Deep cross-platform product matching in e-commerce
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Online shopping has become more and more popular in recent years, which leads to a prosperity on online platforms. Generally, the identical products are provided by many sellers on multiple platforms. Thus the comparison between products on multiple platforms becomes a basic demand for both consumers and sellers. However, identifying identical products on multiple platforms is difficult because the description for a certain product can be various. In this work, we propose a novel neural matching model to solve this problem. Two kinds of descriptions (i.e. product titles and attributes), which are widely provided on online platforms, are considered in our method. We conduct experiments on a real-world data set which contains thousands of products on two online e-commerce platforms. The experimental results show that our method can take use of the product information contained in both titles and attributes and significantly outperform the state-of-the-art matching models.
KeywordsProduct matching Neural network Text matching
This work was supported by National Key R&D Program of China No. 2018YFC0830703, National Natural Science Foundation of China No. 61872370, and the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China No. 2112018391.
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