Abstract
Abstract In this article, based on the collaborative deep learning (CDL) and convolutional matrix factorisation (ConvMF), the language model BERT is used to replace the traditional word vector construction method, and the bidirectional long–short time memory network Bi-LSTM is used to construct an improved collaborative filtering model BMF, which not only solves the phenomenon of ‘polysemy’, but also alleviates the problem of sparse scoring matrix data. Experiments show that the proposed model is effective and superior to CDL and ConvMF. The trained MSE value is 1.031, which is 9.7% lower than ConvMF.
Highlights
Along with the popularisation of the internet, e-commerce has gradually integrated into people’s lives and become an integral part
All these e-commerce platforms provide users with good shopping experience by constructing multiple recommendation models according to their own business characteristics. They have driven the wave of online sales of agricultural products. This shows the importance of recommendation system in the field of agricultural products e-commerce
Stacked Denoising Auto-Encoder (SDAE) in collaborative deep learning (CDL) uses bag-of-words to represent the vectors of the text
Summary
Along with the popularisation of the internet, e-commerce has gradually integrated into people’s lives and become an integral part. The success of Amazon has led to a new wave of application of recommendation systems in the e-commerce field. Based on the concept of ‘precise poverty alleviation’, e-commerce of agricultural products is gradually entering people’s vision as a new economic income-generating point. There are been e-commerce giants of agricultural products, such as JD.COM Fresh, Fruit Day, No 1 Fresh and HQW.COM, in China. All these e-commerce platforms provide users with good shopping experience by constructing multiple recommendation models according to their own business characteristics. They have driven the wave of online sales of agricultural products. A good recommendation system can improve users’ loyalty, and create a lot of economic growth
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