Owing to the dramatic growth of Web2.0 in recent year, blogs have become an important sharing media on the internet. Social bookmarking was invented to be a portal to collect blog articles, and bookmark lists are ranked by recency or popularity. However, people might have their own interests, and they would prefer to read articles which match their preferences. In this research, we have proposed a personalized blog recommendation service on a social bookmarking site, recommending desirable blog articles based on user preferences and reputation-based popularity of articles. We contribute to proposing a novel approach for deriving the reputation-based popularity of articles in a social bookmarking site. Moreover, our recommendation approach adopts content-based filtering (CBF) by considering the target user‟s group preferences to alleviate the limitation of CBF that recommends only those similar items user liked previously. Our experiment results show that the proposed method outperforms traditional CBF and ICF methods, and can effectively improve the quality of recommendation.
Future works will address in two themes. First, solving the cold-start problem;
new users or new articles are hard to analyze due to lack of data. Therefore, future studies are needed to create a way to evaluate new user‟s preference and new article‟s popularity.
Secondly, people often change their preference while surfing on the internet, but the present work only focuses on analyzing past data and predicting future preference. Hence further investigation include providing recommendation list based on different article categories in a real-time manner.
37 Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, IEEE Trans. on Knowl. and Data Eng., vol. 17, no. 6, 734-749.
[3] Agarwal, N., Liu, H., Tang, L., and Yu, P. S. 2008. Identifying the influential bloggers in a community, in Proceedings of the international conference on Web search and web data mining, Palo Alto, California, USA, 207-218.
[4] Altman, A., and Tennenholtz, M. 2005. Ranking systems: the PageRank axioms, in Proceedings of the 6th ACM conference on Electronic commerce, Vancouver, BC, Canada, 1-8.
[5] Baeza-Yates, R. A., and Ribeiro-Neto, B. 1999. Modern Information Retrieval:
Addison-Wesley Longman Publishing Co., Inc.
[6] Burke, R. 2002. Hybrid Recommender Systems: Survey and Experiments, User Modeling and User-Adapted Interaction, vol. 12, no. 4, 331-370.
[7] Chen, W., Zeng, Q., and Wenyin, L. 2006. A User Reputation Model for a User-Interactive Question Answering System, in Proceedings of the Second International Conference on Semantics, Knowledge, and Grid, 40.
[8] Gill, A. J., Gergle, D., French, R. M., and Oberlander, J. 2008. Emotion rating from short blog texts, in Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems, Florence, Italy, 1121-1124.
[9] Glance, N., Hurst, M., and Tomokiyo, T. 2004. BlogPulse: Automated Trend Discovery for Weblogs, in WWW 2004 Workshop on the Weblogging Ecosystem.
[10] Huang, T. C., Cheng, S. C., and Huang, Y. M. 2009. A blog article recommendation generating mechanism using an SBACPSO algorithm, Expert Systems with Applications, vol. 36, no. 7, 10388-10396, September.
[11] Isaías, P., Miranda, P., and Pífano, S. 2009. Critical Success Factors for Web 2.0 A Reference Framework, Online Communities and Social Computing, A. Ozok and P. Zaphiris, eds., pp. 354-363: Springer Berlin Heidelberg.
38
[12] Jain, A. K., Murty, M. N., and Flynn, P. J. 1999. Data Clustering: A Review, ACM Computing Surveys, vol. 31, no. 3, pp. 264 - 323.
[13] Kazienko, P., and Adamski, M. 2007. AdROSA--Adaptive personalization of web advertising, Information Sciences, vol. 177, no. 11, 2269-2295.
[14] Klaisubun, P., Kajondecha, P., and Ishikawa, T. 2007. Behavior Patterns of Information Discovery in Social Bookmarking Service, in Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, 784-787.
[15] Kritikopoulos, A., Sideri, M., and Varlamis, I. 2006. BlogRank: ranking weblogs based on connectivity and similarity features, in Proceedings of the 2nd international workshop on Advanced architectures and algorithms for internet delivery and applications, Pisa, Italy, 8.
[16] Li, H., Lee, F. M., and Chan, S. C. 2008. The Blog-Article Recommendation System (BARS). 771-776.
[17] Linden, G., Smith, B., and York, J. 2003. Amazon.com recommendations: item-to-item collaborative filtering, IEEE Distributed Systems Online, vol. 4, no. 1, 2003.
[18] Liu, E. Z. F., and Chang, Y. F. 2008. The learning opportunities of social bookmarking service: an example of FunP, WSEAS TRANSACTIONS on SYSTEMS, vol. 7, no. 10, 1196-1205.
[19] Liu, J., Birnbaum, L., and Pardo, B. 2008. Categorizing blogger's interests based on short snippets of blog posts, in Proceeding of the 17th ACM conference on Information and knowledge management, Napa Valley, California, USA, 1525-1526.
[20] Liu, K., Chen, W., Bu, J., Chen, C., and Zhang, L. 2007. User Modeling for Recommendation in Blogspace, in Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops, 79-82.
[21] McNally, K., O'Mahony, M. P., Smyth, B., Coyle, M., and Briggs, P. 2010.
Towards a reputation-based model of social web search, in Proceeding of the 14th international conference on Intelligent user interfaces, Hong Kong, China, 179-188.
39
[22] O'Donovan, J., and Smyth, B. 2005. Trust in recommender systems, in Proceedings of the 10th international conference on Intelligent user interfaces, 167-174.
[23] O'Donovan, J., and Smyth, B. 2006. Is trust robust?: an analysis of trust-based recommendation, in Proceedings of the 11th international conference on Intelligent user interfaces, Sydney, Australia, 101-108.
[24] Oreilly, T. 2007. What is Web 2.0: Design Patterns and Business Models for the Next Generation of Software, Communications & Strategies, First Quarter, SSRN, 17.
[25] Page, L., Brin, S., Motwani, R., and Winograd, T., The PageRank Citation
Ranking: Bringing Order to the Web, Technical Report, Stanford InfoLab, 1999.
[26] Peng, T. C., and Chou, S. C. T. 2009. iTrustU: a blog recommender system based on multi-faceted trust and collaborative filtering, in Proceedings of the 2009 ACM symposium on Applied Computing, Honolulu, Hawaii, 1278-1285.
[27] Puspitasari, F., Lim, E.-P., Goh, D. H.-L., Chang, C.-H., Zhang, J., Sun, A., Theng, Y.-L., Chatterjea, K., and Li, Y. 2007. Social bookmarking in digital library systems: framework and case study, in Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries, Vancouver, BC, Canada, 488-488.
[28] Resnick, P., Kuwabara, K., Zeckhauser, R., and Friedman, E. 2000. Reputation systems, Communications of the ACM, vol. 43, no. 12, 45-48.
[29] Salton, G., and McGill, M. J. 1986. Introduction to Modern Information Retrieval:
McGraw-Hill, Inc.
[30] Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. 2000. Analysis of recommendation algorithms for e-commerce, in Proceedings of the 2nd ACM conference on Electronic commerce, Minneapolis, Minnesota, United States, 158-167.
[31] Sarwar, B., Karypis, G., Konstan, J., and Reidl, J. 2001. Item-based collaborative filtering recommendation algorithms, in Proceedings of the 10th international conference on World Wide Web, Hong Kong, Hong Kong.
40
[32] Sood, S., Owsley, S., Hammond, K., and Birnbaum, L. 2007. TagAssist:
Automatic Tag Suggestion for Blog Posts, in International Conference on Weblogs and Social (ICWSM).
[33] Sundaresan, N. 2007. Online trust and reputation systems, in Proceedings of the 8th ACM conference on Electronic commerce, San Diego, California, USA, 366-367.
[34] Tanaka, K. 2009. Web search and information credibility analysis: bridging the gap between Web1.0 and Web2.0, in Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication, Suwon, Korea, 39-44.
[35] Tsai, P.-Y., and Liu, D.-R. 2009. Personalized Popular Blog Recommender Service for Mobile Applications, in Proceedings of the 10th International Conference on E-Commerce and Web Technologies, Linz, Austria, 2-13.
[36] Tsai, T. M., Chia, C. S., and Chou, S.-C. T. 2006. Personalized Blog Recommendation Using the Value, Semantic, and Social Model, in Proceedings of the innovations in information Technology, Dubai 1-5.
[37] Van Rijsbergen, C. J. 1979. Information Retrieval, 2nd ed., London: Butterworth-Heinemann.
[38] Zhang, J., Ackerman, M. S., Adamic, L., and Nam, K. K. 2007. QuME: a mechanism to support expertise finding in online help-seeking communities, in Proceedings of the 20th annual ACM symposium on User interface software and technology, Newport, Rhode Island, USA, 111-114.