政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/124707
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 109952/140888 (78%)
造访人次 : 46264325      在线人数 : 880
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻
    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/124707


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/124707


    题名: 基於同儕的深度學習：超商會員將會購買什麼？
    Peer-Based Deep Learning: What Retail Customers Will Buy?
    作者: 游達
    Yu, Ta
    贡献者: 莊皓鈞
    周彥君
    Chuang, Hao-Chun
    Chou, Yen-Chun
    游達
    Yu, Ta
    关键词: 推薦系統
    深度學習
    超商
    recommendation system
    deep learning
    retail industry
    日期: 2019
    上传时间: 2019-08-07 16:06:11 (UTC+8)
    摘要: 本研究針對商品間隱含相依關係的線下零售業，提出一種基於同儕的機器學習模型―商品迭代式的深度學習模型，作為推薦系統的演算法。此法可以針對不同的目標商品改變輸出及輸入的資料，每一次迭代都可以篩選出對於目標商品最活躍的客群，著重學習目標商品與其他商品的交互購買模式，且迭代過程中也可以對某些資料較少的商品進行過取樣，以增加較少被購買商品的訓練次數，此外，本研究僅需訓練一個模型便能針對各個商品進行推薦。本研究以臺灣分布最廣的零售業―超商為例，實際測試此模型捕捉會員購物喜好的表現，結果顯示模型未見過的特定會員購買過的特定商品，此模型購買量預測可比「歷史預測法」平均改善 29%、有一半的商品降低 44%以上，相對於線性迴歸模型、或是在推薦系統中被廣泛使用的演算法―協同過濾，此深度學習模型不論是「本季買過的所有商品」、或是「上季未買，但本季新買的商品」中，皆獲得「各特定會員商品組之誤差分布」、「各商品誤差分布」及「平均誤差」此三項指標之最佳結果。
    For the offline retail industry with implicit dependence between items, this study proposes a peer-based machine learning model - item iterative deep learning - as an recommendation algorithm. This model can change the output and input data for different target items. Each iteration can filter out the most active customer groups for the target item, focusing on the interactions between the target item and other items. Besides, the iterative process can over-sample some items with less data to increase the number of training samples for less purchased items. In addition, this method only needs to train one model for all target items, instead of training many models for different target items. This study empirically tests predictive performance of the proposed item iterative deep learning using data from Taiwan`s most widely-distributed retailing sector, convenience stores. The results show that for specific items purchased by specific members that have not been seen by the model, comparing with the historical predicting method, the proposed model has improved by an average of 29% and by more than 44% for half of those items. Compared to the linear regression model or the collaborative filtering that is widely-adopted in recommender systems, the proposed model has obtained the best result of the two situations below: “all items bought this season” and "not bought in the previous season, but newly purchased in this season", in the mean and distribution of different prediction error metrics.
    參考文獻: 張志斌（2018年9月 5 日）。有關辦理「106年連鎖式便利商店經營概況調查」案。公平交易委員會新聞稿。取自https://www.ftc.gov.tw/internet/main/doc/docDetail.aspx?uid=126&docid=15589
    Aggarwal, C. C.(2016). Recommender systems . Cham: Springer International Publishing.
    Bansal, T., Belanger, D., & McCallum, A. (2016, September). Ask the gru: Multi-task learning for deep text recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 107-114). ACM.
    Basu, C., Hirsh, H., & Cohen, W.(1998). Recommendation as classification: Using social and content-based information in recommendation. In Proceedings of the 1998 National Conference on Artificial Intelligence, pp. 714–720
    Breese, J. S., Heckerman, D., & Kadie, C.(1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43-52.
    Burke, R.(2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-adapted Interaction, 12(4), pp. 331-370.
    Cai, X., Han, J., & Yang, L. (2018, April). Generative adversarial network based heterogeneous bibliographic network representation for personalized citation recommendation. In Thirty-Second AAAI Conference on Artificial Intelligence.
    Chen, J., Zhang, H., He, X., Nie, L., Liu, W., & Chua, T. S. (2017, August). 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 (pp. 335-344). ACM.
    Cheng, H. T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., & Anil, R. (2016, September). Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems (pp. 7-10). ACM.
    Chu, W. T., & Tsai, Y. L. (2017). A hybrid recommendation system considering visual information for predicting favorite restaurants. World Wide Web, 20(6), pp. 1313-1331.
    Covington, P., Adams, J., & Sargin, E.(2016, September). Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 191-198). ACM.
    Du, C., Li, C., Zheng, Y., Zhu, J., & Zhang, B. (2018). Collaborative filtering with user-item co-autoregressive models. In Thirty-Second AAAI Conference on Artificial Intelligence.
    Georgiev, K., & Nakov, P. (2013). A non-iid framework for collaborative filtering with restricted boltzmann machines. In International conference on machine learning (pp. 1148-1156).
    Gomez-Uribe, C. A., & Hunt, N. (2016). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems, 6(4), pp. 13:1-13:19.
    Gong, Y., & Zhang, Q. (2016, July). Hashtag recommendation using attention-based convolutional neural network. In IJCAI (pp. 2782-2788).
    Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
    Grčar, M., Mladenič, D., Fortuna, B., & Grobelnik, M.(2005, August). Data sparsity issues in the collaborative filtering framework. In International Workshop on Knowledge Discovery on the Web (pp. 58-76). Springer, Berlin, Heidelberg.
    Grönroos, C. (2004). The relationship marketing process: communication, interaction, dialogue, value. Journal of Business & Industrial Marketing, 19(2), pp. 99-113.
    Guo, H., Tang, R., Ye, Y., Li, Z., & He, X. (2017). DeepFM: A factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247.
    Harker, M. J. (1999). Relationship marketing defined? An examination of current relationship marketing definitions. Marketing Intelligence Planning, 17(1), pp. 13-20.
    He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017, April). Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web (pp. 173-182). International World Wide Web Conferences Steering Committee.
    Hinton, G. E., & Salakhutdinov, R. R.(2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), pp. 504-507.
    Huang, M.-H.(2015). The influence of relationship marketing investments on customer gratitude in retailing. Journal of Business Research, 68(6), pp. 1318-1323.
    Jia, X., Li, X., Li, K., Gopalakrishnan, V., Xun, G., & Zhang, A. (2016, August). Collaborative restricted Boltzmann machine for social event recommendation. In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 402-405). IEEE.
    Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014). A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188.
    Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
    Koren, Y. (2008, August). Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 426-434). ACM.
    Kotler, P.(2000). Marketing management: The millennium edition. Upper Saddle River, NJ: Prentice-Hall, Inc.
    LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), pp. 436-444.
    Lee, J., Abu-El-Haija, S., Varadarajan, B., & Natsev, A. P. (2018, July). Collaborative deep metric learning for video understanding. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 481-490). ACM.
    Linden, G., Smith, B., & York, J.(2003). Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7(1), pp. 76-80.
    Liu, R. R., Jia, C. X., Zhou, T., Sun, D., & Wang, B. H. (2009). Personal recommendation via modified collaborative filtering. Physica A: Statistical Mechanics and its Applications, 388(4), pp. 462-468.
    Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013, June). Rectifier nonlinearities improve neural network acoustic models. In Proc. ICML (Vol. 30, No. 1, p. 3).
    Middleton, S. E., Shadbolt, N. R., & De Roure, D. C.(2004). Ontological user profiling in recommender systems. ACM Transactions on Information Systems, 22(1), pp. 54-88.
    Nguyen, H. T., Wistuba, M., Grabocka, J., Drumond, L. R., & Schmidt-Thieme, L. (2017, May). Personalized deep learning for tag recommendation. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 186-197). Springer, Cham.
    Okura, S., Tagami, Y., Ono, S., & Tajima, A. (2017, August). Embedding-based news recommendation for millions of users. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1933-1942). ACM.
    Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13(5-6), pp. 393-408.
    Peng, C., Xiao, T., Li, Z., Jiang, Y., Zhang, X., Jia, K., ... & Sun, J. (2018). Megdet: A large mini-batch object detector. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6181-6189).
    Rifai, S., Vincent, P., Muller, X., Glorot, X., & Bengio, Y. (2011, June). Contractive auto-encoders: Explicit invariance during feature extraction. In Proceedings of the 28th International Conference on International Conference on Machine Learning (pp. 833-840). Omnipress.
    Salakhutdinov, R., & Hinton, G. (2009). Semantic hashing. International Journal of Approximate Reasoning, 50(7), pp. 969-978.
    Schafer, J. B., Konstan, J., & Riedl, J. (1999). Recommender systems in e-commerce. Proceedings of the 1st ACM Conference on Electronic Commerce, pp. 158-166.
    Sedhain, S., Menon, A. K., Sanner, S., & Xie, L. (2015, May). Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th International Conference on World Wide Web (pp. 111-112). ACM.
    Tran, T., & Cohen, R. (2000). Hybrid Recommender Systems for Electronic Commerce. Proceedings of the AAAI 2000 Workshop on Knowledge-Based Electronic Markets, pp. 78-84.
    Van den Oord, A., Dieleman, S., & Schrauwen, B. (2013). Deep content-based music recommendation. In Advances in Neural Information Processing Systems (pp. 2643-2651).
    Wang, J., De Vries, A. P., & Reinders, M. J.(2006). Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 501-508.
    Wang, N., & Yeung, D. Y. (2013). Learning a deep compact image representation for visual tracking. In Advances in neural information processing systems (pp. 809-817)
    Wang, S., Wang, Y., Tang, J., Shu, K., Ranganath, S., & Liu, H. (2017, April). What your images reveal: Exploiting visual contents for point-of-interest recommendation. In Proceedings of the 26th International Conference on World Wide Web (pp. 391-400). International World Wide Web Conferences Steering Committee.
    Wang, X., Wang, Y., Hsu, D., & Wang, Y. (2014). Exploration in interactive personalized music recommendation: a reinforcement learning approach. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 11(1), pp. 7.
    Zhang, J. Z., Watson Iv, G. F., Palmatier, R. W., & Dant, R. P. (2016). Dynamic relationship marketing. Journal of Marketing Research, 80(5), pp. 53-75.
    Zhang, L., Luo, T., Zhang, F., & Wu, Y. (2018). A recommendation model based on deep neural network. IEEE Access, 6, pp. 9454-9463.
    Zhang, S., Yao, L., & Sun, A. (2017a). Deep learning based recommender system: A survey and new perspectives. arXiv preprint arXiv:1707.07435.
    Zhang, S., Yao, L., & Xu, X. (2017b). Autosvd++: An efficient hybrid collaborative filtering model via contractive auto-encoders. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval (pp. 957-960). ACM.
    Zheng, L., Noroozi, V., & Yu, P. S. (2017). Joint deep modeling of users and items using reviews for recommendation. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (pp. 425-434). ACM.
    Zheng, Y., Liu, C., Tang, B., & Zhou, H. (2016). Neural autoregressive collaborative filtering for implicit feedback. In Proceedings of the First Workshop on Deep Learning for Recommender Systems (pp. 2-6). ACM.
    Zineldin, M., & Philipson, S.(2007). Kotler and Borden are not dead: Ｍyth of relationship marketing and truth of the 4Ps. Journal of Consumer Marketing, 24(4), pp. 229-241.
    Zou, C., Yumer, E., Yang, J., Ceylan, D., & Hoiem, D. (2017). 3d-prnn: Generating shape primitives with recurrent neural networks. In Proceedings of the IEEE International Conference on Computer Vision (pp. 900-909).
    描述: 碩士
    國立政治大學
    資訊管理學系
    106356013
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0106356013
    数据类型: thesis
    DOI: 10.6814/NCCU201900418
    显示于类别:[資訊管理學系] 學位論文

    文件中的档案:

    档案 大小格式浏览次数
    601301.pdf2545KbAdobe PDF274检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈