International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
권호 표지
DOI QR코드

DOI QR Code

Design and Implementation of AI Recommendation Platform for Commercial Services

  • Received : 2023.10.21
  • Accepted : 2023.11.02
  • Published : 2023.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we discuss the design and implementation of a recommendation platform actually built in the field. We survey deep learning-based recommendation models that are effective in reflecting individual user characteristics. The recently proposed RNN-based sequential recommendation models reflect individual user characteristics well. The recommendation platform we proposed has an architecture that can collect, store, and process big data from a company's commercial services. Our recommendation platform provides service providers with intuitive tools to evaluate and apply timely optimized recommendation models. In the model evaluation we performed, RNN-based sequential recommendation models showed high scores.

Keywords

References

  1. Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl, "Item-based collaborative filtering recommendation algorithms," Proceedings of the 10th international conference on World Wide Web, May 2001. https://dl.acm.org/doi/10.1145/371920.372071
  2. Oren Barkan and Noam Koenigstein, "Item2Vec: Neural Item Embedding for Collaborative Filtering," 2016 IEEE Workshop on Machine Learning for Signal Processing, November 2016. https://arxiv.org/abs/1603.04259
  3. H Sak, AW Senior, and F Beaufays, "Long short-term memory recurrent neural network architectures for large scale acoustic modeling," INTERSPEECH (2014), Feb 2014. https://static.googleusercontent.com/media/research.google.com/ko//pubs/archive/43905.pdf
  4. Balazs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk,, "SESSION-BASED RECOMMENDATIONS WITH RECURRENT NEURAL NETWORKS," ICLR 2016, March 2016.B. Sklar, Digital Communications, Prentice Hall, pp. 187, 1998. https://arxiv.org/abs/1511.06939
  5. Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang, "BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer," Proceedings of the 28th ACM International Conference on Information and Knowledge Management", November 2019 https://arxiv.org/abs/1904.06690
  6. Jinming Li, Wentao Zhang, Tian Wang, Guanglei Xiong, Alan Lu, and Gerard Medioni, "GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation," April 2023 https://arxiv.org/abs/2304.03879
  7. DE Rumelhart, GE Hinton, and RJ Williams, "Learning representations by back-propagating errors," Nature, October 1986. https://www.nature.com/articles/323533a0
  8. Ilya Sutskever, Oriol Vinyals, and Quoc V. Le, "Sequence to Sequence Learning with Neural Networks," NIPS, September 2014. https://arxiv.org/abs/1409.3215
  9. Heiga Zen and Hasim Sak, "Unidirectional long short-term memory recurrent neural network with recurrent output layer for low-latency speech synthesis," ICASSP, April 2015. https://ieeexplore.ieee.org/abstract/document/7178816
  10. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, October 2019. https://arxiv.org/abs/1810.04805