsler, H., Hummel, H., Berg, B., Eshuis, J.: Evaluating the Effectiveness of Personalised Recommender Systems in Learning Networks. In: Learning Network Services for Professional Development, pp. 95–113. Springer, Heidelberg (2009)
Yao, L., Tang, J., Li, J.: A Unified Approach to Researcher Profiling. In: IEEE/WIC/ACM International Conference on Web Intelligence, pp. 359–365 (2007)
Jack, K., Hammerton, J., Harvey, D., Hoyt, J.J., Reichelt, J., Henning, V.: Mendeley’s Reply to the DataTEL Challenge, pp. 1–3. Elsevier, Procedia Computer Science (2010)
Stamper, J., Koedinger, K., Baker, R.S.J.d., Skogsholm, A., Leber, B., Rankin, J., Demi, S.: PSLC DataShop: A Data Analysis Service for the Learning Science Community. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010, Part II. LNCS, vol. 6095, pp. 455–455. Springer, Heidelberg (2010)
Manouselis, N., Drachsler, H., Verbert, K., Duval, E.: TEL as a Recommendation Context. In: Manouselis, N. (ed.) Recommender Systems for Learning, pp. 21–37. Springer, New York (2010)
Manouselis, K., Kosmopoulos, N., Kastrantas, T.: Developing a Recommendation Web Service for a Federation of Learning Repositories. In: International Conference on Intelligent Networking and Collaborative Systems, INCOS 2009, pp. 208–211 (2009)
Verbert, K., Drachsler, H., Manouselis, N., Wolpers, M.: Dataset-driven Research for Improving Recommender Systems for Learning. In: Proceedings of the 1st International Conference on Learning Analytics and Knowledge, LAK 2011, pp. 44–53 (2011)
Karsten, J., Karen, A.J.: Using triangulation to validate themes in qualitative studies. Qualitative Research in Organizations and Management: An International Journal 4(2), 123–150 (2009)
Weiss, S.M., Indurkhya, N., Zhang, T., Damerau, F.J.: Information Retrieval and Text Mining. In: Fundamentals of Predictive Text Mining, pp. 75–90. Springer, Heidelberg (2010)
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. In: Recommender Systems Handbook, pp. 63–95. Springer, Heidelberg (2011)
Amatriain, X., Jaimes, A., Oliver, N., Pujol, J.M.: Data Mining Methods for Recommender Systems, pp. 39–72. Springer Sience+Business Media (2011)
Wartena, C., Brussee, R.: Topic Detection by Clustering Keywords. In: Proceedings of the 19th International Conference on Database and Expert Systems Application, DEXA 2008, pp. 2–6 (2008)
Zhiqiang, L., Werimin, S., Zhenhua, Y.: Measuring Semantic Similarity between Words Using Wikipedia. In: International Conference on Web Information Systems and Mining, pp. 251–255 (2009)
Medelyan, O., Witten, I.H., Milne, D.: Topic Indexing with Wikipedia. In: Proceeding of AAAI Workshop on Wikipedia and Artificial Intelligence: an Evolving Synergy, pp. 19–24 (2008)
Provalis Research, QDA Miner version 4.0 [Computer Software]. Provalis Research, Montreal, Canada (2011)
Zhang, K., Xu, H., Tang, J., Li, J.: Keyword Extraction Using Support Vector Machine. In: Yu, J.X., Kitsuregawa, M., Leong, H.-V. (eds.) WAIM 2006. LNCS, vol. 4016, pp. 85–96. Springer, Heidelberg (2006)
Budanitsky, A., Hirst, G.: Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures. In: Workshop on WordNet and Other Lexical Resources, NAACL 2001, pp. 29–34 (2001)
Kozakov, L., Park, Y., Fin, T., Drissi, Y.: Glossary extraction and utilization in the information search and delivery system for IBM Technical Support. IBM Systems Journal 43(3), 546–563 (2004)
Tang, J., Zhang, J.: ArnetMiner: Extraction and Mining of Academic Social Networks. In: KDD 2008, pp. 990–998. ACM, Las Vegas (2008)
Butts, C.T.: Social network analysis: A methodological introduction. Asian Journal Of Social Psychology 11(1), 13–41 (2008)
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Amini, B., Ibrahim, R., Othman, M.S. (2013). Data Sets for Offline Evaluation of Scholar’s Recommender System. In: Selamat, A., Nguyen, N.T., Haron, H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science(), vol 7803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36543-0_17
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DOI: https://doi.org/10.1007/978-3-642-36543-0_17
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