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融合效用与兴趣的在线用户健康干预推荐
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Abstract:
面向在线健康社区的推荐，系统需要推荐用户感兴趣且能改善其健康状况的干预方案。为此，本文提出了融合效用与兴趣的在线用户健康干预推荐模型，先利用注意力机制对用户兴趣进行学习，同时计算待推荐方案对其参与者的预计疗效，接着评估干预方案对目标用户的效用，最终结合方案效用和用户兴趣得出推荐结果。实验结果证明与其他推荐模型相比，本文模型有最好的推荐结果且能提高推荐的方案对于用户的效用。
The recommendation systems tailored for online health communities necessitate suggesting interventions that align with users’ interests while also enhancing their health outcomes. In this regard, this paper proposes an integrated online health intervention recommendation model, which amalgamates utility and interest considerations. Initially, an attention mechanism is employed to discern user interests, concurrently computing the anticipated therapeutic effects of candidate interventions for their participants. Subsequently, the utility of intervention plans for target users is evaluated, culminating in the fusion of scheme utility and user interest to derive recommendation outcomes. Experimental findings corroborate that compared to alternative recommendation models, the proposed model yields superior recommendation results and enhances the utility of recommended schemes for users.
| [1] | Ying, S., Hoens, T.R., Jian, J., et al. (2016) Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data, San Francisco, 13-17 August 2016, 255-262. https://doi.org/10.1145/2939672.2939704 |
| [2] | Cheng, H.T., Koc, L., Harmsen, J., et al. (2016) Wide & Deep Learning for Recommender Systems. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, Boston, 15 September 2016, 7-10. https://doi.org/10.1145/2988450.2988454 |
| [3] | Covington, P., Adams, J. and Sargin, E. (2016) Deep Neural Networks for YouTube Recommendations. Proceedings of the 10th ACM Conference on Recommender Systems, Boston, 15-19 September 2016, 191-198. https://doi.org/10.1145/2959100.2959190 |
| [4] | Qu, Y., Han, C., Kan, R., et al. (2016) Product-Based Neural Networks for User Response Prediction. 2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, 12-15 December 2016, 1149-1154. https://doi.org/10.1109/ICDM.2016.0151 |
| [5] | Guo, H., Tang, R., Ye, Y., et al. (2017) DeepFM: A Factorization-Machine Based Neural Network for CTR Prediction. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, 19-25 August 2017, 1725-1731. https://doi.org/10.24963/ijcai.2017/239 |
| [6] | Xiao, J., Ye, H., He, X., et al. (2017) Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, 19-25 August 2017, 3119-3125. https://doi.org/10.24963/ijcai.2017/435 |
| [7] | Lu, Y., Zhang, S., Huang, Y., et al. (2021) Future-Aware Diverse Trends Framework for Recommendation. Proceedings of the Web Conference 2021, Ljubljana, 19-23 April 2021, 2992-3001. https://doi.org/10.1145/3442381.3449791 |
| [8] | Nassar, N., Jafar, A. and Rahhal, Y. (2020) A Novel Deep Multi-Criteria Collaborative Filtering Model for Recommendation System. Knowledge-Based Systems, 187, Article 104811. https://doi.org/10.1016/j.knosys.2019.06.019 |
| [9] | Koren, Y., Bell, R. and Volinsky, C. (2009) Matrix Factorization Techniques for Recommender Systems. Computer, 42, 30-37. https://doi.org/10.1109/MC.2009.263 |
| [10] | Gai, K., Zhu, X., Li, H., et al. (2017) Learning Piece-Wise Linear Models from Large Scale Data for Ad Click Prediction. arXiv:1704.05194. https://doi.org/10.48550/arXiv.1704.05194 |
| [11] | Rendle, S. (2010) Factorization Machines. 2010 IEEE International Conference on Data Mining, Sydney, 13-17 December 2010, 995-1000. https://doi.org/10.1109/ICDM.2010.127 |
| [12] | Bahdanau, D., Cho, K. and Bengio, Y. (2014) Neural Machine Translation by Jointly Learning to Align and Translate. arXiv:1409.0473. https://doi.org/10.48550/arXiv.1409.0473 |
| [13] | Huang, T., Zhang, Z. and Zhang, J. (2019) FiBiNET: Combining Feature Importance and Bilinear Feature Interaction for Click-through Rate Prediction. Proceedings of the 13th ACM Conference on Recommender Systems, Copenhagen, 16-20 September 2019, 169-177. https://doi.org/10.1145/3298689.3347043 |
| [14] | Hu, J., Shen, L. and Sun, G. (2019) Squeeze-and-Excitation Networks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 18-23 June 2018, 7132-7141. https://doi.org/10.1109/CVPR.2018.00745 |
| [15] | Qiang, L., Feng, Y., Wu, S., et al. (2015) A Convolutional Click Prediction Model. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, Melbourne, 18-23 October 2015, 1743-1746. https://doi.org/10.1145/2806416.2806603 |
| [16] | Liu, B., Tang, R., Chen, Y., et al. (2019) Feature Generation by Convolutional Neural Network for Click-through Rate Prediction. The World Wide Web Conference, San Francisco, 13-17 May 2019, 1119-1129. https://doi.org/10.1145/3308558.3313497 |
| [17] | Zhou, G., Song, C., Zhu, X., et al. (2017) Deep Interest Network for Click-through Rate Prediction. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, 19-23 August 2018, 1059-1068. https://doi.org/10.1145/3219819.3219823 |
| [18] | Zhou, G., Mou, N., Fan, Y., et al. (2019) Deep Interest Evolution Network for Click-through Rate Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 5941-5948. https://doi.org/10.1609/aaai.v33i01.33015941 |
| [19] | Deng, F. (2015) Utility-Based Recommender Systems Using Implicit Utility and Genetic Algorithm. Proceedings of the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering, Shenyang, 1-4 April 2015, 860-864. https://doi.org/10.2991/meic-15.2015.197 |
| [20] | Huang, S.L. (2011) Designing Utility-Based Recommender Systems for E-Commerce: Evaluation of Preference-Elicitation Methods. Electronic Commerce Research and Applications, 10, 398-407. https://doi.org/10.1016/j.elerap.2010.11.003 |
| [21] | Li, Z., Fang, X., Bai, X., et al. (2015) Utility-Based Link Recommendation for Online Social Networks. Management Science, 63, 1938-1952. https://doi.org/10.1287/mnsc.2016.2446 |
| [22] | Mz, A., Aa, B., Xing, Z.C., et al. (2019) A Utility-Based News Recommendation System. Decision Support Systems, 117, 14-27. https://doi.org/10.1016/j.dss.2018.12.001 |
| [23] | Sangeetha, S., Sudha Sadasivam, G. and Latha, R. (2021) Utility-Based Differentially Private Recommendation System. Big Data, 9, 203-218. https://doi.org/10.1089/big.2020.0038 |
| [24] | 耿秀丽, 王著鑫. 考虑用户兴趣分析的差分隐私方案推荐[J]. 计算机应用研究, 2022, 39(2): 474-478. |
| [25] | He, X., Liao, L., Zhang, H., et al. (2017) Neural Collaborative Filtering. Proceedings of the 26th International Conference on World Wide Web, Perth, 3-7 April 2017, 173-182. https://doi.org/10.1145/3038912.3052569 |
| [26] | Xue, H.J., Dai, X., Zhang, J., et al. (2017) Deep Matrix Factorization Models for Recommender Systems. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, 19-25 August 2017, 3203-3209. https://doi.org/10.24963/ijcai.2017/447 |
| [27] | He, X. and Chua, T.S. (2017) Neural Factorization Machines for Sparse Predictive Analytics, Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, 7-11 August 2017, 355-364. https://doi.org/10.1145/3077136.3080777 |
