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題名 ALBERT空間到直播電商商品空間：一個表示框架
From ALBERT space to livestreaming commerce product space: a representation framework作者 謝鴻銘
Hsieh, Hung-Ming貢獻者 林怡伶<br>蕭舜文
Lin, Yi-Ling<br>Hsiao, Shun-Wen
謝鴻銘
Hsieh, Hung-Ming關鍵詞 直播電商
商品表示
推薦系統
ALBERT
livestreaming commerce
product representation
recommender system
ALBERT日期 2023 上傳時間 2-Jan-2024 15:38:20 (UTC+8) 摘要 直播電商市場近年來迅速成長，與傳統電子商務不同，直播電商中的商品並非預先定義的，這樣導致直播店商中的商品尤其複雜。為了幫助消費者在直播電商中找到合適的產品，推薦系統的使用至關重要，而這類系統的效果在很大程度上依賴於強大的商品表示。然而，在直播電商中學習商品表示仍未被充分探索，為此，本研究提出了一個在直播電商中學習商品表示的框架，該框架基於ALBERT將商品名稱轉換為商品表示，用以表示消費者、產品和直播主。此外，我們發現預訓練的語言模型的語料空間不適合用來表示商品，因此我們提出的框架能將語料空間轉換到商品空間，從而提高了推薦效果。最後，我們也嘗試將提出的框架學習到的商品空間進行視覺化，在二維的空間中比較我們的商品表示與語料的商品表示的差異，還有視覺化消費者購買的軌跡及直播主販賣的軌跡。
The livestreaming commerce (LSC) market has experienced rapid growth in recent years. Unlike traditional e-commerce, items in LSC are not predefined, making the items particularly complex. To assist consumers in discovering suitable products in LSC, the use of a recommender system is essential, and the effectiveness of such systems heavily relies on robust product representation. However, learning product representation in LSC remains underexplored. Addressing this gap, this study proposes a framework for learning product representation in LSC. This framework utilizes item names in conjunction with ALBERT to represent consumers, products, and streamers. Furthermore, we find that pre-trained language models' corpus space is inadequate for product representation. Our proposed framework allows the conversion of corpus space into product space, enhancing recommendation performance. Lastly, we visualize the learned product representations, comparing it to the corpus-based product representations in two dimensions. We also visualize consumer purchase trajectories and streamer sales trajectories.參考文獻 Arora, A., Glaser, D., Kluge, P., Kim, A., Kohli, S., and Sak, N. 2021. "It’s Showtime! How Live Commerce Is Transforming the Shopping Experience." Retrieved 12/15, 2022, from https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/its-showtime-how-live-commerce-is-transforming-the-shopping-experience Batmaz, Z., Yurekli, A., Bilge, A., and Kaleli, C. 2019. "A Review on Deep Learning for Recommender Systems: Challenges and Remedies," Artificial Intelligence Review (52), pp. 1-37. Bianchi, F., Yu, B., and Tagliabue, J. 2021. "Bert Goes Shopping: Comparing Distributional Models for Product Representations," Proceedings of The 4th Workshop on e-Commerce and NLP, pp. 1-12. Chen, Q., Zhao, H., Li, W., Huang, P., and Ou, W. 2019. "Behavior Sequence Transformer for E-Commerce Recommendation in Alibaba," Proceedings of the 1st international workshop on deep learning practice for high-dimensional sparse data, pp. 1-4. Chen, T., and Guestrin, C. 2016. "Xgboost: A Scalable Tree Boosting System," Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785-794. Chen, Y., Lu, F., and Zheng, S. 2020. "A Study on the Influence of E-Commerce Live Streaming on Consumer Repurchase Intentions," International Journal of Marketing Studies (12:4), p. 48. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. 2014. "Learning Phrase Representations Using Rnn Encoder-Decoder for Statistical Machine Translation," arXiv preprint arXiv:1406.1078). Covington, P., Adams, J., and Sargin, E. 2016. "Deep Neural Networks for Youtube Recommendations," Proceedings of the 10th ACM conference on recommender systems, pp. 191-198. Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. 2018. "Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding," arXiv preprint arXiv:1810.04805). Fu, W. 2021. "Consumer Choices in Live Streaming Retailing, Evidence from Taobao Ecommerce," The 2021 12th International Conference on E-business, Management and Economics, pp. 12-20. Grbovic, M., Radosavljevic, V., Djuric, N., Bhamidipati, N., Savla, J., Bhagwan, V., and Sharp, D. 2015. "E-Commerce in Your Inbox: Product Recommendations at Scale," Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1809-1818. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T.-S. 2017. "Neural Collaborative Filtering," Proceedings of the 26th international conference on world wide web, pp. 173-182. Hidasi, B., Karatzoglou, A., Baltrunas, L., and Tikk, D. 2015. "Session-Based Recommendations with Recurrent Neural Networks," arXiv preprint arXiv:1511.06939). Hochreiter, S., and Schmidhuber, J. 1997. "Long Short-Term Memory," Neural computation (9:8), pp. 1735-1780. Hou, Y., He, Z., McAuley, J., and Zhao, W. X. 2023. "Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders," Proceedings of the ACM Web Conference 2023, pp. 1162-1171. iiMedia. 2023. "Development of China's Gift Economy Industry and Consumer Behavior Study: The Continuous Expansion of China's Live Streaming E-Commerce Market Is Boosting the Gift Economy." Retrieved 11/08, 2023, from https://www.iimedia.cn/c1061/93305.html Jambo. 2023. "Igniting New Retail +1 Business Opportunities." Retrieved 11/08, 2023, from https://jambolive.tv/article/news/72/ Jambo. n.d. "Jambo Home Page." Retrieved 11/07, 2023, from https://jambolive.tv/ Koren, Y., Bell, R., and Volinsky, C. 2009. "Matrix Factorization Techniques for Recommender Systems," Computer (42:8), pp. 30-37. Kurniasari, L., and Setyanto, A. 2020. "Sentiment Analysis Using Recurrent Neural Network," Journal of Physics: Conference Series: IOP Publishing, p. 012018. Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R. 2019. "Albert: A Lite Bert for Self-Supervised Learning of Language Representations," arXiv preprint arXiv:1909.11942). Le, Q., and Mikolov, T. 2014. "Distributed Representations of Sentences and Documents," International conference on machine learning: PMLR, pp. 1188-1196. Liu, Y., and Lapata, M. 2019. "Text Summarization with Pretrained Encoders," arXiv preprint arXiv:1908.08345). McInnes, L., Healy, J., and Melville, J. 2018. "Umap: Uniform Manifold Approximation and Projection for Dimension Reduction," arXiv preprint arXiv:1802.03426). Mikolov, T., Chen, K., Corrado, G., and Dean, J. 2013. "Efficient Estimation of Word Representations in Vector Space," arXiv preprint arXiv:1301.3781). Pantano, E., Priporas, C.-V., Stylos, N., and Dennis, C. 2019. "Facilitating Tourists' Decision Making through Open Data Analyses: A Novel Recommender System," Tourism Management Perspectives (31), pp. 323-331. Park, D. H., Kim, H. K., Choi, I. Y., and Kim, J. K. 2012. "A Literature Review and Classification of Recommender Systems Research," Expert systems with applications (39:11), pp. 10059-10072. PyTorch. "Torch.Nn.Gru." Retrieved 11/19, 2023, from https://pytorch.org/docs/stable/generated/torch.nn.GRU.html PyTorch. "Torch.Nn.Lstm." Retrieved 11/19, 2023, from https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html PyTorch. "Torch.Nn.Rnn." Retrieved 11/19, 2023, from https://pytorch.org/docs/stable/generated/torch.nn.RNN.html PyTorch. "Torch.Nn.Transformerencoder." Retrieved 11/19, 2023, from https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoder.html Rendle, S., Gantner, Z., Freudenthaler, C., and Schmidt-Thieme, L. 2011. "Fast Context-Aware Recommendations with Factorization Machines," Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pp. 635-644. Schafer, J. B., Frankowski, D., Herlocker, J., and Sen, S. 2007. "Collaborative Filtering Recommender Systems," in The Adaptive Web: Methods and Strategies of Web Personalization. Springer, pp. 291-324. Schafer, J. B., Konstan, J., and Riedl, J. 1999. "Recommender Systems in E-Commerce," Proceedings of the 1st ACM conference on Electronic commerce, pp. 158-166. Singhal, S., Shah, R. R., Chakraborty, T., Kumaraguru, P., and Satoh, S. i. 2019. "Spotfake: A Multi-Modal Framework for Fake News Detection," 2019 IEEE fifth international conference on multimedia big data (BigMM): IEEE, pp. 39-47. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. 2017. "Attention Is All You Need," Advances in neural information processing systems (30). Walter, F. E., Battiston, S., Yildirim, M., and Schweitzer, F. 2012. "Moving Recommender Systems from on-Line Commerce to Retail Stores," Information systems and e-business management (10), pp. 367-393. Wang, S., Hu, L., Wang, Y., Cao, L., Sheng, Q. Z., and Orgun, M. 2019. "Sequential Recommender Systems: Challenges, Progress and Prospects," arXiv preprint arXiv:2001.04830). Wang, T., and Fu, Y. 2020. "Item-Based Collaborative Filtering with Bert," Proceedings of The 3rd Workshop on e-Commerce and NLP, pp. 54-58. Wang, Y., Lu, Z., Cao, P., Chu, J., Wang, H., and Wattenhofer, R. 2022. "How Live Streaming Changes Shopping Decisions in E-Commerce: A Study of Live Streaming Commerce," Computer Supported Cooperative Work (CSCW) (31:4), pp. 701-729. Wu, C.-Y., Ahmed, A., Beutel, A., Smola, A. J., and Jing, H. 2017. "Recurrent Recommender Networks," Proceedings of the tenth ACM international conference on web search and data mining, pp. 495-503. Yang, M. 2020. "Ckip Albert Tiny Chinese." Retrieved 11/19, 2023, from https://huggingface.co/ckiplab/albert-tiny-chinese Yang, M., and Ma, W.-Y. 2023. "Ckip-Transformers." Retrieved 11/09, 2023, from https://github.com/ckiplab/ckip-transformers Yu, S., Jiang, Z., Chen, D.-D., Feng, S., Li, D., Liu, Q., and Yi, J. 2021. "Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation," Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 3886-3894. Zhang, M., Liu, Y., Wang, Y., and Zhao, L. 2022. "How to Retain Customers: Understanding the Role of Trust in Live Streaming Commerce with a Socio-Technical Perspective," Computers in Human Behavior (127), p. 107052. Zhang, S., Yao, L., Sun, A., and Tay, Y. 2019. "Deep Learning Based Recommender System: A Survey and New Perspectives," ACM computing surveys (CSUR) (52:1), pp. 1-38. 描述 碩士
國立政治大學
資訊管理學系
110356018資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110356018 資料類型 thesis dc.contributor.advisor 林怡伶<br>蕭舜文 zh_TW dc.contributor.advisor Lin, Yi-Ling<br>Hsiao, Shun-Wen en_US dc.contributor.author (Authors) 謝鴻銘 zh_TW dc.contributor.author (Authors) Hsieh, Hung-Ming en_US dc.creator (作者) 謝鴻銘 zh_TW dc.creator (作者) Hsieh, Hung-Ming en_US dc.date (日期) 2023 en_US dc.date.accessioned 2-Jan-2024 15:38:20 (UTC+8) - dc.date.available 2-Jan-2024 15:38:20 (UTC+8) - dc.date.issued (上傳時間) 2-Jan-2024 15:38:20 (UTC+8) - dc.identifier (Other Identifiers) G0110356018 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/149068 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 110356018 zh_TW dc.description.abstract (摘要) 直播電商市場近年來迅速成長，與傳統電子商務不同，直播電商中的商品並非預先定義的，這樣導致直播店商中的商品尤其複雜。為了幫助消費者在直播電商中找到合適的產品，推薦系統的使用至關重要，而這類系統的效果在很大程度上依賴於強大的商品表示。然而，在直播電商中學習商品表示仍未被充分探索，為此，本研究提出了一個在直播電商中學習商品表示的框架，該框架基於ALBERT將商品名稱轉換為商品表示，用以表示消費者、產品和直播主。此外，我們發現預訓練的語言模型的語料空間不適合用來表示商品，因此我們提出的框架能將語料空間轉換到商品空間，從而提高了推薦效果。最後，我們也嘗試將提出的框架學習到的商品空間進行視覺化，在二維的空間中比較我們的商品表示與語料的商品表示的差異，還有視覺化消費者購買的軌跡及直播主販賣的軌跡。 zh_TW dc.description.abstract (摘要) The livestreaming commerce (LSC) market has experienced rapid growth in recent years. Unlike traditional e-commerce, items in LSC are not predefined, making the items particularly complex. To assist consumers in discovering suitable products in LSC, the use of a recommender system is essential, and the effectiveness of such systems heavily relies on robust product representation. However, learning product representation in LSC remains underexplored. Addressing this gap, this study proposes a framework for learning product representation in LSC. This framework utilizes item names in conjunction with ALBERT to represent consumers, products, and streamers. Furthermore, we find that pre-trained language models' corpus space is inadequate for product representation. Our proposed framework allows the conversion of corpus space into product space, enhancing recommendation performance. Lastly, we visualize the learned product representations, comparing it to the corpus-based product representations in two dimensions. We also visualize consumer purchase trajectories and streamer sales trajectories. en_US dc.description.tableofcontents 1 Introduction 1 2 Related Work 3 2.1 Livestreaming Commerce (LSC) 3 2.2 Recommender System 4 2.3 Sequential Recommender System 5 2.4 Text Representation 5 2.5 Product Representation 7 3 Methodology 8 3.1 Framework 8 3.2 Embedding Model 9 3.3 Consumer-Item Representation 10 3.4 Streamer Representation 11 3.5 Output Layer 12 3.6 Baseline 12 3.7 Training Process 13 4 Experiment 15 4.1 Dataset 15 4.2 Settings 16 4.3 Preliminary Experiment: Explore Historical Length (L) 17 4.4 Do Item Names Better Represent Products in LSC? 18 4.5 Does the PLM Space Require Transformation Using an MLP? 19 4.6 Is the Proposed Framework Helpful? 21 4.7 Does our product representation offer insights? 24 5 Conclusion and Future Work 30 References 32 zh_TW dc.format.extent 1537442 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110356018 en_US dc.subject (關鍵詞) 直播電商 zh_TW dc.subject (關鍵詞) 商品表示 zh_TW dc.subject (關鍵詞) 推薦系統 zh_TW dc.subject (關鍵詞) ALBERT zh_TW dc.subject (關鍵詞) livestreaming commerce en_US dc.subject (關鍵詞) product representation en_US dc.subject (關鍵詞) recommender system en_US dc.subject (關鍵詞) ALBERT en_US dc.title (題名) ALBERT空間到直播電商商品空間：一個表示框架 zh_TW dc.title (題名) From ALBERT space to livestreaming commerce product space: a representation framework en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Arora, A., Glaser, D., Kluge, P., Kim, A., Kohli, S., and Sak, N. 2021. "It’s Showtime! How Live Commerce Is Transforming the Shopping Experience." Retrieved 12/15, 2022, from https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/its-showtime-how-live-commerce-is-transforming-the-shopping-experience Batmaz, Z., Yurekli, A., Bilge, A., and Kaleli, C. 2019. "A Review on Deep Learning for Recommender Systems: Challenges and Remedies," Artificial Intelligence Review (52), pp. 1-37. Bianchi, F., Yu, B., and Tagliabue, J. 2021. "Bert Goes Shopping: Comparing Distributional Models for Product Representations," Proceedings of The 4th Workshop on e-Commerce and NLP, pp. 1-12. Chen, Q., Zhao, H., Li, W., Huang, P., and Ou, W. 2019. "Behavior Sequence Transformer for E-Commerce Recommendation in Alibaba," Proceedings of the 1st international workshop on deep learning practice for high-dimensional sparse data, pp. 1-4. Chen, T., and Guestrin, C. 2016. "Xgboost: A Scalable Tree Boosting System," Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785-794. Chen, Y., Lu, F., and Zheng, S. 2020. "A Study on the Influence of E-Commerce Live Streaming on Consumer Repurchase Intentions," International Journal of Marketing Studies (12:4), p. 48. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. 2014. "Learning Phrase Representations Using Rnn Encoder-Decoder for Statistical Machine Translation," arXiv preprint arXiv:1406.1078). Covington, P., Adams, J., and Sargin, E. 2016. "Deep Neural Networks for Youtube Recommendations," Proceedings of the 10th ACM conference on recommender systems, pp. 191-198. Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. 2018. "Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding," arXiv preprint arXiv:1810.04805). Fu, W. 2021. "Consumer Choices in Live Streaming Retailing, Evidence from Taobao Ecommerce," The 2021 12th International Conference on E-business, Management and Economics, pp. 12-20. Grbovic, M., Radosavljevic, V., Djuric, N., Bhamidipati, N., Savla, J., Bhagwan, V., and Sharp, D. 2015. "E-Commerce in Your Inbox: Product Recommendations at Scale," Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1809-1818. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T.-S. 2017. "Neural Collaborative Filtering," Proceedings of the 26th international conference on world wide web, pp. 173-182. Hidasi, B., Karatzoglou, A., Baltrunas, L., and Tikk, D. 2015. "Session-Based Recommendations with Recurrent Neural Networks," arXiv preprint arXiv:1511.06939). Hochreiter, S., and Schmidhuber, J. 1997. "Long Short-Term Memory," Neural computation (9:8), pp. 1735-1780. Hou, Y., He, Z., McAuley, J., and Zhao, W. X. 2023. "Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders," Proceedings of the ACM Web Conference 2023, pp. 1162-1171. iiMedia. 2023. "Development of China's Gift Economy Industry and Consumer Behavior Study: The Continuous Expansion of China's Live Streaming E-Commerce Market Is Boosting the Gift Economy." Retrieved 11/08, 2023, from https://www.iimedia.cn/c1061/93305.html Jambo. 2023. "Igniting New Retail +1 Business Opportunities." Retrieved 11/08, 2023, from https://jambolive.tv/article/news/72/ Jambo. n.d. "Jambo Home Page." Retrieved 11/07, 2023, from https://jambolive.tv/ Koren, Y., Bell, R., and Volinsky, C. 2009. "Matrix Factorization Techniques for Recommender Systems," Computer (42:8), pp. 30-37. Kurniasari, L., and Setyanto, A. 2020. "Sentiment Analysis Using Recurrent Neural Network," Journal of Physics: Conference Series: IOP Publishing, p. 012018. Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R. 2019. "Albert: A Lite Bert for Self-Supervised Learning of Language Representations," arXiv preprint arXiv:1909.11942). Le, Q., and Mikolov, T. 2014. "Distributed Representations of Sentences and Documents," International conference on machine learning: PMLR, pp. 1188-1196. Liu, Y., and Lapata, M. 2019. "Text Summarization with Pretrained Encoders," arXiv preprint arXiv:1908.08345). McInnes, L., Healy, J., and Melville, J. 2018. "Umap: Uniform Manifold Approximation and Projection for Dimension Reduction," arXiv preprint arXiv:1802.03426). Mikolov, T., Chen, K., Corrado, G., and Dean, J. 2013. "Efficient Estimation of Word Representations in Vector Space," arXiv preprint arXiv:1301.3781). Pantano, E., Priporas, C.-V., Stylos, N., and Dennis, C. 2019. "Facilitating Tourists' Decision Making through Open Data Analyses: A Novel Recommender System," Tourism Management Perspectives (31), pp. 323-331. Park, D. H., Kim, H. K., Choi, I. Y., and Kim, J. K. 2012. "A Literature Review and Classification of Recommender Systems Research," Expert systems with applications (39:11), pp. 10059-10072. PyTorch. "Torch.Nn.Gru." Retrieved 11/19, 2023, from https://pytorch.org/docs/stable/generated/torch.nn.GRU.html PyTorch. "Torch.Nn.Lstm." Retrieved 11/19, 2023, from https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html PyTorch. "Torch.Nn.Rnn." Retrieved 11/19, 2023, from https://pytorch.org/docs/stable/generated/torch.nn.RNN.html PyTorch. "Torch.Nn.Transformerencoder." Retrieved 11/19, 2023, from https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoder.html Rendle, S., Gantner, Z., Freudenthaler, C., and Schmidt-Thieme, L. 2011. "Fast Context-Aware Recommendations with Factorization Machines," Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pp. 635-644. Schafer, J. B., Frankowski, D., Herlocker, J., and Sen, S. 2007. "Collaborative Filtering Recommender Systems," in The Adaptive Web: Methods and Strategies of Web Personalization. Springer, pp. 291-324. Schafer, J. B., Konstan, J., and Riedl, J. 1999. "Recommender Systems in E-Commerce," Proceedings of the 1st ACM conference on Electronic commerce, pp. 158-166. Singhal, S., Shah, R. R., Chakraborty, T., Kumaraguru, P., and Satoh, S. i. 2019. "Spotfake: A Multi-Modal Framework for Fake News Detection," 2019 IEEE fifth international conference on multimedia big data (BigMM): IEEE, pp. 39-47. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. 2017. "Attention Is All You Need," Advances in neural information processing systems (30). Walter, F. E., Battiston, S., Yildirim, M., and Schweitzer, F. 2012. "Moving Recommender Systems from on-Line Commerce to Retail Stores," Information systems and e-business management (10), pp. 367-393. Wang, S., Hu, L., Wang, Y., Cao, L., Sheng, Q. Z., and Orgun, M. 2019. "Sequential Recommender Systems: Challenges, Progress and Prospects," arXiv preprint arXiv:2001.04830). Wang, T., and Fu, Y. 2020. "Item-Based Collaborative Filtering with Bert," Proceedings of The 3rd Workshop on e-Commerce and NLP, pp. 54-58. Wang, Y., Lu, Z., Cao, P., Chu, J., Wang, H., and Wattenhofer, R. 2022. "How Live Streaming Changes Shopping Decisions in E-Commerce: A Study of Live Streaming Commerce," Computer Supported Cooperative Work (CSCW) (31:4), pp. 701-729. Wu, C.-Y., Ahmed, A., Beutel, A., Smola, A. J., and Jing, H. 2017. "Recurrent Recommender Networks," Proceedings of the tenth ACM international conference on web search and data mining, pp. 495-503. Yang, M. 2020. "Ckip Albert Tiny Chinese." Retrieved 11/19, 2023, from https://huggingface.co/ckiplab/albert-tiny-chinese Yang, M., and Ma, W.-Y. 2023. "Ckip-Transformers." Retrieved 11/09, 2023, from https://github.com/ckiplab/ckip-transformers Yu, S., Jiang, Z., Chen, D.-D., Feng, S., Li, D., Liu, Q., and Yi, J. 2021. "Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation," Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 3886-3894. Zhang, M., Liu, Y., Wang, Y., and Zhao, L. 2022. "How to Retain Customers: Understanding the Role of Trust in Live Streaming Commerce with a Socio-Technical Perspective," Computers in Human Behavior (127), p. 107052. Zhang, S., Yao, L., Sun, A., and Tay, Y. 2019. "Deep Learning Based Recommender System: A Survey and New Perspectives," ACM computing surveys (CSUR) (52:1), pp. 1-38. zh_TW
