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Smart Agriculture ›› 2024, Vol. 6 ›› Issue (1): 123-134.doi: 10.12133/j.smartag.SA202312012

• 信息处理与决策 • 上一篇    下一篇

融合时间感知和增强过滤的农业知识推荐模型

王鹏哲1,2(), 朱华吉2,3,4, 缪祎晟2,3,4, 刘畅2,3,4, 吴华瑞1,2,3,4()   

  1. 1. 广西大学 计算机与电子信息学院,广西 南宁 530000,中国
    2. 国家农业信息化工程技术研究中心,北京 100097,中国
    3. 北京市农林科学院信息技术研究中心,北京 100097,中国
    4. 农业农村部数字乡村技术重点实验室,北京 100097,中国
  • 收稿日期:2023-12-14 出版日期:2024-01-30
  • 作者简介:
    王鹏哲,研究方向为农业知识推荐。E-mail:

    WANG Pengzhe, E-mail:

  • 通信作者:
    吴华瑞,博士,研究员,研究方向为农业智能系统与物联网。E-mail:

Agricultural Knowledge Recommendation Model Integrating Time Perception and Context Filtering

WANG Pengzhe1,2(), ZHU Huaji2,3,4, MIAO Yisheng2,3,4, LIU Chang2,3,4, WU Huarui1,2,3,4()   

  1. 1. College of Computer and Electronic Information, Guangxi University, Nanning 530004, China
    2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    3. Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    4. Key Laboratory of Digital Village Technology, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
  • Received:2023-12-14 Online:2024-01-30
  • corresponding author:
    WU Huarui, E-mail:
  • Supported by:
    National Key Research and Develpment Program(2022YFD1600602)

摘要:

目的/意义 农业场景下的知识服务具有周期性长、活动时间长的特点。传统推荐模型无法有效挖掘农业场景下的基于农时的隐藏信息。针对上述问题,提出一种融合时间感知和增强过滤的农业知识个性化推荐模型(Time-aware and Filter-enhanced Sequential Recommendation Model for Agriculture Knowledge, TiFSA)。 方法 首先,基于时间感知的位置嵌入方法,将农户交互的时间信息与位置嵌入相结合,帮助学习农业情境下基于农时的项目相关性。其次,在时间感知位置嵌入的基础上,引入滤波器过滤算法,自适应地衰减农户情境数据中的噪声。最后,引入时间信息的多头自注意力网络,实现对时间、项目和特征的统一建模,对农户随时间变化的偏好特征进行情境表示,从而为用户提供可靠的推荐结果。 结果和讨论 根据“全国农业知识智能服务云平台”中的用户交互序列数据集进行实验。结果表明,该模型在农业数据集上的命中率为45.79%,归一化折损累计增益为53.52%;与近几年性能最佳的模型Ti-SASRec相比分别提升16.19%和14.02%。 结论 该模型能够有效捕获农业领域的用户情境特征和建模农户的动态偏好,具有更好的推荐性能。

关键词: 农业知识推荐, 滤波器算法, 时间感知, 自注意力网络, 序列推荐

Abstract:

Objective Knowledge services in agricultural scenarios have the characteristics of long periodicity and prolonged activity time. Traditional recommendation models cannot effectively mine hidden information in agricultural scenarios, in order to improve the quality of agricultural knowledge recommendation services, agricultural contextual information based on agricultural time should be fully considered. To address these issues, a Time-aware and filter-enhanced sequential recommendation model for agricultural knowledge (TiFSA) was proposed, integrating temporal perception and enhanced filtering. Methods First, based on the temporal positional embedding, combining the temporal information of farmers' interactions with positional embedding based on time perception, it helped to learn project relevance based on agricultural season in agricultural contexts. A multi-head self-attention network recommendation algorithm based on time-awareness was proposed for the agricultural knowledge recommendation task, which extracted different interaction time information in the user interaction sequence and introduced it into the multi-head self-attention network to calculate the attention weight, which encoded the user's periodic interaction information based on the agricultural time, and also effectively captured the user's dynamic preference information over time. Then, through the temporal positional embedding, a filter filtering algorithm was introduced to adaptively attenuate the noise in farmers' situational data adaptively. The filtering algorithm was introduced to enhance the filtering module to effectively filter the noisy information in the agricultural dataset and alleviate the overfitting problem due to the poorly normalized and sparse agricultural dataset. By endowing the model with lower time complexity and adaptive noise attenuation capability. The applicability of this method in agricultural scenarios was improved. Next, a multi-head self attention network with temporal information was constructed to achieve unified modeling of time, projects, and features, and represent farmers' preferences of farmers over time in context, thereby providing reliable recommendation results for users. Finally, the AdamW optimizer was used to update and compute the model parameters. AdamW added L2 regularization and an appropriate penalty mechanism for larger weights, which could update all weights more smoothly and alleviate the problem of falling into local minima. Applied in the field of agricultural recommendation, it could further improve the training effect of the model. The experimental data came from user likes, comments, and corresponding time information in the "National Agricultural Knowledge Intelligent Service Cloud Platform", and the dataset ml-1m in the movie recommendation scenario was selected as an auxiliary validation of the performance of this model. Results and Discussions According to the user interaction sequence datasets in the "National Agricultural Knowledge Intelligent Service Cloud Platform", from the experimental results, it could be learned that TiFSA outperforms the other models on two different datasets, in which the enhancement was more obvious on the Agriculture dataset, where HR and NDCG were improved by 14.02% and 16.19%, respectively, compared to the suboptimal model, TiSASRec; while on the ml-1m dataset compared to the suboptimal model, SASRec, HR and NDCG were improved by 1.90% and 2.30%, respectively. In summary, the TiFSA model proposed in this paper has a large improvement compared with other models, which verifies verified the effectiveness of the TiFSA model and showed that the time interval information of farmer interaction and the filtering algorithm play an important role in the improvement of the model performance in the agricultural context. From the results of the ablation experiments, it could be seen that when the time-aware and enhanced filtering modules were removed, the values of the two metrics HR@10 and NDCG@10 were 0.293 6 and 0.203 9, respectively, and the recommended performance was poor. When only the time-aware module and only the augmentation filtering module were removed, the experimental results had different degrees of improvement compared to TiFSA-tf, and the TiFSA model proposed in this paper achieved the optimal performance in the two evaluation metrics. When only the multi-head self-attention network was utilized for recommendation, both recommendation metrics of the model were lower, indicating that the traditional sequence recommendation method that only considered the item number was not applicable to agricultural scenarios. When the augmented filtering module was introduced without the time-aware module, the model performance was improved, but still failed to achieve the ideal recommendation effect. When only the time-aware module was introduced without the augmented filtering module, there was a significant improvement in the model effect, which proved that the time-aware module was more applicable to agricultural scenarios and can effectively improve the model performance of the sequence recommendation task. When both time-aware and augmented filtering modules were introduced, the model performance was further improved, which on the one hand illustrated the dependence of the augmented filtering module on the time-aware module, and on the other hand verified the necessity of adopting the augmented filtering to the time-aware self-attention network model. Conclusions This research proposes an agricultural knowledge recommendation model that integrates time-awareness and augmented filtering, which introduces the user's interaction time interval into the embedded information, so that the model effectively learns the information of agricultural time in the agricultural scene, and the prediction of the user's interaction time and the object is more closely related to the actual scene; augmented filtering algorithms are used to attenuate the noise in the agricultural data. At the same time, the enhanced filtering algorithm is used to attenuate the noise in the agricultural data, and can be effectively integrated into the model for use, further improving the recommendation performance of the model. The experimental results show the effectiveness of the proposed TiFSA model on the agricultural dataset. The ablation experiments confirm the positive effect of time-awareness and enhanced filtering modules on the improvement of recommendation performance.

Key words: agricultural knowledge recommendation, filter algorithm, temporal perception, self-attention network, sequence recommendation

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