|
|
基于用户显式与隐式反馈的购物推荐算法
|
Abstract:
商业网站通常采用推荐系统来处理大量信息。新用户行为数据的缺乏导致系统无法为其提供推荐，这一问题被称为冷启动问题。此外，这些算法严重依赖于稀疏的显式反馈数据，使得为用户提供精确推荐变得具有挑战性，从而引发了数据稀疏性问题。为了解决这些问题，本文提出了一种基于用户显式和隐式反馈的购物推荐算法。首先，该算法使用特殊过滤器提取特征并生成候选列表。其次，通过交互学习模型和多隐式反馈学习模型，从候选列表中每个项目的评分数据中提取隐式关系。多隐式反馈学习模型从三个角度分析隐式关系，并将这三种隐式反馈作为辅助数据来解决数据稀疏性问题。最后，使用Tafeng数据集和Book Crossing数据集验证了该算法在购物推荐中的有效性。
Business websites typically employ recommendation systems to process large amounts of information. The lack of behavioral data for new users leads to the system’s inability to make recommendations for them, which is known as the cold-start problem. Additionally, these algorithms heavily rely on sparse explicit feedback data, making it challenging to provide users with precise recommendations, leading to the data sparsity problem. To address these issues, a recommendation algorithm for shopping based on explicit and implicit feedback from the user is proposed. Firstly, the algorithm uses special filters to extract features and generate a candidate list. Secondly, the interaction learning model and the multi-implicit feedback learning model are used to extract implicit relationships from the rating data for each item in the candidate list. The multi-implicit feedback learning model analyzes the implicit relationship from three perspectives. The three types of implicit feedback are used as auxiliary data to solve the data sparsity problem. The effectiveness of the algorithm in shopping recommendation is validated using the Tafeng dataset and the BookCrossing dataset.
| [1] | Roy, D. and Dutta, M. (2022) A Systematic Review and Research Perspective on Recommender Systems. Journal of Big Data, 9, Article No. 59. https://doi.org/10.1186/s40537-022-00592-5 |
| [2] | Li, J., Ren, Y. and Deng, K. (2022) FairGAN: GANs-Based Fairness-Aware Learning for Recommendations with Implicit Feedback. Proceedings of the ACM Web Conference 2022, Virtual, 25-29 April 2022, 297-307. https://doi.org/10.1145/3485447.3511958 |
| [3] | Wang, G., He, X. and Ishuga, C.I. (2018) HAR-SI: A Novel Hybrid Article Recommendation Approach Integrating with Social Information in Scientific Social Network. Knowledge-Based Systems, 148, 85-99. https://doi.org/10.1016/j.knosys.2018.02.024 |
| [4] | Pradhan, T. and Pal, S. (2020) CNAVER: A Content and Network-Based Academic Venue Recommender System. Knowledge-Based Systems, 189, Article 105092. https://doi.org/10.1016/j.knosys.2019.105092 |
| [5] | Kang, N., Doornenbal, M.A. and Schijvenaars, R.J.A. (2015) Elsevier Journal Finder: Recommending Journals for your Paper. Proceedings of the 9th ACM Conference on Recommender Systems, Vienna, 16-20 September 2015, 261-264. https://doi.org/10.1145/2792838.2799663 |
| [6] | Schafer, J.B., Frankowski, D., Herlocker, J. and Sen, S. (n.d.) Collaborative Filtering Recommender Systems. In: Brusilovsky, P., Kobsa, A. and Nejdl, W., Eds., The Adaptive Web, Springer, 291-324. https://doi.org/10.1007/978-3-540-72079-9_9 |
| [7] | Dong, B., Zhu, Y., Li, L. and Wu, X. (2021) Hybrid Collaborative Recommendation of Co-Embedded Item Attributes and Graph Features. Neurocomputing, 442, 307-316. https://doi.org/10.1016/j.neucom.2021.01.129 |
| [8] | Zhang, F. (2014) A Matrix Decomposition and Its Applications. Linear and Multilinear Algebra, 63, 2033-2042. https://doi.org/10.1080/03081087.2014.933219 |
| [9] | Abdi, H. (2007) Singular Value Decomposition (SVD) and Generalized Singular Value Decomposition. Encyclopedia of Measurement and Statistics, 907, 44. |
| [10] | Luo, X., Liu, H., Gou, G., Xia, Y. and Zhu, Q. (2012) A Parallel Matrix Factorization Based Recommender by Alternating Stochastic Gradient Decent. Engineering Applications of Artificial Intelligence, 25, 1403-1412. https://doi.org/10.1016/j.engappai.2011.10.011 |
| [11] | Gridach, M. (2020) Hybrid Deep Neural Networks for Recommender Systems. Neurocomputing, 413, 23-30. https://doi.org/10.1016/j.neucom.2020.06.025 |
| [12] | Raziperchikolaei, R., Li, T. and Chung, Y. (2021) Neural Representations in Hybrid Recommender Systems: Prediction versus Regularization. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual, 11-15 July 2021, 1743-1747. https://doi.org/10.1145/3404835.3463051 |
| [13] | Ni, S., Hu, S. and Li, L. (2024) An Intention-Aware Markov Chain Based Method for Top-K Recommendation. IEEE Transactions on Automation Science and Engineering, 21, 581-592. https://doi.org/10.1109/tase.2022.3230783 |
| [14] | Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., et al. (2021) Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions. Journal of Big Data, 8, Article No. 53. https://doi.org/10.1186/s40537-021-00444-8 |
| [15] | Corciolani, M. and Dalli, D. (2014) Gift-giving, Sharing and Commodity Exchange at Bookcrossing.com: New Insights from a Qualitative Analysis. Management Decision, 52, 755-776. https://doi.org/10.1108/md-03-2012-0241 |
| [16] | Yu, F., Liu, Q., Wu, S., Wang, L. and Tan, T. (2016) A Dynamic Recurrent Model for Next Basket Recommendation. Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pisa, 17-21 July 2016, 729-732. https://doi.org/10.1145/2911451.2914683 |
| [17] | Willmott, C. and Matsuura, K. (2005) Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in Assessing Average Model Performance. Climate Research, 30, 79-82. https://doi.org/10.3354/cr030079 |
| [18] | Hodson, T.O. (2022) Root Mean Square Error (RMSE) or Mean Absolute Error (MAE): When to Use Them or Not. Geoscientific Model Development Discussions, 15, 5481-5487. https://doi.org/10.5194/gmd-15-5481-2022 |
| [19] | Terzi, M., Rowe, M., Ferrario, M. and Whittle, J. (2014) Text-Based User-kNN: Measuring User Similarity Based on Text Reviews. User Modeling, Adaptation and Personalization, Aalborg, 7-11 July 2014, 195-206. https://doi.org/10.1007/978-3-319-08786-3_17 |
| [20] | Hidasi, B., Karatzoglou, A., Baltrunas, L. and Tikk, D. (2015) Session-Based Recommendations with Recurrent Neural Networks. arXiv: 1511.06939. https://doi.org/10.48550/arXiv.1511.06939 |
| [21] | Xian, Z., Li, Q., Li, G. and Li, L. (2017) New Collaborative Filtering Algorithms Based on SVD++ and Differential Privacy. Mathematical Problems in Engineering, 2017, Article ID: 1975719. https://doi.org/10.1155/2017/1975719 |
| [22] | Zamanzadeh Darban, Z. and Valipour, M.H. (2022) GHRS: Graph-Based Hybrid Recommendation System with Application to Movie Recommendation. Expert Systems with Applications, 200, Article 116850. https://doi.org/10.1016/j.eswa.2022.116850 |
