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整合主题模型与合着网路进行学术文献的推荐

【中文摘要】:许多文献资料库系统使用内容导向技术(content-based)撷取文章给使用者,内容导向技术是根据使用者提供的关键字来搜寻文章。另一方面,许多的推荐系统技术根据使用者的长期浏览或交易历史记录来推荐符合使用者长期的爱好,然而在文献资料库系统,通常只拥有使用者短期的爱好并且感兴趣的文章通常数量不多。在过去研究已经使用,例如:文

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章内容、使用者记录档与合着网路(coauthorship network),推荐文章给使用者以满足短期的爱好。
在本研究,我们整合学者之间所合作文章的主题资讯至合作网路来扩展整个共同作者网路。更具体地说明,我们提出以潜藏狄利克里分配为基础的合着

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网路 (LDA-coauthorship-network-based),此技术使用潜藏狄利克里分配(latent Dirichlet allocation, LDA)与任务导向(task-focused)技术做为文献的推荐技术。实验结果显示我们的方法比传统的合着网路在所有的实验环境都更有效,与内容导向技术相比,当每个任务档(task profile)包含的内容相似度非常相近时,我们的方法比内容导向技术好,但任务档的内容相似度低时,我们的方法结果较差。因此我们进一步发展一套混合方法,可自动切换内容导向与潜藏狄利克里分配为基础的合着网路。此方法可根据任务档中内容相似程度来进行切换至最适合的方法。实验结果显示了混合方法在所有实验环境都表现最优。
【英文摘要】:Most literature database systems use content-based technique to retrieve articles to users. However, the content-based technique relies on exact keywords provided by users to search for articles the users are interested in. On the other hand, most recommender system techniques are based on user’s long-term browsing/transaction history so as to recommend items that meet users’ long term interest. However, in literature database system, users’ information need is often short-term. Previous works in recommending articles to satisfy users’ short-term interest have util

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ized article content, usage log, and coauthorship network.
In this study, we extend coauthorship network method and incorporate scholars’ collaboration topics into the coauthorship network. Specifically, we propose a LDA-coauthorship-network-based technique that integrates topic information into links of the coauthorship network using latent Dirichlet allocation (LDA), and a task-focused (short-term) technique is proposed for recommending literature articles. Experimental results show that the proposed approach is more effective than the traditional coauthorship network method under all operating regions. When compared to the content-based technique, it has better performance when each task profile contains articles that are similar in their content but is less effective otherwise. We further develop a hybrid method that switches between content-based technique and LDA-coauthorship-network-based technique based on the content coherence of a task profile. Experimental results show that the hybrid method outperforms all the other methods under all operating regions.
【参考文献】:

  • Adamic, L. A., & Adar, E. (2003). Friends and neighbors on the web. Social networks, 25(3), 211-230.
  • Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749.
  • Balabanović, M., & Shoham, Y. (1997). Fab: content-based, collaborative recommendation. Communications of the ACM, 40(3), 66-72.
  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993-1022.
  • Boyd-Graber, J., Chang, J., Gerrish, S., Wang, C., & Blei, D. (2009). Reading tea leaves: How humans interpret topic models. Paper presented at the Advances in Neural Information Processing Systems (NIPS).
  • Counts, S., & Geraci, J. (2005). Incorporating physical co-presence at events into digital social networking. Paper presented at the CHI ”05 extended abstracts on Human factors in computing systems.
  • Davis, G. F., Yoo, M., & Baker, W. E. (2003). The small world of the American corporate elite, 1982-2001. Strategic organization, 1(3), 301-326.
  • Domingos, P., & Richardson, M. (2001). Mining the network value of customers. Paper presented at the Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining.
  • Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1), 5228.
  • Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1), 5-53.
  • Hwang, S. Y., Wei, C. P., Huang, Y., & Tang, Y. (2010). Combining Coauthorship Network and Content for Literature Recommendation. Proc. Of Pacific-Asia Conference on Information Systems (PACIS2010).
  • Hwang, S. Y., Wei, C. P., & Liao, Y. F. (2010). Coauthorship networks and academic literature recommendation. Electronic Commerce Research and Applications, 9(4), 323-334.
  • Linden, G., Smith, B., & York, J. (2003). Amazon. com recommendations: Item-to-item collaborative filtering. Internet Computing, IEEE, 7(1), 76-80.
  • Liu, X., Bollen, J., Nelson, M. L., & Van de Sompel, H. (2005). Co-authorship networks in the digital library research community. Information Processing & Management, 41(6), 1462-1480.
  • Lynch, C. (2001). Personalization and recommender systems in the larger context: New directions and research questions. Paper presented at the Second DELOS Network of Excellence Workshop on Personalisation and Recommender Systems in Digital Libraries.
  • Matsuo, Y., Tomobe, H., Hasida, K., & Ishizuka, M. (2004). Finding social network for trust calculation. Paper presented at the Proceedings of the 16th European Conference on Artificial Intelligence.
  • Miller, B. N., Albert, I., Lam, S. K., Konstan, J. A., & Riedl, J. (2003). MovieLens unplugged: experiences with an occasionally connected recommender system. Paper presented at the Proceedings of the 8th international conference on Intelligent user interfaces.
  • Mobasher, B., Dai, H., Luo, T., & Nakagawa, M. (2001). Effective personalization based on association rule discovery from web usage data. Paper presented at the In Proceedings of WIDM 2001.
  • Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404.
  • Rosen-Zvi, M., Chemudugunta, C., Griffiths, T., Smyth, P., & Steyvers, M. (2010). Learning author-topic models from text corpora. ACM Transactions on Information Systems (TOIS), 28(1), 4.
  • Rosen-Zvi, M., Griffiths, T., Steyvers, M., & Smyth, P. (2004). The author-topic model for authors and documents. Paper presented at the Proceedings of the 20th conference on Uncertainty in artificial intelligence.
  • Shen, X., Tan, B., & Zhai, C. (2005). Context-sensitive information retrieval using implicit feedback. Paper presented at the Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval.
  • Wei, C. P., Shaw, M. J., & Easley, R. F. (2002). Recommendation Systems in Electronic Commerce. E-Service: new directions in theory and practice, 168.
  • Wei, X., & Croft, W. B. (2006). LDA-based document models for ad-hoc retrieval. Paper presented at the Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval.
  • Yang, Y., & Pedersen, J. O. (1997). A comparative study on feature selection in text categorization. Paper presented at the International Conference on Machine Learning (ICML).
  • Yoshikane, F., & Kageura, K. (2004). Comparative analysis of coauthorship networks of different domains: The growth and change of networks. Scientometrics, 60(3), 435-446.
  • Zacharia, G., Moukas, A., & Maes, P. (2000). Collaborative reputation mechanisms for electronic marketplaces. Decision Support Systems, 29(4), 371-388.
  • 来源:中山大学;作者:陈裕翔
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