{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T21:40:08Z","timestamp":1654119608001},"reference-count":24,"publisher":"IGI Global","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,1,1]]},"abstract":"<p>Collaborative tagging has been very popular with the development of the Web 2.0, which helps users manage, share and utilize resources effectively. For various kinds of resources, the way to recommend appropriate resources to right users is the key problem in tagging system. This paper proposes a user taste diffusion model based on the tripartite hypergraph to deal with the tri-relation of user-resource-tag in folksonomies and the data sparsity problem in personalized recommendation. Through the defined tri-relation model and diffusion probability matrix, the user\u2019s taste is diffused from itself to other users, resources and tags. When diffusion stops, the candidate resources can be identified then be ranked according to the taste values. As a result the top resources that have not been collected by the given user are selected as the final recommendations. Benefiting from the introduction of iterative diffusion mechanism, the recommendation results not only cover the resources collected by the given user\u2019s direct neighbors but also cover the ones which are collected by his\/her extended neighbors. Experimental results show that our method performs better in terms of precision and recall than other recommendation methods.<\/p>","DOI":"10.4018\/jkss.2012010102","type":"journal-article","created":{"date-parts":[[2012,4,5]],"date-time":"2012-04-05T13:15:42Z","timestamp":1333631742000},"page":"18-30","source":"Crossref","is-referenced-by-count":1,"title":["A Resource Recommendation Method Based on User Taste Diffusion Model in Folksonomies"],"prefix":"10.4018","volume":"3","author":[{"given":"Jiangning","family":"Wu","sequence":"first","affiliation":[{"name":"Dalian University of Technology, China"}]},{"given":"Yunfei","family":"Shi","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, China"}]},{"given":"Chonghui","family":"Guo","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, China"}]}],"member":"2432","reference":[{"key":"jkss.2012010102-0","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"jkss.2012010102-1","unstructured":"Diederich, J., & Iofciu, T. (2006). Finding communities of practice from user profiles based on folksonomies. In Proceedings of the 1st International Workshop on Building Technology Enhanced Learning Solutions for Communities of Practice, Crete, Greece (pp. 288-297)."},{"key":"jkss.2012010102-2","first-page":"1","article-title":"Random hypergraphs and their applications.","volume":"9","author":"G.Gourab","year":"2009","journal-title":"Physical Review E: Statistical, Nonlinear, and Soft Matter Physics"},{"key":"jkss.2012010102-3","doi-asserted-by":"crossref","unstructured":"Halpin, H., Robu, V., & Shepherd, H. (2007). The complex dynamics of collaborative tagging. In Proceedings of the 16th International Conference on World Wide Web, Banff, AB, Canada (pp. 211-220).","DOI":"10.1145\/1242572.1242602"},{"key":"jkss.2012010102-4","doi-asserted-by":"crossref","unstructured":"Han, P., Xie, B., Yang, F., & Shen, R. M. (2005). An adaptive spreading activation scheme for performing more effective recommendations. 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Skowron (Eds.), Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (LNCS 4702, pp. 506-514).","DOI":"10.1007\/978-3-540-74976-9_52"},{"key":"jkss.2012010102-9","doi-asserted-by":"crossref","unstructured":"Liang, H., Xu, Y., Li, Y., & Nayak, R. (2008). Collaborative filtering recommender systems using tag information. In Proceedings of the Web Intelligence\/Intelligent Agent Technology Workshops, Sydney, Australia (pp. 59-62).","DOI":"10.1109\/WIIAT.2008.97"},{"key":"jkss.2012010102-10","doi-asserted-by":"publisher","DOI":"10.1142\/S0129183109013613"},{"key":"jkss.2012010102-11","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-85820-3_19"},{"key":"jkss.2012010102-12","unstructured":"Millen, D. R., & Feinberg, J. (2006). Using social tagging to improve social navigation. In Proceedings of the Workshop on the Social Navigation and Community based Adaptation Technologies, Dublin, Ireland."},{"key":"jkss.2012010102-13","unstructured":"Niwa, S., Doi, T., & Honiden, S. (2006). Web page recommender system based on folksonomy mining. In Proceedings of the Third International Conference on Information Technology, Las Vegas, NV (pp. 388-393)."},{"key":"jkss.2012010102-14","doi-asserted-by":"crossref","unstructured":"Peng, J., Zeng, D., Zhao, H., & Wang, F. (2010). Collaborative filtering in social tagging systems based on joint item-tag recommendations. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management, Toronto, ON, Canada (pp. 809-818).","DOI":"10.1145\/1871437.1871541"},{"key":"jkss.2012010102-15","doi-asserted-by":"publisher","DOI":"10.1145\/361219.361220"},{"key":"jkss.2012010102-16","doi-asserted-by":"crossref","unstructured":"Song, X., Tseng, B. L., Lin, C. Y., & Sun, L. M. (2006). Personalized recommendation driven by information flow. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, WA (pp. 509-516).","DOI":"10.1145\/1148170.1148258"},{"key":"jkss.2012010102-17","unstructured":"Sun, A. R., Cheng, J., & Zeng, D. D. (2009). A novel recommendation framework for microblogging based on information diffusion. In Proceedings of the 19th Annual Workshop on Information Technologies & Systems, Phoenix, AZ (pp. 199-216)."},{"key":"jkss.2012010102-18","unstructured":"Troussov, A., Parra, D., & Brusilovsky, P. (2009). Spreading activation approach to tag- aware recommenders: modeling similarity on multidimensional networks. In Proceedings of the Workshop on Recommender Systems and the Social Web, New York, NY."},{"key":"jkss.2012010102-19","doi-asserted-by":"crossref","unstructured":"Tso-Sutter, K. H. L., Mariho, L. B., & Schmidt-Thieme, L. (2008). 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