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題名 探討推薦系統之高階關係影響
Exploring High-Order Relations for Recommender Systems
作者 陳志明
Chen, Chih-Ming
貢獻者 蔡銘峰<br>楊奕軒
Tsai, Ming-Feng<br>‪Yang, Yi-Hsuan
陳志明
Chen, Chih-Ming
關鍵詞 推薦系統
協同過濾
高階關係
Recommender System
Collaborative Filtering
High-Order Relation
日期 2022
上傳時間 2-Sep-2022 15:53:48 (UTC+8)
摘要 推薦系統已經被廣泛的運用在各種現實生活系統之中，這間接說明具實用性的推薦系統研究將會帶給世界更多的影響力，有鑑於此，我們開發了一個推薦系統架構名為SMORe，它不僅只是一個開發工具，而是被設計成具備跟上前沿研究開發可能的架構，基於此架構開發，它讓我們所提出的推薦模型皆能實現高效率且高準確度的預測，完全可與現存其他知名架構競爭，甚至表現地更好。
在此工作中，我們提出了一系列研究包含: 1) HPE, 2) Hop-Rec, 3) CSE, 4) IPR 等共四種協同過濾模型。這四種模型的共通特色為「利用高階關係改善推薦演算法」，請注意這些並非為獨立的研究，讀者可以透過我們提供的各個理論解釋來理解我們的演算法設計思維，更精簡的說明為，推薦系統相關資料集通常含有用戶與物品之間的關係，而高階關係指的是那些沒有被記錄的連結，在我們的演算法中，HPE利用隨機遊走的方式取得高階鄰居關係，用以融合用戶的異質興趣，Hop-Rec則利用隨機遊走的方式來區分用戶與物品之間的關聯強度，進而設計合適的最佳化方程式，CSE巧妙地利用蒐集到的高階鄰居關係來分群用戶與物品，從而提昇推薦的品質，IPR作為集大成，將常用的點對點協同過濾方程透過高階關係重新打造成邊對邊的協同 過濾方程，可用以清楚地解釋為何高階關係可以被有效利用在推薦系統演算法之中。
Recommender system is everywhere in enterprise applications nowadays. This indicates that investigating applicable research has a more significant impact on the real world. In light of this, we developed a recommendation-purpose framework named SMORe. It is not only a toolkit but also a research-capable framework for doing cutting-edge research topics. Based on the framework, the implemented proposed models can achieve high-performance and high-accuracy predictions compared to most existing solutions.
For the proposed models, we focus on the topic of high-order relations with recommendation algorithms. Specifically, we present a series of four collaborative filtering models: 1) HPE, 2) Hop-Rec, 3) CSE, and 4) IPR. Their main features are to ‘utilize high-order relation modeling for the recommendation algorithms. Note that they are not independent works. By demonstrating their theoretical analysis, the readers can understand the rationale of our proposals. In brief, a recommendation dataset contains the user-to-item edges. The high-order information modeling is an attempt to make use of the unobserved edges. In short, HPE applies random walks to retrieve high-order neighbor data to better fuse the heterogeneous preferences. Hop-Rec determines the strongness of a high-order user-to-item pair and re-shapes the corresponding loss function. CSE shows a delicate way to cluster the users and items by high-order information and simultaneously keep and improve the recommendation quality. IPR brings the conventional entity-level CF modeling to the interaction-level CF modeling using the concept of high-order relations and finally provides an intuitive explanation about why high-order information can benefit the recommendations.
參考文獻 [1] G. Adomavicius and A. Tuzhilin. Context-aware recommender systems. ACM RecSys ’08.
[2] Q. Ai, V. Azizi, X. Chen, and Y. Zhang. Learning heterogeneous knowledge base embeddings for explainable recommendation. Algorithms, 2018.
[3] T.Badriyah, E.T.Wijayanto, I.Syarif, and P.Kristalina. A hybrid recommendation system for e-commerce based on product description and user profile. INTECH ’17.
[4] O. Barkan and N. Koenigstein. Item2vec: Neural item embedding for collaborative filtering.
[5] A.Bordes, N.Usunier, A.Garcia-Dura ́n, J.Weston, and O.Yakhnenko. Translating embeddings for modeling multi-relational data. NIPS ’13. Curran Associates, Inc.
[6] C. J. C. Burges. From RankNet to LambdaRank to LambdaMART: An overview. Technical report, Microsoft Research, 2010.
[7] Y. Cen, J. Zhang, X. Zou, C. Zhou, H. Yang, and J. Tang. Controllable multi-interest framework for recommendation. ACM KDD ’20.
[8] S. Chaudhuri and A. Tewari. Online learning to rank with top-k feedback.
[9] C. Chen, M. Zhang, Y. Liu, and S. Ma. Neural attentional rating regression with review-level explanations. WWW ’18.
[10] C.-M. Chen, M.-F. Tsai, Y.-C. Lin, and Y.-H. Yang. Query-based music recommendations via preference embedding. ACM RecSys ’16.
[11] C.-M. Chen, C.-J. Wang, M.-F. Tsai, and Y.-H. Yang. Collaborative similarity embedding for recommender systems. WWW ’19.
[12] H.-T.Cheng, L.Koc, J.Harmsen, T.Shaked, T.Chandra, H.Aradhye, G.Anderson, G. Corrado, W. Chai, M. Ispir, R. Anil, Z. Haque, L. Hong, V. Jain, X. Liu, and H. Shah. Wide & deep learning for recommender systems. ACM DLRS ’16.
[13] S.-Y. Chou, Y.-H. Yang, J.-S. R. Jang, and Y.-C. Lin. Addressing cold start for next-song recommendation. ACM RecSys ’16.
[14] E. Christakopoulou and G. Karypis. Local item-item models for top-n recommendation. ACM RecSys ’16.
[15] F. Christoffel, B. Paudel, C. Newell, and A. Bernstein. Blockbusters and wallflowers: Accurate, diverse, and scalable recommendations with random walks. ACM RecSys ’15.
[16] C. Cooper, S. H. Lee, T. Radzik, and Y. Siantos. Random walks in recommender systems: Exact computation and simulations. WWW ’14.
[17] P. Covington, J. Adams, and E. Sargin. Deep neural networks for youtube recommendations. ACM RecSys ’16.
[18] J. Davidson, B. Liebald, J. Liu, P. Nandy, T. Van Vleet, U. Gargi, S. Gupta, Y. He, M. Lambert, B. Livingston, and D. Sampath. The youtube video recommendation system. ACM RecSys ’10.
[19] Y. Deldjoo, M. Elahi, P. Cremonesi, F. Garzotto, P. Piazzolla, and M. Quadrana. Content-based video recommendation system based on stylistic visual features. Journal on Data Semantics, 2016.
[20] M. D. Ekstrand, M. Ludwig, J. A. Konstan, and J. T. Riedl. Rethinking the recommender research ecosystem: Reproducibility, openness, and LensKit. ACM RecSys ’11.
[21] M. Fan, J. Guo, S. Zhu, S. Miao, M. Sun, and P. Li. Mobius: Towards the next generation of query-ad matching in baidu’s sponsored search. ACM KDD ’19.
[22] F. Fouss, A. Pirotte, J.-M. Renders, and M. Saerens. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation.
[23] F. Fouss, A. Pirotte, and M. Saerens. A novel way of computing similarities be- tween nodes of a graph, with application to collaborative recommendation. IEEE WI ’05.
[24] Z. Gantner, S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. MyMediaLite: A free recommender system library. 2011.
[25] M. Gao, L. Chen, X. He, and A. Zhou. Bine: Bipartite network embedding. ACM SIGIR ’18.
[26] M. Ge, C. Delgado-Battenfeld, and D. Jannach. Beyond accuracy: Evaluating recommender systems by coverage and serendipity. ACM RecSys ’10.
[27] A. Gilotte, C. Calauze"nes, T. Nedelec, A. Abraham, and S. Dolle ́. Offline a/b testing for recommender systems. ACM WSDM ’18.
[28] C. A. Gomez-Uribe and N. Hunt. The netflix recommender system: Algorithms, business value, and innovation. ACM Trans. Manage. Inf. Syst.
[29] M. Gori and A. Pucci. Itemrank: A random-walk based scoring algorithm for recommender engines. IJCAI ’07.
[30] M. Grbovic and H. Cheng. Real-time personalization using embeddings for search ranking at airbnb. ACM KDD ’18.
[31] A. Grover and J. Leskovec. Node2vec: Scalable feature learning for networks. ACM KDD ’16.
[32] G. Guo, J. Zhang, and N. Yorke-Smith. Trustsvd: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. AAAI ’15.
[33] H. Guo, R. TANG, Y. Ye, Z. Li, and X. He. Deepfm: A factorization-machine based neural network for ctr prediction. IJCAI ’17.
[34] W. L. Hamilton, R. Ying, and J. Leskovec. Inductive representation learning on large graphs. NIPS ’17.
[35] R. He, W.-C. Kang, and J. McAuley. Translation-based recommendation. ACM RecSys ’17.
[36] R. He and J. McAuley. Vbpr: Visual bayesian personalized ranking from implicit feedback. AAAI ’16.
[37] X. He and T.-S. Chua. Neural factorization machines for sparse predictive analytics. ACM SIGIR ’17.
[38] X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang. Lightgcn: Simplifying and powering graph convolution network for recommendation. ACM SIGIR ’20.
[39] X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua. Neural collaborative filtering. WWW ’17.
[40] X. He, H. Zhang, M.-Y. Kan, and T.-S. Chua. Fast matrix factorization for online recommendation with implicit feedback. ACM SIGIR ’16.
[41] J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., 2004.
[42] B. Hidasi and A. Karatzoglou. Recurrent neural networks with top-k gains for session-based recommendations. ACM CIKM ’18.
[43] C.-K. Hsieh, L. Yang, Y. Cui, T.-Y. Lin, S. Belongie, and D. Estrin. Collaborative metric learning. WWW ’17.
[44] Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. IEEE ICDM ’08.
[45] J.-T. Huang, A. Sharma, S. Sun, L. Xia, D. Zhang, P. Pronin, J. Padmanabhan, G. Ottaviano, and L. Yang. Embedding-based retrieval in facebook search. ACM KDD ’20, 2020.
[46] N.Hurley and M.Zhang. Novelty and diversity in top-n recommendation–analysis and evaluation. ACM Trans. Internet Technol., 2011.
[47] D. Jannach, P. Resnick, A. Tuzhilin, and M. Zanker. Recommender systems—beyond matrix completion. Communications of the ACM, 2016.
[48] X. Jin, Y. Zhou, and B. Mobasher. A maximum entropy web recommendation system: combining collaborative and content features. ACM KDD ’05.
[49] Y. Juan, Y. Zhuang, W.-S. Chin, and C.-J. Lin. Field-aware factorization machines for ctr prediction. ACM RecSys ’16.
[50] S. Kabbur, X. Ning, and G. Karypis. FISM: factored item similarity models for top-n recommender systems. ACM KDD ’13.
[51] W.-C. Kang and J. McAuley. Self-attentive sequential recommendation. IEEE ICDM ’18.
[52] T. Kenter, A. Borisov, C. Van Gysel, M. Dehghani, M. de Rijke, and B. Mitra. Neural networks for information retrieval. ACM WSDM ’18.
[53] D. Kim, C. Park, J. Oh, S. Lee, and H. Yu. Convolutional matrix factorization for document context-aware recommendation. ACM RecSys ’16.
[54] Y. Koren. Factorization meets the neighborhood: A multifaceted collaborative filtering model. ACM KDD ’08.
[55] Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 2009.
[56] M. Kula. Metadata embeddings for user and item cold-start recommendations. CBRecSys@RecSys ’15.
[57] J. J. Levandoski, M. Sarwat, A. Eldawy, and M. F. Mokbel. Lars: A location-aware recommender system. IEEE ICDE ’12.
[58] O. Levy and Y. Goldberg. Neural word embedding as implicit matrix factorization. NIPS ’14.
[59] C. Li, Z. Liu, M. Wu, Y. Xu, H. Zhao, P. Huang, G. Kang, Q. Chen, W. Li, and D. L. Lee. Multi-interest network with dynamic routing for recommendation at tmall. ACM CIKM ’19.
[60] D. Liang, J. Altosaar, L. Charlin, and D. M. Blei. Factorization meets the item em- bedding: Regularizing matrix factorization with item co-occurrence. ACM RecSys ’16.
[61] G. Linden, B. Smith, and J. York. Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing, 2003.
[62] D. Liu, J. Li, B. Du, J. Chang, and R. Gao. Daml: Dual attention mutual learning between ratings and reviews for item recommendation. ACM KDD ’19.
[63] B. Loni, R. Pagano, M. Larson, and A. Hanjalic. Bayesian personalized ranking with multi-channel user feedback. ACM RecSys ’16.
[64] C. Ma, P. Kang, B. Wu, Q. Wang, and X. Liu. Gated attentive-autoencoder for content-aware recommendation. ACM WSDM ’19.
[65] J. McAuley, C. Targett, Q. Shi, and A. Van Den Hengel. Image-based recommendations on styles and substitutes. ACM SIGIR ’15.
[66] T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space.
[67] T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems 26, NIPS ’13. Curran Associates, Inc.
[68] A. V. V. V. N. S. N. Sivaramakrishnan1, V.Subramaniyaswamy. A deep learning-based hybrid model for recommendation generation and ranking. Neural Computing and Applications, 2020.
[69] X. Ning and G. Karypis. Slim: Sparse linear methods for top-n recommender systems. IEEE ICDM ’11.
[70] D. W. Oard, J. Kim, et al. Implicit feedback for recommender systems. In Proceedings of the AAAI workshop on recommender systems, volume 83, pages 81–83. AAAI, 1998.
[71] L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical report, 1999.
[72] E. Palumbo, G. Rizzo, and R. Troncy. entity2rec: Learning user-item relatedness from knowledge graphs for top-n item recommendation. ACM RecSys ’17.
[73] R. Pan, Y. Zhou, B. Cao, N. N. Liu, R. Lukose, M. Scholz, and Q. Yang. One-class collaborative filtering. IEEE ICDM ’08.
[74] H. Park, J. Jung, and U. Kang. A comparative study of matrix factorization and random walk with restart in recommender systems. IEEE Big Data ’17.
[75] R. Pasricha and J. McAuley. Translation-based factorization machines for sequen- tial recommendation. ACM RecSys ’18.
[76] J. Pennington, R. Socher, and C. D. Manning. Glove: Global vectors for word representation. EMNLP ’14.
[77] B. Perozzi, R. Al-Rfou, and S. Skiena. Deepwalk: Online learning of social repre- sentations. ACM KDD ’14.
[78] B. Perozzi, V. Kulkarni, and S. Skiena. Walklets: Multiscale graph embeddings for interpretable network classification.
[79] M. Quadrana, A. Karatzoglou, B. Hidasi, and P. Cremonesi. Personalizing session- based recommendations with hierarchical recurrent neural networks. ACM RecSys ’17.
[80] B. Recht, C. Re, S. Wright, and F. Niu. Hogwild: A lock-free approach to paral- lelizing stochastic gradient descent. NIPS ’11. Curran Associates, Inc.
[81] S. Rendle. Factorization machines. IEEE ICDM ’10.
[82] S. Rendle. Factorization machines with libFM. ACM Trans. Intell. Syst. Technol.
[83] S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. UAI ’09.
[84] J. D. M. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. ICML ’05.
[85] F. Ricci, L. Rokach, and B. Shapira. Introduction to recommender systems hand- book. In Recommender systems handbook, pages 1–35. Springer, 2011.
[86] R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. NIPS ’07.
[87] A. Sharma, J. Jiang, P. Bommannavar, B. Larson, and J. Lin. Graphjet: Real-time
content recommendations at twitter. Proc. VLDB Endow.
[88] X. Su and T. M. Khoshgoftaar. A survey of collaborative filtering techniques.
Advances in artificial intelligence, 2009.
[89] F. Sun, J. Liu, J. Wu, C. Pei, X. Lin, W. Ou, and P. Jiang. Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. ACM CIKM ’19.
[90] J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei. Line: Large-scale information network embedding. WWW ’15.
[91] Y. Tay, L. Anh Tuan, and S. C. Hui. Latent relational metric learning via memory-based attention for collaborative ranking. WWW ’18.
[92] H. Tong, C. Faloutsos, and J.-Y. Pan. Fast random walk with restart and its applications. IEEE ICDM ’06.
[93] A. van den Oord, S. Dieleman, and B. Schrauwen. Deep content-based music recommendation. NIPS ’13. Curran Associates, Inc.
[94] M. Volkovs, G. Yu, and T. Poutanen. Dropoutnet: Addressing cold start in recommender systems. NIPS ’17.
[95] A. J. Walker. An efficient method for generating discrete random variables with general distributions. ACM Trans. Math. Softw., 1977.
[96] H. Wang, F. Zhang, J. Wang, M. Zhao, W. Li, X. Xie, and M. Guo. Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. ACM CIKM ’18.
[97] H. Wang, M. Zhao, X. Xie, W. Li, and M. Guo. Knowledge graph convolutional networks for recommender systems. WWW ’19.
[98] J. Wang, P. Huang, H. Zhao, Z. Zhang, B. Zhao, and D. L. Lee. Billion-scale commodity embedding for e-commerce recommendation in alibaba. ACM KDD ’18.
[99] P. Wang, J. Guo, Y. Lan, J. Xu, S. Wan, and X. Cheng. Learning hierarchical representation model for nextbasket recommendation. ACM SIGIR ’15.
[100] X. Wang, X. He, Y. Cao, M. Liu, and T.-S. Chua. Kgat: Knowledge graph attention network for recommendation. ACM KDD ’19.
[101] X. Wang, X. He, M. Wang, F. Feng, and T.-S. Chua. Neural graph collaborative filtering. ACM SIGIR ’19.
[102] X. Wang, C. Li, N. Golbandi, M. Bendersky, and M. Najork. The lambdaloss framework for ranking metric optimization. ACM CIKM ’18.
[103] J. Weston, S. Bengio, and N. Usunier. Wsabie: Scaling up to large vocabulary image annotation. IJCAI ’11. AAAI Press.
[104] J. Weston, C. Wang, R. Weiss, and A. Berenzeig. Latent collaborative retrieval. ICML ’12.
[105] J. Weston, H. Yee, and R. J. Weiss. Learning to rank recommendations with the k-order statistic loss. ACM RecSys ’13.
[106] L. Wu, C. Quan, C. Li, Q. Wang, B. Zheng, and X. Luo. A context-aware user-item representation learning for item recommendation. ACM Trans. Inf. Syst., 2019.
[107] Y. Wu, C. DuBois, A. X. Zheng, and M. Ester. Collaborative denoising auto- encoders for top-n recommender systems. ACM WSDM ’16.
[108] F.Xue, X.He, X.Wang, J.Xu, K.Liu, and R.Hong. Deep item-based collaborative filtering for top-n recommendation.
[109] J.-H. Yang, C.-M. Chen, C.-J. Wang, and M.-F. Tsai. Hop-rec: High-order proximity for implicit recommendation. ACM RecSys ’18.
[110] R.Ying, R.He, K.Chen, P.Eksombatchai, W.L.Hamilton, and J.Leskovec. Graph convolutional neural networks for web-scale recommender systems. ACM KDD ’18.
[111] L. Yu, C. Zhang, S. Pei, G. Sun, and X. Zhang. WalkRanker: A unified pairwise ranking model with multiple relations for item. AAAI ’18.
[112] Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. M. Thalmann. Time-aware point-of- interest recommendation. ACM SIGIR ’13.
[113] D. Zhang, J. Yin, X. Zhu, and C. Zhang. SINE: Scalable incomplete network embedding. IEEE ICDM ’18.
[114] F. Zhang, N. J. Yuan, D. Lian, X. Xie, and W.-Y. Ma. Collaborative knowledge base embedding for recommender systems. ACM KDD ’16.
[115] W. Zhang, T. Chen, J. Wang, and Y. Yu. Optimizing top-n collaborative filtering via dynamic negative item sampling. ACM SIGIR ’13.
[116] W. X. Zhao, G. He, K. Yang, H. Dou, J. Huang, S. Ouyang, and J. Wen. Kb4rec: A data set for linking knowledge bases with recommender systems. Data Intelligence, 2019.
[117] W. X. Zhao, J. Huang, and J.-R. Wen. Learning distributed representations for recommender systems with a network embedding approach. AIRS ’16.
[118] L. Zheng, V. Noroozi, and P. S. Yu. Joint deep modeling of users and items using reviews for recommendation. ACM WSDM ’17.
[119] C. Zhou, Y. Liu, X. Liu, Z. Liu, and J. Gao. Scalable graph embedding for asym- metric proximity. AAAI ’17.
[120] G. Zhou, N. Mou, Y. Fan, Q. Pi, W. Bian, C. Zhou, X. Zhu, and K. Gai. Deep interest evolution network for click-through rate prediction. AAAI’19/IAAI’19/EAAI’19.
[121] K. Zhou, H. Wang, W. X. Zhao, Y. Zhu, S. Wang, F. Zhang, Z. Wang, and J.-R. Wen. S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. ACM CIKM ’20.
描述 博士
國立政治大學
社群網路與人智計算國際研究生博士學位學程(TIGP)
104761501
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104761501
資料類型 thesis
dc.contributor.advisor 蔡銘峰<br>楊奕軒zh_TW
dc.contributor.advisor Tsai, Ming-Feng<br>‪Yang, Yi-Hsuanen_US
dc.contributor.author (Authors) 陳志明zh_TW
dc.contributor.author (Authors) Chen, Chih-Mingen_US
dc.creator (作者) 陳志明zh_TW
dc.creator (作者) Chen, Chih-Mingen_US
dc.date (日期) 2022en_US
dc.date.accessioned 2-Sep-2022 15:53:48 (UTC+8)-
dc.date.available 2-Sep-2022 15:53:48 (UTC+8)-
dc.date.issued (上傳時間) 2-Sep-2022 15:53:48 (UTC+8)-
dc.identifier (Other Identifiers) G0104761501en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141872-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 社群網路與人智計算國際研究生博士學位學程(TIGP)zh_TW
dc.description (描述) 104761501zh_TW
dc.description.abstract (摘要) 推薦系統已經被廣泛的運用在各種現實生活系統之中，這間接說明具實用性的推薦系統研究將會帶給世界更多的影響力，有鑑於此，我們開發了一個推薦系統架構名為SMORe，它不僅只是一個開發工具，而是被設計成具備跟上前沿研究開發可能的架構，基於此架構開發，它讓我們所提出的推薦模型皆能實現高效率且高準確度的預測，完全可與現存其他知名架構競爭，甚至表現地更好。
在此工作中，我們提出了一系列研究包含: 1) HPE, 2) Hop-Rec, 3) CSE, 4) IPR 等共四種協同過濾模型。這四種模型的共通特色為「利用高階關係改善推薦演算法」，請注意這些並非為獨立的研究，讀者可以透過我們提供的各個理論解釋來理解我們的演算法設計思維，更精簡的說明為，推薦系統相關資料集通常含有用戶與物品之間的關係，而高階關係指的是那些沒有被記錄的連結，在我們的演算法中，HPE利用隨機遊走的方式取得高階鄰居關係，用以融合用戶的異質興趣，Hop-Rec則利用隨機遊走的方式來區分用戶與物品之間的關聯強度，進而設計合適的最佳化方程式，CSE巧妙地利用蒐集到的高階鄰居關係來分群用戶與物品，從而提昇推薦的品質，IPR作為集大成，將常用的點對點協同過濾方程透過高階關係重新打造成邊對邊的協同 過濾方程，可用以清楚地解釋為何高階關係可以被有效利用在推薦系統演算法之中。		zh_TW
dc.description.abstract (摘要) Recommender system is everywhere in enterprise applications nowadays. This indicates that investigating applicable research has a more significant impact on the real world. In light of this, we developed a recommendation-purpose framework named SMORe. It is not only a toolkit but also a research-capable framework for doing cutting-edge research topics. Based on the framework, the implemented proposed models can achieve high-performance and high-accuracy predictions compared to most existing solutions.
For the proposed models, we focus on the topic of high-order relations with recommendation algorithms. Specifically, we present a series of four collaborative filtering models: 1) HPE, 2) Hop-Rec, 3) CSE, and 4) IPR. Their main features are to ‘utilize high-order relation modeling for the recommendation algorithms. Note that they are not independent works. By demonstrating their theoretical analysis, the readers can understand the rationale of our proposals. In brief, a recommendation dataset contains the user-to-item edges. The high-order information modeling is an attempt to make use of the unobserved edges. In short, HPE applies random walks to retrieve high-order neighbor data to better fuse the heterogeneous preferences. Hop-Rec determines the strongness of a high-order user-to-item pair and re-shapes the corresponding loss function. CSE shows a delicate way to cluster the users and items by high-order information and simultaneously keep and improve the recommendation quality. IPR brings the conventional entity-level CF modeling to the interaction-level CF modeling using the concept of high-order relations and finally provides an intuitive explanation about why high-order information can benefit the recommendations.		en_US
dc.description.tableofcontents 1 Introduction 1
1.1 Recommender System ........................... 1
1.2 Collaborative Filtering ........................... 5
1.3 Embedding-based CF Models........................ 11
1.4 High-Order Relation Modeling for CF models . . . . . . . . . . . . . . . 13
2 Related Work 15
2.1 Factorization Model............................. 15
2.2 Graph Embedding.............................. 17
2.3 Factorization Meets Graph Embedding................... 18
3 Proposals 20
3.1 SMORe: Sampler, Mapper and Optimizer for Recommendation . . . . . 22
3.1.1 Modeling and Modularization ................... 22
3.1.2 Sampler............................... 23
3.1.3 Mapper............................... 27
3.1.4 Optimizer.............................. 28
3.1.5 CF Model Simulation with SMORe ................ 29
3.1.6 Implementation Case Studies.................... 34
3.1.7 Experimentson Case Studies.................... 37
3.2 HPE: Heterogeneous Preference Embedding . . . . . . . . . . . . . . . . 44
3.2.1 Background Knowledge ...................... 44
3.2.2 Query-based Recommendations .................. 45
3.2.3 Network Embedding and HPE................... 46
3.2.4 Construction of User Preference Network . . . . . . . . . . . . . 46
3.2.5 Edge Sampling via Weighted Random Walks . . . . . . . . . . . 47
3.2.6 Query Intention Modeling via HPE ................ 48
3.2.7 Experiments ............................ 49
3.3 HOP-Rec: High-Order Proximity for Recommendaitons . . . . . . . . . 52
3.3.1 Background Knowledge ...................... 52
3.3.2 Interaction Graph and High-order Proximity . . . . . . . . . . . . 54
3.3.3 HOP-Rec: Graph Meets Factorization . . . . . . . . . . . . . . . 54
3.3.4 Experiments ............................ 57
3.4 CSE: Collaborative Similarity Embedding . . . . . . . . . . . . . . . . . 60
3.4.1 Background Knowledge ...................... 61
3.4.2 The Proposed CSE Framework................... 62
3.4.3 The Direct Similarity Embedding (DSEmbed) Module . . . . . . 64
3.4.4 The Neighborhood Similarity Embedding (NSEmbed) Module . . 65
3.4.5 Sampling .............................. 66
3.4.6 Optimization ............................ 67
3.4.7 Model Analysis........................... 67
3.4.8 Experiment............................. 69
3.5 IPR: Interaction-level Preference Ranking . . . . . . . . . . . . . . . . . 73
3.5.1 Background Knowledge ...................... 73
3.5.2 IPR Formulation .......................... 74
3.5.3 Proposed IPR Framework ..................... 74
3.5.4 Sampling Strategy and Optimization. . . . . . . . . . . . . . . . 76
3.5.5 Experiments ............................ 78
3.6 Revisit SMORe Framework and Proposed Models . . . . . . . . . . . . . 82
3.6.1 Recommendation Task Capability ................. 82 3.6.2 Recommendation Performance in Real-World Datasets . . . . . . 83
4 Other Trials 84
4.1 Playlist Recommendation via Preference Embedding . . . . . . . . . . . 84
4.2 NavWalker ................................. 85
4.3 Item Concept Network ........................... 86
4.4 Skewness Ranking Optimization ...................... 86 4.5 Text-aware Preference Ranking....................... 87
4.6 Long Short-term Preference Ranking.................. 90
5 Summary 91
5.1 Conclusion ................................. 91
5.2 FutureWork................................. 92
5.3 A Curated List of RecSys Research Topics. . . . . . . . . . . . . . . . . 93
Bibliography 96		zh_TW
dc.format.extent 6212796 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104761501en_US
dc.subject (關鍵詞) 推薦系統zh_TW
dc.subject (關鍵詞) 協同過濾zh_TW
dc.subject (關鍵詞) 高階關係zh_TW
dc.subject (關鍵詞) Recommender Systemen_US
dc.subject (關鍵詞) Collaborative Filteringen_US
dc.subject (關鍵詞) High-Order Relationen_US
dc.title (題名) 探討推薦系統之高階關係影響zh_TW
dc.title (題名) Exploring High-Order Relations for Recommender Systemsen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] G. Adomavicius and A. Tuzhilin. Context-aware recommender systems. ACM RecSys ’08.
[2] Q. Ai, V. Azizi, X. Chen, and Y. Zhang. Learning heterogeneous knowledge base embeddings for explainable recommendation. Algorithms, 2018.
[3] T.Badriyah, E.T.Wijayanto, I.Syarif, and P.Kristalina. A hybrid recommendation system for e-commerce based on product description and user profile. INTECH ’17.
[4] O. Barkan and N. Koenigstein. Item2vec: Neural item embedding for collaborative filtering.
[5] A.Bordes, N.Usunier, A.Garcia-Dura ́n, J.Weston, and O.Yakhnenko. Translating embeddings for modeling multi-relational data. NIPS ’13. Curran Associates, Inc.
[6] C. J. C. Burges. From RankNet to LambdaRank to LambdaMART: An overview. Technical report, Microsoft Research, 2010.
[7] Y. Cen, J. Zhang, X. Zou, C. Zhou, H. Yang, and J. Tang. Controllable multi-interest framework for recommendation. ACM KDD ’20.
[8] S. Chaudhuri and A. Tewari. Online learning to rank with top-k feedback.
[9] C. Chen, M. Zhang, Y. Liu, and S. Ma. Neural attentional rating regression with review-level explanations. WWW ’18.
[10] C.-M. Chen, M.-F. Tsai, Y.-C. Lin, and Y.-H. Yang. Query-based music recommendations via preference embedding. ACM RecSys ’16.
[11] C.-M. Chen, C.-J. Wang, M.-F. Tsai, and Y.-H. Yang. Collaborative similarity embedding for recommender systems. WWW ’19.
[12] H.-T.Cheng, L.Koc, J.Harmsen, T.Shaked, T.Chandra, H.Aradhye, G.Anderson, G. Corrado, W. Chai, M. Ispir, R. Anil, Z. Haque, L. Hong, V. Jain, X. Liu, and H. Shah. Wide & deep learning for recommender systems. ACM DLRS ’16.
[13] S.-Y. Chou, Y.-H. Yang, J.-S. R. Jang, and Y.-C. Lin. Addressing cold start for next-song recommendation. ACM RecSys ’16.
[14] E. Christakopoulou and G. Karypis. Local item-item models for top-n recommendation. ACM RecSys ’16.
[15] F. Christoffel, B. Paudel, C. Newell, and A. Bernstein. Blockbusters and wallflowers: Accurate, diverse, and scalable recommendations with random walks. ACM RecSys ’15.
[16] C. Cooper, S. H. Lee, T. Radzik, and Y. Siantos. Random walks in recommender systems: Exact computation and simulations. WWW ’14.
[17] P. Covington, J. Adams, and E. Sargin. Deep neural networks for youtube recommendations. ACM RecSys ’16.
[18] J. Davidson, B. Liebald, J. Liu, P. Nandy, T. Van Vleet, U. Gargi, S. Gupta, Y. He, M. Lambert, B. Livingston, and D. Sampath. The youtube video recommendation system. ACM RecSys ’10.
[19] Y. Deldjoo, M. Elahi, P. Cremonesi, F. Garzotto, P. Piazzolla, and M. Quadrana. Content-based video recommendation system based on stylistic visual features. Journal on Data Semantics, 2016.
[20] M. D. Ekstrand, M. Ludwig, J. A. Konstan, and J. T. Riedl. Rethinking the recommender research ecosystem: Reproducibility, openness, and LensKit. ACM RecSys ’11.
[21] M. Fan, J. Guo, S. Zhu, S. Miao, M. Sun, and P. Li. Mobius: Towards the next generation of query-ad matching in baidu’s sponsored search. ACM KDD ’19.
[22] F. Fouss, A. Pirotte, J.-M. Renders, and M. Saerens. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation.
[23] F. Fouss, A. Pirotte, and M. Saerens. A novel way of computing similarities be- tween nodes of a graph, with application to collaborative recommendation. IEEE WI ’05.
[24] Z. Gantner, S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. MyMediaLite: A free recommender system library. 2011.
[25] M. Gao, L. Chen, X. He, and A. Zhou. Bine: Bipartite network embedding. ACM SIGIR ’18.
[26] M. Ge, C. Delgado-Battenfeld, and D. Jannach. Beyond accuracy: Evaluating recommender systems by coverage and serendipity. ACM RecSys ’10.
[27] A. Gilotte, C. Calauze"nes, T. Nedelec, A. Abraham, and S. Dolle ́. Offline a/b testing for recommender systems. ACM WSDM ’18.
[28] C. A. Gomez-Uribe and N. Hunt. The netflix recommender system: Algorithms, business value, and innovation. ACM Trans. Manage. Inf. Syst.
[29] M. Gori and A. Pucci. Itemrank: A random-walk based scoring algorithm for recommender engines. IJCAI ’07.
[30] M. Grbovic and H. Cheng. Real-time personalization using embeddings for search ranking at airbnb. ACM KDD ’18.
[31] A. Grover and J. Leskovec. Node2vec: Scalable feature learning for networks. ACM KDD ’16.
[32] G. Guo, J. Zhang, and N. Yorke-Smith. Trustsvd: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. AAAI ’15.
[33] H. Guo, R. TANG, Y. Ye, Z. Li, and X. He. Deepfm: A factorization-machine based neural network for ctr prediction. IJCAI ’17.
[34] W. L. Hamilton, R. Ying, and J. Leskovec. Inductive representation learning on large graphs. NIPS ’17.
[35] R. He, W.-C. Kang, and J. McAuley. Translation-based recommendation. ACM RecSys ’17.
[36] R. He and J. McAuley. Vbpr: Visual bayesian personalized ranking from implicit feedback. AAAI ’16.
[37] X. He and T.-S. Chua. Neural factorization machines for sparse predictive analytics. ACM SIGIR ’17.
[38] X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang. Lightgcn: Simplifying and powering graph convolution network for recommendation. ACM SIGIR ’20.
[39] X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua. Neural collaborative filtering. WWW ’17.
[40] X. He, H. Zhang, M.-Y. Kan, and T.-S. Chua. Fast matrix factorization for online recommendation with implicit feedback. ACM SIGIR ’16.
[41] J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., 2004.
[42] B. Hidasi and A. Karatzoglou. Recurrent neural networks with top-k gains for session-based recommendations. ACM CIKM ’18.
[43] C.-K. Hsieh, L. Yang, Y. Cui, T.-Y. Lin, S. Belongie, and D. Estrin. Collaborative metric learning. WWW ’17.
[44] Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. IEEE ICDM ’08.
[45] J.-T. Huang, A. Sharma, S. Sun, L. Xia, D. Zhang, P. Pronin, J. Padmanabhan, G. Ottaviano, and L. Yang. Embedding-based retrieval in facebook search. ACM KDD ’20, 2020.
[46] N.Hurley and M.Zhang. Novelty and diversity in top-n recommendation–analysis and evaluation. ACM Trans. Internet Technol., 2011.
[47] D. Jannach, P. Resnick, A. Tuzhilin, and M. Zanker. Recommender systems—beyond matrix completion. Communications of the ACM, 2016.
[48] X. Jin, Y. Zhou, and B. Mobasher. A maximum entropy web recommendation system: combining collaborative and content features. ACM KDD ’05.
[49] Y. Juan, Y. Zhuang, W.-S. Chin, and C.-J. Lin. Field-aware factorization machines for ctr prediction. ACM RecSys ’16.
[50] S. Kabbur, X. Ning, and G. Karypis. FISM: factored item similarity models for top-n recommender systems. ACM KDD ’13.
[51] W.-C. Kang and J. McAuley. Self-attentive sequential recommendation. IEEE ICDM ’18.
[52] T. Kenter, A. Borisov, C. Van Gysel, M. Dehghani, M. de Rijke, and B. Mitra. Neural networks for information retrieval. ACM WSDM ’18.
[53] D. Kim, C. Park, J. Oh, S. Lee, and H. Yu. Convolutional matrix factorization for document context-aware recommendation. ACM RecSys ’16.
[54] Y. Koren. Factorization meets the neighborhood: A multifaceted collaborative filtering model. ACM KDD ’08.
[55] Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 2009.
[56] M. Kula. Metadata embeddings for user and item cold-start recommendations. CBRecSys@RecSys ’15.
[57] J. J. Levandoski, M. Sarwat, A. Eldawy, and M. F. Mokbel. Lars: A location-aware recommender system. IEEE ICDE ’12.
[58] O. Levy and Y. Goldberg. Neural word embedding as implicit matrix factorization. NIPS ’14.
[59] C. Li, Z. Liu, M. Wu, Y. Xu, H. Zhao, P. Huang, G. Kang, Q. Chen, W. Li, and D. L. Lee. Multi-interest network with dynamic routing for recommendation at tmall. ACM CIKM ’19.
[60] D. Liang, J. Altosaar, L. Charlin, and D. M. Blei. Factorization meets the item em- bedding: Regularizing matrix factorization with item co-occurrence. ACM RecSys ’16.
[61] G. Linden, B. Smith, and J. York. Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing, 2003.
[62] D. Liu, J. Li, B. Du, J. Chang, and R. Gao. Daml: Dual attention mutual learning between ratings and reviews for item recommendation. ACM KDD ’19.
[63] B. Loni, R. Pagano, M. Larson, and A. Hanjalic. Bayesian personalized ranking with multi-channel user feedback. ACM RecSys ’16.
[64] C. Ma, P. Kang, B. Wu, Q. Wang, and X. Liu. Gated attentive-autoencoder for content-aware recommendation. ACM WSDM ’19.
[65] J. McAuley, C. Targett, Q. Shi, and A. Van Den Hengel. Image-based recommendations on styles and substitutes. ACM SIGIR ’15.
[66] T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space.
[67] T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems 26, NIPS ’13. Curran Associates, Inc.
[68] A. V. V. V. N. S. N. Sivaramakrishnan1, V.Subramaniyaswamy. A deep learning-based hybrid model for recommendation generation and ranking. Neural Computing and Applications, 2020.
[69] X. Ning and G. Karypis. Slim: Sparse linear methods for top-n recommender systems. IEEE ICDM ’11.
[70] D. W. Oard, J. Kim, et al. Implicit feedback for recommender systems. In Proceedings of the AAAI workshop on recommender systems, volume 83, pages 81–83. AAAI, 1998.
[71] L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical report, 1999.
[72] E. Palumbo, G. Rizzo, and R. Troncy. entity2rec: Learning user-item relatedness from knowledge graphs for top-n item recommendation. ACM RecSys ’17.
[73] R. Pan, Y. Zhou, B. Cao, N. N. Liu, R. Lukose, M. Scholz, and Q. Yang. One-class collaborative filtering. IEEE ICDM ’08.
[74] H. Park, J. Jung, and U. Kang. A comparative study of matrix factorization and random walk with restart in recommender systems. IEEE Big Data ’17.
[75] R. Pasricha and J. McAuley. Translation-based factorization machines for sequen- tial recommendation. ACM RecSys ’18.
[76] J. Pennington, R. Socher, and C. D. Manning. Glove: Global vectors for word representation. EMNLP ’14.
[77] B. Perozzi, R. Al-Rfou, and S. Skiena. Deepwalk: Online learning of social repre- sentations. ACM KDD ’14.
[78] B. Perozzi, V. Kulkarni, and S. Skiena. Walklets: Multiscale graph embeddings for interpretable network classification.
[79] M. Quadrana, A. Karatzoglou, B. Hidasi, and P. Cremonesi. Personalizing session- based recommendations with hierarchical recurrent neural networks. ACM RecSys ’17.
[80] B. Recht, C. Re, S. Wright, and F. Niu. Hogwild: A lock-free approach to paral- lelizing stochastic gradient descent. NIPS ’11. Curran Associates, Inc.
[81] S. Rendle. Factorization machines. IEEE ICDM ’10.
[82] S. Rendle. Factorization machines with libFM. ACM Trans. Intell. Syst. Technol.
[83] S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. UAI ’09.
[84] J. D. M. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. ICML ’05.
[85] F. Ricci, L. Rokach, and B. Shapira. Introduction to recommender systems hand- book. In Recommender systems handbook, pages 1–35. Springer, 2011.
[86] R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. NIPS ’07.
[87] A. Sharma, J. Jiang, P. Bommannavar, B. Larson, and J. Lin. Graphjet: Real-time
content recommendations at twitter. Proc. VLDB Endow.
[88] X. Su and T. M. Khoshgoftaar. A survey of collaborative filtering techniques.
Advances in artificial intelligence, 2009.
[89] F. Sun, J. Liu, J. Wu, C. Pei, X. Lin, W. Ou, and P. Jiang. Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. ACM CIKM ’19.
[90] J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei. Line: Large-scale information network embedding. WWW ’15.
[91] Y. Tay, L. Anh Tuan, and S. C. Hui. Latent relational metric learning via memory-based attention for collaborative ranking. WWW ’18.
[92] H. Tong, C. Faloutsos, and J.-Y. Pan. Fast random walk with restart and its applications. IEEE ICDM ’06.
[93] A. van den Oord, S. Dieleman, and B. Schrauwen. Deep content-based music recommendation. NIPS ’13. Curran Associates, Inc.
[94] M. Volkovs, G. Yu, and T. Poutanen. Dropoutnet: Addressing cold start in recommender systems. NIPS ’17.
[95] A. J. Walker. An efficient method for generating discrete random variables with general distributions. ACM Trans. Math. Softw., 1977.
[96] H. Wang, F. Zhang, J. Wang, M. Zhao, W. Li, X. Xie, and M. Guo. Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. ACM CIKM ’18.
[97] H. Wang, M. Zhao, X. Xie, W. Li, and M. Guo. Knowledge graph convolutional networks for recommender systems. WWW ’19.
[98] J. Wang, P. Huang, H. Zhao, Z. Zhang, B. Zhao, and D. L. Lee. Billion-scale commodity embedding for e-commerce recommendation in alibaba. ACM KDD ’18.
[99] P. Wang, J. Guo, Y. Lan, J. Xu, S. Wan, and X. Cheng. Learning hierarchical representation model for nextbasket recommendation. ACM SIGIR ’15.
[100] X. Wang, X. He, Y. Cao, M. Liu, and T.-S. Chua. Kgat: Knowledge graph attention network for recommendation. ACM KDD ’19.
[101] X. Wang, X. He, M. Wang, F. Feng, and T.-S. Chua. Neural graph collaborative filtering. ACM SIGIR ’19.
[102] X. Wang, C. Li, N. Golbandi, M. Bendersky, and M. Najork. The lambdaloss framework for ranking metric optimization. ACM CIKM ’18.
[103] J. Weston, S. Bengio, and N. Usunier. Wsabie: Scaling up to large vocabulary image annotation. IJCAI ’11. AAAI Press.
[104] J. Weston, C. Wang, R. Weiss, and A. Berenzeig. Latent collaborative retrieval. ICML ’12.
[105] J. Weston, H. Yee, and R. J. Weiss. Learning to rank recommendations with the k-order statistic loss. ACM RecSys ’13.
[106] L. Wu, C. Quan, C. Li, Q. Wang, B. Zheng, and X. Luo. A context-aware user-item representation learning for item recommendation. ACM Trans. Inf. Syst., 2019.
[107] Y. Wu, C. DuBois, A. X. Zheng, and M. Ester. Collaborative denoising auto- encoders for top-n recommender systems. ACM WSDM ’16.
[108] F.Xue, X.He, X.Wang, J.Xu, K.Liu, and R.Hong. Deep item-based collaborative filtering for top-n recommendation.
[109] J.-H. Yang, C.-M. Chen, C.-J. Wang, and M.-F. Tsai. Hop-rec: High-order proximity for implicit recommendation. ACM RecSys ’18.
[110] R.Ying, R.He, K.Chen, P.Eksombatchai, W.L.Hamilton, and J.Leskovec. Graph convolutional neural networks for web-scale recommender systems. ACM KDD ’18.
[111] L. Yu, C. Zhang, S. Pei, G. Sun, and X. Zhang. WalkRanker: A unified pairwise ranking model with multiple relations for item. AAAI ’18.
[112] Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. M. Thalmann. Time-aware point-of- interest recommendation. ACM SIGIR ’13.
[113] D. Zhang, J. Yin, X. Zhu, and C. Zhang. SINE: Scalable incomplete network embedding. IEEE ICDM ’18.
[114] F. Zhang, N. J. Yuan, D. Lian, X. Xie, and W.-Y. Ma. Collaborative knowledge base embedding for recommender systems. ACM KDD ’16.
[115] W. Zhang, T. Chen, J. Wang, and Y. Yu. Optimizing top-n collaborative filtering via dynamic negative item sampling. ACM SIGIR ’13.
[116] W. X. Zhao, G. He, K. Yang, H. Dou, J. Huang, S. Ouyang, and J. Wen. Kb4rec: A data set for linking knowledge bases with recommender systems. Data Intelligence, 2019.
[117] W. X. Zhao, J. Huang, and J.-R. Wen. Learning distributed representations for recommender systems with a network embedding approach. AIRS ’16.
[118] L. Zheng, V. Noroozi, and P. S. Yu. Joint deep modeling of users and items using reviews for recommendation. ACM WSDM ’17.
[119] C. Zhou, Y. Liu, X. Liu, Z. Liu, and J. Gao. Scalable graph embedding for asym- metric proximity. AAAI ’17.
[120] G. Zhou, N. Mou, Y. Fan, Q. Pi, W. Bian, C. Zhou, X. Zhu, and K. Gai. Deep interest evolution network for click-through rate prediction. AAAI’19/IAAI’19/EAAI’19.
[121] K. Zhou, H. Wang, W. X. Zhao, Y. Zhu, S. Wang, F. Zhang, Z. Wang, and J.-R. Wen. S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. ACM CIKM ’20.		zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU202201340en_US