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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳建錦(Chien-Chin Chen) | |
| dc.contributor.author | Ming-Chen Wu | en |
| dc.contributor.author | 吳洺甄 | zh_TW |
| dc.date.accessioned | 2021-06-17T03:21:40Z | - |
| dc.date.available | 2021-07-23 | |
| dc.date.copyright | 2018-07-23 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-06-21 | |
| dc.identifier.citation | Adomavicius, G., & Kwon, Y. (2012). Improving aggregate recommendation diversity using ranking-based techniques. IEEE Transactions on Knowledge and Data Engineering, 24(5), 896-911.
Aggarwal, C. C. (2016). Recommender systems: Springer. Alpaydin, E. (2014). Introduction to machine learning: MIT press. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022. Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Paper presented at the Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence. Chen, C. C., Wan, Y.-H., Chung, M.-C., & Sun, Y.-C. (2013). An effective recommendation method for cold start new users using trust and distrust networks. Information Sciences, 224, 19-36. Chen, H., & Karger, D. R. (2006). Less is more: probabilistic models for retrieving fewer relevant documents. Paper presented at the Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. Dewan, R., Jing, B., & Seidmann, A. (2000). Adoption of Internet-based product customization and pricing strategies. Journal of Management Information Systems, 17(2), 9-28. Fürnkranz, J., Hüllermeier, E., Mencía, E. L., & Brinker, K. (2008). Multilabel classification via calibrated label ranking. Machine learning, 73(2), 133-153. Gemman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs Distributions, and The Bayesian of Images. IEEE Trans. Pattern Anal. Mach. Intelligence, 6. Gemulla, R., Nijkamp, E., Haas, P. J., & Sismanis, Y. (2011). Large-scale matrix factorization with distributed stochastic gradient descent. Paper presented at the Proceedings of the 17th 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, 101(suppl 1), 5228-5235. Guo, G., Zhang, J., & Yorke-Smith, N. (2013). A Novel Bayesian Similarity Measure for Recommender Systems. Paper presented at the IJCAI. He, X., Zhang, H., Kan, M.-Y., & Chua, T.-S. (2016). Fast matrix factorization for online recommendation with implicit feedback. Paper presented at the Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. Jiang, Y., Shang, J., & Liu, Y. (2010). Maximizing customer satisfaction through an online recommendation system: A novel associative classification model. Decision Support Systems, 48(3), 470-479. Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Paper presented at the Ijcai. Koren, Y. (2008). Factorization meets the neighborhood: a multifaceted collaborative filtering model. Paper presented at the Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8). Lai, H.-J., Liang, T.-P., & Ku, Y.-C. (2003). Customized internet news services based on customer profiles. Paper presented at the Proceedings of the 5th international conference on Electronic commerce. Larson, R. R. (2010). Introduction to information retrieval. Journal of the American Society for Information Science and Technology, 61(4), 852-853. Li, S., Kawale, J., & Fu, Y. (2015). Deep collaborative filtering via marginalized denoising auto-encoder. Paper presented at the Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Liang, T.-P., Lai, H.-J., & Ku, Y.-C. (2006). Personalized content recommendation and user satisfaction: Theoretical synthesis and empirical findings. Journal of Management Information Systems, 23(3), 45-70. Linden, G., Smith, B., & York, J. (2003). Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing, 7(1), 76-80. Liu, T.-Y. (2009). Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval, 3(3), 225-331. Lops, P., De Gemmis, M., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. In Recommender systems handbook (pp. 73-105): Springer. Mitchell, T. M. (1997). Machine learning. 1997. Burr Ridge, IL: McGraw Hill, 45(37), 870-877. Mnih, A., & Salakhutdinov, R. R. (2008). Probabilistic matrix factorization. Paper presented at the Advances in neural information processing systems. Pessiot, J.-F., Truong, V., Usunier, N., Amini, M., & Gallinari, P. (2007). Learning to rank for collaborative filtering. Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2009). BPR: Bayesian personalized ranking from implicit feedback. Paper presented at the Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). GroupLens: an open architecture for collaborative filtering of netnews. Paper presented at the Proceedings of the 1994 ACM conference on Computer supported cooperative work. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000). Application of dimensionality reduction in recommender system-a case study. Retrieved from Schafer, J. B., Konstan, J., & Riedl, J. (1999). Recommender systems in e-commerce. Paper presented at the Proceedings of the 1st ACM conference on Electronic commerce. Sharma, A., & Yan, B. (2013). Pairwise learning in recommendation: experiments with community recommendation on linkedin. Paper presented at the Proceedings of the 7th ACM Conference on Recommender Systems. Voorhees, E. M. (1999). The TREC-8 Question Answering Track Report. Paper presented at the Trec. Xie, W., Dong, Q., & Gao, H. (2014). A probabilistic recommendation method inspired by latent dirichlet allocation model. Mathematical Problems in Engineering, 2014. Zhang, B.-T., & Seo, Y.-W. (2001). Personalized web-document filtering using reinforcement learning. Applied Artificial Intelligence, 15(7), 665-685. Zhang, Y., Chen, M., Huang, D., Wu, D., & Li, Y. (2017). iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization. Future Generation Computer Systems, 66, 30-35. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/69629 | - |
| dc.description.abstract | 現今的網路服務或電子商務提供了非常多的商品與選擇，也因此當使用者在搜尋想要的商品時，有效的推薦系統是非常重要的。在本文中，我們提出了一種基於圖形理論模型的推薦方法，將成對學習的概念納入隱含狄利克雷分布模型中，以挖掘使用者對於項目優先級的偏好。再透過投票機制將使用者對項目的偏好進行積分排序，最後產生出針對每位使用者的個人化推薦清單。由真實世界之數據資料的實驗表明，我們的模型所得出的使用者偏好在項目的推薦中是有效的。此外，結合成對學習的方法在推薦的準確度方面也成功地增強了基於隱含狄利克雷分布的推薦系統。 | zh_TW |
| dc.description.abstract | Internet e-services now provide so many items that effective recommendation systems are crucial to users to search for desire items. In this paper, we present a new recommendation method which is based on theoretical graphical models. We incorporate the concept of pairwise learning into the latent Dirichlet allocation model to discover user preferences which differentiate users’ precedence on items. A voting mechanism applied to the learned user preferences is devised so that favorite items are suggested to the users. Preliminary experiments based on a real-world dataset demonstrate that the discovered user preferences are effective in item recommendations. Also, incorporating pairwise learning successfully enhances the LDA based recommendation method in terms of the recommendation precision. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T03:21:40Z (GMT). No. of bitstreams: 1 ntu-107-R05725030-1.pdf: 1920515 bytes, checksum: 53300e2f18f1e82402aaf50692cca236 (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
摘要 ii ABSTRACT iii Chapter 1. INTRODUCTION 1 Chapter 2. RELATED WORKS 4 2.1 Recommendation Systems 4 2.2 Collaborative Filtering 4 2.3 Pairwise Learning to Rank 5 2.4 LDA Based Recommendation Systems 6 Chapter 3. THE PAIRWISE LDA BASED RECOMMENDATION SYSTEM 7 3.1 Preference Learning 8 3.2 Recommendation Generation 11 Chapter 4. EXPERIMENT 13 4.1 Dataset and Evaluation Metrics 13 4.2 Effect of System Parameters 15 4.3 Comparisons with Other Recommendation Methods 17 Chapter 5. CONCLUSIONS 22 REFERENCES 23 | |
| dc.language.iso | en | |
| dc.subject | 推薦系統 | zh_TW |
| dc.subject | 成對學習 | zh_TW |
| dc.subject | 隱含狄利克雷分布 | zh_TW |
| dc.subject | 偏好學習 | zh_TW |
| dc.subject | 排序學習 | zh_TW |
| dc.subject | 推薦系統 | zh_TW |
| dc.subject | 成對學習 | zh_TW |
| dc.subject | 隱含狄利克雷分布 | zh_TW |
| dc.subject | 偏好學習 | zh_TW |
| dc.subject | 排序學習 | zh_TW |
| dc.subject | Preference Learning | en |
| dc.subject | Latent Dirichlet Allocation | en |
| dc.subject | Latent Dirichlet Allocation | en |
| dc.subject | Pairwise Learning | en |
| dc.subject | Pairwise Learning | en |
| dc.subject | Recommendation Systems | en |
| dc.subject | Learning to Rank | en |
| dc.subject | Recommendation Systems | en |
| dc.subject | Learning to Rank | en |
| dc.subject | Preference Learning | en |
| dc.title | 結合成對學習與隱含狄利克雷分布之項目推薦 | zh_TW |
| dc.title | Incorporating Pairwise Learning into Latent Dirichlet Allocation for Effective Item Recommendations | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳孟彰(Meng-Chang Chen),張詠淳(Yung-Chun Chang) | |
| dc.subject.keyword | 推薦系統,排序學習,偏好學習,隱含狄利克雷分布,成對學習, | zh_TW |
| dc.subject.keyword | Recommendation Systems,Learning to Rank,Preference Learning,Latent Dirichlet Allocation,Pairwise Learning, | en |
| dc.relation.page | 26 | |
| dc.identifier.doi | 10.6342/NTU201801044 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-06-22 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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| ntu-107-1.pdf 未授權公開取用 | 1.88 MB | Adobe PDF |
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