随着水利信息化建设的逐步深入,水情信息的实时推荐需求越来越强烈。水利数据具有很强的时效性,要求推荐系统能够提供实时推荐服务。基于用户的协同过滤算法和基于信息的协同过滤算法(Item-based Collaborative Filtering,ItemCF)是推荐领域常用的2种算法,但两者在本质上都属于离线算法,不能满足水情信息分发实时性要求。提出了一种基于长短期记忆神经网络(Long-Short-Term Memory,LSTM)的水情信息分发实时推荐算法并对其优化。实验结果表明:基于LSTM的实时推荐算法在推荐时延方面最优,而优化的结合二分类模型和ItemCF推荐结果的实时推荐算法在推荐准确率方面最优,设计实现优化的基于LSTM的实时推荐算法综合效果较好,在保证水情信息推荐准确性的同时保证了推荐实时性。
Abstract
The demand for real-time recommendation of water information is growing stronger with the deepening of water conservancy informatization in China. Since the data of water is highly time-sensitive, recommendation system is required to provide real-time recommendation services. User-based collaborative filtering and item-based collaborative filtering (ItemCF) are two commonly used algorithms in the recommendation field. Both, however, are offline algorithms in nature and cannot meet the requirement of real-time distribution of water information. In this paper, a real-time recommendation algorithm for water regime information distribution based on Long-Short-Term Memory (LSTM) is proposed and optimized to ensure the accuracy of water information recommendation while ensuring the real-time recommendation.
关键词
水情信息 /
分发 /
实时推荐 /
ItemCF /
LSTM /
二分类模型 /
优化
Key words
water information distribution /
real-time recommendation /
ItemCF /
LSTM /
dichotomous model /
optimization
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