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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 林守德 | |
dc.contributor.author | Yi-Lin Tsai | en |
dc.contributor.author | 蔡易霖 | zh_TW |
dc.date.accessioned | 2021-06-07T17:58:40Z | - |
dc.date.copyright | 2012-08-15 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2012-08-09 | |
dc.identifier.citation | [1] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl: “Item-based Collaborative Filtering Recommendation Algorithms,” Proceedings of the 10th International Conference on World Wide Web, 2001.
[2] N. Lathia, S. Hailes, and L. Capra: “kNN CF: A Temporal Social Network,” ACM Conference on Recommender Systems, pp. 227–234, 2008. [3] R. M. Bell and Y. Koren: “Scalable collaborative filtering with jointly derived neighborhood interpolation weights.” ICDM-07, 2007. [4] Y. Koren. “The bellkor solution to the Netflix grand prize.” Netflix prize documentation, 2009. [5] M. Piotte and M. Chabbert. “The pragmatic theory solution to the Netflix grand prize”. Netflix prize documentation, 2009. [6] A. Tscher, M. Jahrer, and R. Bell. “The bigchaos solution to the Netflix grand prize.” Netflix prize documentation, 2009. [7] L. Tucker. “Some mathematical notes on three-mode factor analysis.” Psychometrika, 31:279–311, 1966. [8] P. Symeonidis, A. Nanopoulos, and Y. Manolopoulos. “Tag recommendations based on tensor dimensionality reduction”. ACM conference on Recommender systems 2008. [9] S. Rendle, L. B. Marinho, A. Nanopoulos, and L. Schmidt-Thieme. “Learning optimal ranking with tensor factorization for tag recommendation.” Proceeding of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, 2009. [10] J. Carroll and J. Chang. “Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-young” decomposition.” Psychometrika, 35:283–319, 1970. [11] L. D. Lathauwer, B. D. Moor, and J. Vandewalle. “A multilinear singular value decomposition”. SIAM J. Matrix Anal. Appl., 21(4):1253–1278, 2000. [12] J. Malinowski, T. Keim, O. Wendt, and T. Weitzel. “Matching people and jobs: A bilateral recommendation approach.” In Proceedings of the 39th Annual Hawaii International Conference on System Sciences, volume 6, page 137c, 2006. [13] Lukas Brozovsky, Vaclav Petricek. “Recommender system for online dating service.” CoRR, abs/cs/0703042, 2007 [14] O. Celma and P. Cano.” From hits to niches?: or how popular artists can bias music recommendation and discovery.” In NETFLIX ’08: Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition, pages 18, New York, NY, USA, 2008. [15] L. Pizzato, T. Rej, T. Chung, K. Yacef, I. Koprinska, and J. Kay. “Reciprocal recommenders.” Technical Report 651, School of IT, University of Sydney, 2010. [16] L. Brozovsky, V. Petricek, “Recommender system for online dating service”, In Proceedings of Znalosti 2007 Conference, VSB, Ostrava, 2007. [17] Keim, T. Extending the Applicability of Recommender Systems, “A Multilayer Framework for Matching Human Resources”. In proceedings of 39th Hawaii International Conference on System Sciences, 2007. [18] L. Pizzato, T. Rej, T. Chung, I. Koprinska, and J. Kay. “RECON: A reciprocal recommender for online dating”. RecSys, 2010. [19] Akehurst, J., Koprinska, I., Yacef, K., Pizzato, L., Kay, J., Rej, T., “CCR - A Content-Collaborative Reciprocal Recommender for Online Dating”. In: proceeding of the 22nd International Joint Conference on Artificial Intelligence (IJCAI). 2011. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16031 | - |
dc.description.abstract | 推薦系統通常基於使用者的喜好而推薦物品給使用者,雙向推薦系統卻是將使用者推薦給另一名使用者,而且希望能使他們互相滿意對方而達到高的配對率。實際上的應用有線上求職或線上交友系統,這些網站嘗試幫他們的顧客配對但卻無法使用傳統的推薦系統來達成目的,因為傳統的推薦系統只能推薦使用者可能喜歡的列表,無法保證使用者也會被喜歡。為了解決這個問題,我們提出了一個基於矩陣分解模型的新方法並且實驗在兩個真實的資料集上:分別是線上交友網站(libimseti)以及線上求職網站(104人力銀行)。我們模型中的核心概念是調整MF模型中找區域最佳解的學習方式(SGD函式),試著讓可能配對的雙方都出現在對方的推薦列表中。根據實驗結果,我們的模型在AUC的評估準則中表現的比傳統MF模型以及一些目前發展最好的雙向推薦系統還要好。換句話說,我們的模型可以藉由推薦讓使用者更容易找到適合的配對。 | zh_TW |
dc.description.abstract | Recommendation systems usually recommend items based on user’s preference, but reciprocal recommendation systems recommend people to people and try to make them match. Applications like online-job-hunting or online-dating. They match their customers but can’t profit from traditional recommendation system, because we don’t know whether the recommendation terms like us or not. To solve this problem, we provide a Matrix Factorization (MF) based model and experiment with two real-world dataset, one is from online-dating website (libimseti), one is from an online employment system (104 human resource bank). The core concept of our model is to adjust the learning strategy of MF (SGD function), trying to re-rank the recommendation list of two potential match users. By experiment result, our model gets better AUC (area under curve) than traditional MF model and some state-of-the-art reciprocal recommenders. In other words, we can make higher match ratio from recommendations. | en |
dc.description.provenance | Made available in DSpace on 2021-06-07T17:58:40Z (GMT). No. of bitstreams: 1 ntu-101-R99922099-1.pdf: 721225 bytes, checksum: 68af759dd848fe307deeec6ffdea1d47 (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES vii Chapter 1 Introduction 1 1.1 Background 1 1.2 Contributions 2 1.3 Thesis Organization 3 Chapter 2 Related Work 4 2.1 Traditional Recommendation System 4 2.1.1 Collaborative Filtering 5 2.1.2 k-Nearest Neighbor (kNN) 5 2.1.3 Matrix Factorization (MF) 6 2.1.4 Tensor Factorization 7 2.2 Reciprocal Recommendation System 8 Chapter 3 Methodology 10 3.0 Problem definition 10 3.1 Basic Model introduction 10 3.1.1 Matrix Factorization (MF) 11 3.1.2 Tensor Factorization (TF) 12 3.2 Reciprocal approach 14 3.2.1 Stochastic Gradient Descent (SGD) 15 3.2.2 Reciprocal Regularized SGD function 15 Chapter 4 Experiment 17 4.1 Dataset 17 4.1.1 Online dating: Libimseti 17 4.1.2 Human resource: 104 18 4.1.3 Validation 19 4.1.4 Evaluation 20 4.1.5 Ground truth 20 4.2 Performance issues 22 4.2.1 Reading Meta data as better initialization value 22 4.2.2 Learning match ratio 22 4.2.3 Regularized term 23 4.3 Baseline 24 4.3.1 Recommend the most popular terms 24 4.3.2 Stochastic Matching model 24 4.4 Result and Discussion 26 Chapter 5 Conclusion and Future Work 30 REFERENCE 32 | |
dc.language.iso | en | |
dc.title | 基於矩陣分解的雙向推薦系統 | zh_TW |
dc.title | A Factorization-based Reciprocal Recommendation System | en |
dc.type | Thesis | |
dc.date.schoolyear | 100-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳建錦,劉昭麟,蔡明峰 | |
dc.subject.keyword | 推薦系統,雙向推薦系統,協同式過濾,矩陣分解,張量矩陣分解, | zh_TW |
dc.subject.keyword | recommendation system,reciprocal recommendation system,collaborative filtering,Matrix Factorization,Tensor Factorization, | en |
dc.relation.page | 34 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2012-08-10 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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