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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張智星 | zh_TW |
dc.contributor.advisor | Jyh-Shing Roger Jang | en |
dc.contributor.author | 吳偉樂 | zh_TW |
dc.contributor.author | Wei Luok Ngu | en |
dc.date.accessioned | 2023-01-10T17:16:26Z | - |
dc.date.available | 2023-11-10 | - |
dc.date.copyright | 2023-01-07 | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2002-01-01 | - |
dc.identifier.citation | [1] F. Ricci, L. Rokach, and B. Shapira, “Introduction to recommender systems handbook,” in Recommender Systems Handbook. Springer, 2011, pp. 1–35.
[2] W. Pan, M. Liu, and Z. Ming, “Transfer learning for heterogeneous one-class collaborative filtering,” in Proceedings of the 8th IEEE Intelligent Systems, 2016, pp. 43–49. [3] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th International Conference on World Wide Web, 2001, pp. 285–295. [4] Y. Hu, Y. Koren, and C. Volinsky, “Collaborative filtering for implicit feedback datasets,” in Proceedings of the 8th IEEE International Conference on Data Mining, 2008, pp. 263–272. [5] S. Kabbur, X. Ning, and G. Karypis, “Fism: Factored item similarity models for topn recommender systems,” in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013, pp. 659–667. [6] R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec, “Graph convolutional neural networks for web-scale recommender systems,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018, pp. 974–983. [7] S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme, “Bpr: Bayesian personalized ranking from implicit feedback,” arXiv preprint arXiv:1205.2618, 2012. [8] R. v. d. Berg, T. N. Kipf, and M. Welling, “Graph convolutional matrix completion,” arXiv preprint arXiv:1706.02263, 2017. [9] X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang, “Lightgcn: Simplifying and powering graph convolution network for recommendation,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, pp. 639–648. [10] X. Wang, H. Jin, A. Zhang, X. He, T. Xu, and T.-S. Chua, “Disentangled graph collaborative filtering,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, pp. 1001–1010. [11] L. Xia, C. Huang, Y. Xu, P. Dai, B. Zhang, and L. Bo, “Multiplex behavioral relation learning for recommendation via memory augmented transformer network,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, pp. 2397–2406. [12] L. Xia, C. Huang, Y. Xu, P. Dai, X. Zhang, H. Yang, J. Pei, and L. Bo, “Knowledgeenhanced hierarchical graph transformer network for multi-behavior recommendation,” in Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021, pp. 4486–4493. [13] B. Jin, C. Gao, X. He, D. Jin, and Y. Li, “Multi-behavior recommendation with graph convolutional networks,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, pp. 659– 668. [14] B. Loni, R. Pagano, M. Larson, and A. Hanjalic, “Bayesian personalized ranking with multi-channel user feedback,” in Proceedings of the 10th ACM Conference on Recommender Systems, 2016, pp. 361–364. [15] H. Qiu, Y. Liu, G. Guo, Z. Sun, J. Zhang, and H. T. Nguyen, “Bprh: Bayesian personalized ranking for heterogeneous implicit feedback,” in Information Sciences, vol. 453, 2018, pp. 80–98. [16] C. Chen, M. Zhang, Y. Zhang, W. Ma, Y. Liu, and S. Ma, “Efficient heterogeneous collaborative filtering without negative sampling for recommendation,” in Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020, pp. 19–26. [17] C. Chen, W. Ma, M. Zhang, Z. Wang, X. He, C. Wang, Y. Liu, and S. Ma, “Graph heterogeneous multi-relational recommendation,” in Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021, pp. 3958–3966. [18] Y. Koren, “Factorization meets the neighborhood: a multifaceted collaborative filtering model,” in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, 2008, pp. 426–434. [19] R. He and J. McAuley, “Vbpr: visual bayesian personalized ranking from implicit feedback,” in Proceedings of the 30th AAAI Conference on Artificial Intelligence, 2016, pp. 144–150. [20] X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, “Neural collaborative iltering,” in Proceedings of the 26th International Conference on World Wide Web, 2017, pp. 173–182. [21] H.-J. Xue, X. Dai, J. Zhang, S. Huang, and J. Chen, “Deep matrix factorization models for recommender systems.” in Proceedings of the 26th International Joint Conference on Artificial Intelligence, 2017, pp. 3203–3209 [22] Z.-H. Deng, L. Huang, C.-D. Wang, J.-H. Lai, and S. Y. Philip, “Deepcf: A unified framework of representation learning and matching function learning in recommender system,” in Proceedings of the 33th AAAI Conference on Artificial Intelligence, vol. 33, no. 01, 2019, pp. 61–68. [23] J. Chen, H. Zhang, X. He, L. Nie, W. Liu, and T.-S. Chua, “Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention,” in Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017, pp. 335–344. [24] X. Wang, X. He, M. Wang, F. Feng, and T.-S. Chua, “Neural graph collaborative filtering,” in Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019, pp. 165–174. [25] A. Krohn-Grimberghe, L. Drumond, C. Freudenthaler, and L. Schmidt-Thieme, “Multi-relational matrix factorization using bayesian personalized ranking for social network data,” in Proceedings of the 5th ACM International Conference on Web Search and Data Mining, 2012, pp. 173–182. [26] L. Tang, B. Long, B.-C. Chen, and D. Agarwal, “An empirical study on recommendation with multiple types of feedback,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 283–292. [27] A. P. Singh and G. J. Gordon, “Relational learning via collective matrix factorization,” in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008, pp. 650 [28] Z. Zhao, Z. Cheng, L. Hong, and E. H. Chi, “Improving user topic interest profiles by behavior factorization,” in Proceedings of the 24th International Conference on World Wide Web, 2015, pp. 1406–1416. [29] J. Ding, G. Yu, X. He, Y. Quan, Y. Li, T.-S. Chua, D. Jin, and J. Yu, “Improving implicit recommender systems with view data,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2018, pp. 3343–3349. [30] C. Gao, X. He, D. Gan, X. Chen, F. Feng, Y. Li, T.-S. Chua, and D. Jin, “Neural multi-task recommendation from multi-behavior data,” in Proceedings of the 35th IEEE International Conference on Data Engineering, 2019, pp. 1554–1557. [31] W. Wei, C. Huang, L. Xia, Y. Xu, J. Zhao, and D. Yin, “Contrastive meta learning with behavior multiplicity for recommendation,” in Proceedings of the 15th ACM International Conference on Web Search and Data Mining, 2022, pp. 1120–1128. [32] J.-H. Yang, C.-M. Chen, C.-J. Wang, and M.-F. Tsai, “Hop-rec: High-order proximity for implicit recommendation,” in Proceedings of the 12th ACM Conference on Recommender Systems, 2018, pp. 140–144. [33] C.-M. Chen, C.-J. Wang, M.-F. Tsai, and Y.-H. Yang, “Collaborative similarity embedding for recommender systems,” in In Proceedings of the 28th International Conference on World Wide Web, 2019, pp. 2637–2643. [34] S.-Y. Liu, H. H. Chen, C.-M. Chen, M.-F. Tsai, and C.-J. Wang, “Ipr: Interactionlevel preference ranking for explicit feedback,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022, pp. 1912–1916 [35] Y.-N. Chuang, C.-M. Chen, C.-J. Wang, M.-F. Tsai, Y. Fang, and E.-P. Lim, “Tpr: Text-aware preference ranking for recommender systems,” in Proceedings of the 29th ACM International Conference on Information and Knowledge Management, 2020, pp. 215–224. [36] B. Recht, C. Re, S. Wright, and F. Niu, “Hogwild!: A lock-free approach to parallelizing stochastic gradient descent,” in Proceedings of the 24th International Conference on Neural Information Processing Systems, 2011, pp. 693–701. [37] W. Krichene and S. Rendle, “On sampled metrics for item recommendation,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2020, pp. 1748–1757. [38] X. Luo, D. Wu, C. Chen, J. Ma, M. Deng, C. Shen, J. Huang, and X.-S. Hua, “Criterion-based heterogeneous collaborative filtering for multi-behavior implicit recommendation,” arXiv preprint arXiv:2105.11876, 2021. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83197 | - |
dc.description.abstract | 多行為推薦的目標是利用用戶及物品的多交互關係例如購買和加入購物車來進行建模以解決推薦中常見的資料稀疏及冷啟動問題。雖然最近一些基於多行為的推薦演算法成功地利用不同種類的用戶及物品交互行為來提升推薦效果,但這些方法還存在一些限制。第一,大多數開創性的工作將單一行為當作目標行為且只根據目標行為來優化模型;然而,這需要重新訓練模型以預測其它行為,因此對於大規模資料集和許多實際應用來說效率很低。第二,雖然近期有些研究透過結合所有種類的行為一起進行模型優化以解決上述問題,但模型學習到的行為向量是所有用戶及物品都共用的,這樣的設定非常粗糙且不足以補抓用戶在不同行為下的偏好。除此之外,雖然這些最先進的多行為推薦演算法看似能對針對不同的行為對用戶推薦商品,但其它行為的預測並沒有被明確地評估在相關論文。我們透過使用個性化多互動偏好排名(PMiPR)來解決這些限制,它是應用於多行為推薦中有效及高效向量學習框架。具體來說,PMiPR 透過學習用戶及物品在每種行為下的特定行為向量將多行為信息整合至建模過程中。這不僅以更細粒度的方式對多行為信息建模,也讓我們能透過利用為指定的用戶及物品的行為向量來對不同的行為進行推薦。在四個公開的基準資料集上進行的綜合實驗證明了 PMiPR 在多行為推薦的有效性及效能。 | zh_TW |
dc.description.abstract | The goal of multi-behavior recommendation is to leverage user-item interactions such as purchase and add-to-cart into the modeling process to address the commonly-faced data sparsity or cold start issues in recommendation. Although some recent multi-behavior-based recommendation algorithms successfully leverage different types of user-item interactions to improve recommendation performance, these methods still have limitations. First, most pioneering works treat a single behavior as the target behavior and optimize the model based on the target behavior only; this however necessitates re-training of the model to predict other behaviors and is thus inefficient for large-scale datasets and many real-world applications. Second, although recent studies address this issue by jointly optimizing the model based on all types of behaviors, the learned behavior embeddings are shared across all users and items, which is coarse-grained and insufficient to capture user preferences under different behaviors. Moreover, although such state-of-the-art multi-behavior recommendation algorithms seem able to recommend items for users w.r.t. different behaviors, they do not explicitly evaluate their methods in the reported experiments. We address these limitations with personal- ized multi-interaction preference ranking (PMiPR), an effective and efficient embedding learning framework for multi-behavior recommendation. Specifically, the proposed PMiPR incorporates multi-behavioral information into the modeling process by learning user-specific and item-specific behavior embeddings for each type of behavior. This not only models multi-behavioral information in a more fine-grained way but enables us to make recommendations w.r.t. different behaviors by leveraging the designated behavior embeddings for users and items. Comprehensive experiments on four public benchmark datasets demonstrate the effectiveness and efficiency of PMiPR for multi-behavior recommendation. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-01-10T17:16:26Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-01-10T17:16:26Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 誌謝 ii
摘要 iii Abstract iv 1 Introduction 1 1.1 Research Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Chapter Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Literature Review 6 2.1 Background of Recommender System . . . . . . . . . . . . . . . . . . . 6 2.2 Single-behavior Recommendation . . . . . . . . . . . . . . . . . . . . . 7 2.3 Multi-behavior Recommendation . . . . . . . . . . . . . . . . . . . . . . 11 2.3.1 Single-task Learning . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.2 Multi-task Learning . . . . . . . . . . . . . . . . . . . . . . . . 15 3 Research Method 18 3.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2 Multi-interaction Preference Ranking . . . . . . . . . . . . . . . . . . . 20 3.3 Embedding Matrix Learning . . . . . . . . . . . . . . . . . . . . . . . . 22 3.4 Sampling Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.5 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.6 Global Behavior Embedding . . . . . . . . . . . . . . . . . . . . . . . . 26 3.7 Scoring Function for Multi-behavior Recommendation . . . . . . . . . . 27 4 Experimental Setup 28 4.1 Roadmap for experiments . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.2 Experimental Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.2.1 Datasets Description . . . . . . . . . . . . . . . . . . . . . . . . 29 4.2.2 Datasets Preprocessing . . . . . . . . . . . . . . . . . . . . . . . 30 4.2.3 Baselines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.2.4 Parameters Settings . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.2.5 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . 33 5 Experimental Results 35 5.1 Experiment 1: Overall Performance Comparison on Various Behavior . . . 35 5.1.1 Purchase Recommendation . . . . . . . . . . . . . . . . . . . . . 40 5.1.2 Cart and View Recommendation . . . . . . . . . . . . . . . . . . 41 5.2 Experiment 2: Ablation Studies on Global Behavior Embeddings . . . . . 42 5.3 Experiment 3: Sensitivity Analysis on Hyperparameters . . . . . . . . . . 43 5.4 Experiment 4: Computational Efficiency Comparison . . . . . . . . . . . 45 6 Conclusion and Future Work 47 6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Bibliography 49 | - |
dc.language.iso | zh_TW | - |
dc.title | 使用個性化多互動偏好排名的多行為推薦系統 | zh_TW |
dc.title | Personalized Multi-interaction Preference Ranking for Multi-behavior Recommendation | en |
dc.title.alternative | Personalized Multi-interaction Preference Ranking for Multi-behavior Recommendation | - |
dc.type | Thesis | - |
dc.date.schoolyear | 111-1 | - |
dc.description.degree | 碩士 | - |
dc.contributor.coadvisor | 王釧茹 | zh_TW |
dc.contributor.coadvisor | Chuan-Ju Wang | en |
dc.contributor.oralexamcommittee | 蔡銘峰;黃瀚萱 | zh_TW |
dc.contributor.oralexamcommittee | Ming-Feng Tsai;Hen-Hsen Huang | en |
dc.subject.keyword | 協同過濾,多行為推薦,偏好排序, | zh_TW |
dc.subject.keyword | Collaborative filtering,Multi-Behavior Recommendation,Preference ranking, | en |
dc.relation.page | 54 | - |
dc.identifier.doi | 10.6342/NTU202203790 | - |
dc.rights.note | 同意授權(全球公開) | - |
dc.date.accepted | 2022-09-25 | - |
dc.contributor.author-college | 電機資訊學院 | - |
dc.contributor.author-dept | 資料科學學位學程 | - |
顯示於系所單位: | 資料科學學位學程 |
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