skip to main content
research-article

DeepDepict: Enabling Information Rich, Personalized Product Description Generation With the Deep Multiple Pointer Generator Network

Published: 28 June 2021 Publication History

Abstract

In e-commerce platforms, the online descriptive information of products shows significant impacts on the purchase behaviors. To attract potential buyers for product promotion, numerous workers are employed to write the impressive product descriptions. The hand-crafted product descriptions are less-efficient with great labor costs and huge time consumption. Meanwhile, the generated product descriptions do not take consideration into the customization and the diversity to meet users’ interests. To address these problems, we propose one generic framework, namely DeepDepict, to automatically generate the information-rich and personalized product descriptive information. Specifically, DeepDepict leverages the graph attention to retrieve the product-related knowledge from external knowledge base to enrich the diversity of products, constructs the personalized lexicon to capture the linguistic traits of individuals for the personalization of product descriptions, and utilizes multiple pointer-generator network to fuse heterogeneous data from multi-sources to generate informative and personalized product descriptions. We conduct intensive experiments on one public dataset. The experimental results show that DeepDepict outperforms existing solutions in terms of description diversity, BLEU, and personalized degree with significant margin gain, and is able to generate product descriptions with comprehensive knowledge and personalized linguistic traits.

References

[1]
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of the 3rd International Conference on Learning Representations. 2015.
[2]
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In Advances in Neural Information Processing Systems. 2787–2795.
[3]
Qibin Chen, Junyang Lin, Yichang Zhang, Hongxia Yang, Jingren Zhou, and Jie Tang. 2019. Towards knowledge-based personalized product description generation in e-commerce. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3040–3050.
[4]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the NAACL-HLT. 2019.
[5]
Emily Dinan, Varvara Logacheva, Valentin Malykh, Alexander Miller, Kurt Shuster, Jack Urbanek, Douwe Kiela, Arthur Szlam, Iulian Serban, Ryan Lowe, et al. 2020. The second conversational intelligence challenge (convai2). In The NeurIPS’18 Competition. Springer, 187–208.
[6]
Li Dong, Shaohan Huang, Furu Wei, Mirella Lapata, Ming Zhou, and Ke Xu. 2017. Learning to generate product reviews from attributes. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. 623–632.
[7]
Mauro Dragoni. 2019. An evolutionary strategy for concept-based multi-domain sentiment analysis. IEEE Computational Intelligence Magazine 14, 2 (2019), 18–27.
[8]
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. 2017. Convolutional sequence to sequence learning. In Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 1243–1252.
[9]
Bin Guo, Hao Wang, Yasan Ding, Wei Wu, Shaoyang Hao, Yueqi Sun, and Zhiwen Yu. 2021. Conditional text generation for harmonious human-machine interaction. ACM Transactions on Intelligent Systems and Technology 12, 2 (2021), 1--50.
[10]
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019. The curious case of neural text degeneration. In Proceedings of the International Conference on Learning Representations.
[11]
Ting Yao Hsu, Chieh Yang Huang, Yen Chia Hsu, and Ting Hao Kenneth Huang. 2020. Visual story post-editing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019. Association for Computational Linguistics (ACL), 6581–6586.
[12]
Eun-Ju Lee and Soo Yun Shin. 2014. When do consumers buy online product reviews? Effects of review quality, product type, and reviewerâs photo. Computers in Human Behavior 31 (2014), 356–366.
[13]
Beibei Li, Anindya Ghose, and Panagiotis G. Ipeirotis. 2011. Towards a theory model for product search. In Proceedings of the 20th International Conference on World Wide Web, 327–336.
[14]
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016. A diversity-promoting objective function for neural conversation models. In Proceedings of NAACL-HLT. 110–119.
[15]
Yunji Liang, Huihui Li, Bin Guo, Zhiwen Yu, Xiaolong Zheng, Sagar Samtani, and Daniel D. Zeng. 2021. Fusion of heterogeneous attention mechanisms in multi-view convolutional neural network for text classification. Information Sciences 548 (2021), 295–312.
[16]
Minh-Thang Luong, Hieu Pham, and Christopher D. Manning. 2015. Effective approaches to attention-based neural machine translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 1412–1421.
[17]
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Çağlar GuÌlçehre, and Bing Xiang. 2016. Abstractive text summarization using sequence-to-sequence RNNs and beyond. In Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning. 280–290.
[18]
Slava Novgorodov, Ido Guy, Guy Elad, and Kira Radinsky. 2019. Generating product descriptions from user reviews. In The World Wide Web Conference. 1354–1364.
[19]
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: A method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 311–318.
[20]
Juan Ramos et al. 2003. Using TF-IDF to determine word relevance in document queries. In Proceedings of the First Instructional Conference on Machine Learning, Vol. 242. Piscataway, NJ, 133–142.
[21]
Abigail See, Peter J Liu, and Christopher D. Manning. 2017. Get to the point: Summarization with pointer-generator networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1073–1083.
[22]
Iulian Vlad Serban, Tim Klinger, Gerald Tesauro, Kartik Talamadupula, Bowen Zhou, Yoshua Bengio, and Aaron Courville. 2017. Multiresolution recurrent neural networks: An application to dialogue response generation. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence.
[23]
I Sutskever, O. Vinyals, and Q.V. Le. 2014. Sequence to sequence learning with neural networks. In Proceedings of the Advances in NIPS (2014).
[24]
Quoc-Tuan Truong and Hady Lauw. 2019. Multimodal review generation for recommender systems. In The World Wide Web Conference. ACM, 1864–1874.
[25]
Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, and Hang Li. 2016. Modeling coverage for neural machine translation. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 76–85.
[26]
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph attention networks. In International Conference on Learning Representations.
[27]
Oriol Vinyals and Quoc Le. 2015. A neural conversational model[J]. In Proceedings of the 31th International Conference on Machine Learning, Lille, France, vol. 37.
[28]
Jinpeng Wang, Yutai Hou, Jing Liu, Yunbo Cao, and Chin-Yew Lin. 2017. A statistical framework for product description generation. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 187–192.
[29]
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016. Google’s neural machine translation system: Bridging the gap between human and machine translation. (2016).
[30]
Bo Xu, Yong Xu, Jiaqing Liang, Chenhao Xie, Bin Liang, Wanyun Cui, and Yanghua Xiao. 2017. Cn-dbpedia: A never-ending chinese knowledge extraction system. In Proceedings of the International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer, 428–438.
[31]
Tao Zhang, Jin Zhang, Chengfu Huo, and Weijun Ren. 2019. Automatic generation of pattern-controlled product description in e-commerce. In The World Wide Web Conference. ACM, 2355–2365.

Cited By

View all
  • (2025)Personalized Product Description Generation With Gated Pointer-Generator TransformerIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.339684012:1(52-63)Online publication date: Feb-2025
  • (2023)Link prediction for heterogeneous information networks based on enhanced meta-path aggregation and attention mechanismInternational Journal of Machine Learning and Cybernetics10.1007/s13042-023-01822-914:9(3087-3103)Online publication date: 28-Mar-2023
  • (2022)Ptr4BERTAdvances in Multimedia10.1155/2022/65406962022Online publication date: 1-Jan-2022

Index Terms

  1. DeepDepict: Enabling Information Rich, Personalized Product Description Generation With the Deep Multiple Pointer Generator Network

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Transactions on Knowledge Discovery from Data
      ACM Transactions on Knowledge Discovery from Data  Volume 15, Issue 5
      October 2021
      508 pages
      ISSN:1556-4681
      EISSN:1556-472X
      DOI:10.1145/3461317
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 28 June 2021
      Accepted: 01 January 2021
      Revised: 01 December 2020
      Received: 01 May 2020
      Published in TKDD Volume 15, Issue 5

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. Personalized text generation
      2. product description generation

      Qualifiers

      • Research-article
      • Research
      • Refereed

      Funding Sources

      • fundamental research funds for the central universities
      • National Natural Science Foundation of China
      • National Science Fund for Distinguished Young Scholars

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)54
      • Downloads (Last 6 weeks)5
      Reflects downloads up to 16 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2025)Personalized Product Description Generation With Gated Pointer-Generator TransformerIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.339684012:1(52-63)Online publication date: Feb-2025
      • (2023)Link prediction for heterogeneous information networks based on enhanced meta-path aggregation and attention mechanismInternational Journal of Machine Learning and Cybernetics10.1007/s13042-023-01822-914:9(3087-3103)Online publication date: 28-Mar-2023
      • (2022)Ptr4BERTAdvances in Multimedia10.1155/2022/65406962022Online publication date: 1-Jan-2022

      View Options

      Login options

      Full Access

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format.

      HTML Format

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media