|
|
E-Commerce Letters 2024
基于深度学习的电子商务个性化推荐模型
|
Abstract:
智能推荐是电子商务领域的一项重要任务。目前普遍使用关联规则、协同过滤、马尔可夫链、递归神经网络等技术进行购物篮推荐。本文主要研究基于深度学习的电子商务智能推荐系统(IRS)。本文首先进行了电子商务推荐系统的总体设计，提出了电子商务IRS的功能模块和系统架构。然后，讨论了电子商务IRS中的推荐算法，并基于卷积神经网络对电子商务IRS进行了优化。最后，本文比较分析了三种流行的推荐算法在阿里巴巴数据集上的性能。实验结果表明，本模型在不同推荐列表长度下的召回率和NDCG指标上均取得了更高的数值，均明显优于其他两种算法(Item和BPR)，在挖掘用户会话序列中的兴趣和行为偏好方面具有很强的价值，能够从大规模数据中学习到更丰富和复杂的用户行为和商品信息，这有助于提高推荐的效率和准确性，具有较强的实际意义和推广价值。
Intelligent recommendation is an important task in the field of electronic commerce. At present, association rules, collaborative filtering, Markov chain and recursive neural network are widely used to recommend shopping baskets. This paper mainly studies the e-commerce intelligent recommendation system (IRS) based on deep learning. In this paper, firstly, the overall design of e-commerce recommendation system is carried out, and the functional modules and system architecture of e-commerce IRS are put forward. Then, the recommendation algorithm in e-commerce IRS is discussed, and the e-commerce IRS is optimized based on convolutional neural network. Finally, this paper compares and analyzes the performance of three popular recommendation algorithms on Alibaba data sets. The experimental results show that this model achieves higher recall and NDCG index under different recommended list lengths, which are obviously superior to the other two algorithms (Item and BPR). It is of great value in mining users’ interests and behavior preferences in conversation sequences, and can learn richer and more complex information about users’ behaviors and commodities from large-scale data, which is helpful to improve the efficiency and accuracy of recommendation, and has strong practical significance and promotion value.
| [1] | Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., et al. (2019) Efficient User Profiling Based Intelligent Travel Recommender System for Individual and Group of Users. Mobile Networks & Applications, 24, 1018-1033. https://doi.org/10.1007/s11036-018-1059-2 |
| [2] | Taneja, A. and Arora, A. (2018) Cross Domain Recommendation Using Multidimensional Tensor Factorization. Expert Systems with Applications, 92, 304-316. https://doi.org/10.1016/j.eswa.2017.09.042 |
| [3] | Peng, M.J., Liu, X.L., Luo, X.M., et al. (2017) Recognizing Intentions of E-Commerce Consumers Based on Ant Colony Optimization Simulation. Journal of Intelligent & Fuzzy Systems, 33, 2687-2697. https://doi.org/10.3233/JIFS-169318 |
| [4] | Zhang, M. AND Liu, Y. (2021) A Commentary of TikTok Recommendation Algorithms in MIT Technology Review 2021. Fundamental Research, 1, 846-847. https://doi.org/10.1016/j.fmre.2021.11.015 |
| [5] | Shen, X. AND Sun, Y. (2023) A Recommendation Algorithm Incorporating Moth-Flame Optimization Algorithm and Fuzzy Clustering. 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA), Shenyang, 29-31 January 2023, 677-683. https://doi.org/10.1109/ICPECA56706.2023.10075973 |
| [6] | Yu, H., Dou, L. AND Zhang, F. (2015) A Novel Collaborative Recommendation Algorithm Integrating Probabilistic Matrix Factorization and Neighbor Model. Journal of Information &Computational Science, 12, 2011-2019. https://doi.org/10.12733/jics20105604 |
| [7] | Liu, K.H., Chuang, H.L. AND Liu, T.J. (2022) Clothing Recommendation Based on Deep Learning. 2022 IEEE International Conference on Consumer Electronics-Taiwan, Taipei, 6-8 July 2022, 281-282. https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869006 |
| [8] | Xhaferra, E., Cina, E. AND Toti, L. (2022) Classification of Standard Fashion MNIST Dataset Using Deep Learning Based CNN Algorithms. 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, 20-22 October 2022, 494-498. https://doi.org/10.1109/ISMSIT56059.2022.9932737 |
| [9] | Hu, Z, H., Li, X., Wei, C., et al. (2019) Examining Collaborative Filtering Algorithms for Clothing Recommendation in E-Commerce. Textile Research Journal, 89, 2821-2835. https://doi.org/10.1177/0040517518801200 |
| [10] | Bhoi, A., Nayak, R.P., Bhoi, S.K., et al. (2021) IoT-IIRS: Internet of Things Based Intelligent-Irrigation Recommendation System Using Machine Learning Approach for Efficient Water Usage. PeerJ Computer Science, 7, e578. https://doi.org/10.7717/peerj-cs.578 |
| [11] | Vellaichamy, V. and Kalimuthu, V. (2017) Hybrid Collaborative Movie Recommender System Using Clustering and Bat Optimization. International Journal of Intelligent Engineering & Systems, 10, 38-47. https://doi.org/10.22266/ijies2017.1031.05 |
| [12] | Liu, Y. and Zhang, Q.X. (2022) Research on E-Commerce User Interest Recommendation Method Based on TF-IDF Algorithm. 2022 2nd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS), Shenyang, 25-27 February 2022, 291-295. https://doi.org/10.1109/ACCTCS53867.2022.00066 |
| [13] | Gururaj, P. (2021) Artificial Intelligence-Application in the Field of E-Commerce. International Journal of Research-GRANTHAALAYAH, 9, 170-177. https://doi.org/10.29121/granthaalayah.v9.i4.2021.3849 |
| [14] | Belkhadir, I., Omar, E.D. and Boumhidi, J. (2019) An Intelligent Recommender System Using Social Trust Path for Recommendations in Web-Based Social Networks. Procedia Computer Science, 148, 181-190. https://doi.org/10.1016/j.procs.2019.01.035 |
| [15] | Yadav, V., Shukla, R., Tripathi, A., et al. (2021) A New Approach for Movie Recommender System Using K-Means Clustering and PCA. Journal of Scientific & Industrial Research, 80, 159-165. https://doi.org/10.56042/jsir.v80i02.40102 |
| [16] | Ko, H., Lee, S., Park, Y., et al. (2022) A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields. Electronics, 11, Article 141. https://doi.org/10.3390/electronics11010141 |
| [17] | Thejaswini, N. and Aditya, C.R. (2019) Smart E-Commerce Recommendation System for Handling Limited Resource and Cold Start Problem. International Journal of Computer Sciences and Engineering, 7, 961-964. https://doi.org/10.26438/ijcse/v7i5.961964 |
| [18] | Wadhwani, K. and Jain, V. (2023) Multichannel Consumer Service Integration of Recommendation Systems. In: Sheth, J.N., Jain, V., Mogaji, E. and Ambika, A., Eds., Customer Centric Support Services in the Digital Age, Palgrave Macmillan, Cham, 101-129. https://doi.org/10.1007/978-3-031-37097-7_6 |
| [19] | Wu, W. and Xu, Y. (2022) Information Personalized Recommendation Algorithm of Cross-Border E-Commerce Shopping Guide Platform Based on Multi-Objective Optimization. 2022 Euro-Asia Conference on Frontiers of Computer Science and Information Technology (FCSIT), Beijing, 16-18 December 2022, 198-200. https://doi.org/10.1109/FCSIT57414.2022.00048 |
| [20] | Mi, J.X., Feng, J. and Huang, K.Y. (2022) Designing Efficient Convolutional Neural Network Structure: A Survey. Neurocomputing, 489, 139-156. https://doi.org/10.1016/j.neucom.2021.08.158 |
| [21] | Namatēvs, I. (2017) Deep Convolutional Neural Networks: Structure, Feature Extraction and Training. Information Technology and Management Science, 20, 40-47. https://doi.org/10.1515/itms-2017-0007 |
| [22] | Li, Z., Liu, F., Yang, W., et al. (2021) A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Transactions on Neural Networks and Learning Systems, 33, 6999-7019. https://doi.org/10.1109/TNNLS.2021.3084827 |
| [23] | Simonyan, K. and Zisserman, A. (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv: 1409.1556,. |
| [24] | Zhu, Q., Zhang, S. and Sun, Y. (2022) Recommendation Algorithm Research Based on MPA. 2022 2nd Asia Conference on Information Engineering (ACIE), Haikou, 15-17 January 2022, 24-29. https://doi.org/10.1109/ACIE55485.2022.00013 |
| [25] | Balakrishnan, S. and Chopra, S. (2012) Collaborative Ranking. Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, Seattle, 8-12 February 2012, 143-152. https://doi.org/10.1145/2124295.2124314 |
