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- W4383560219 abstract "The field of e-commerce provides customers with many kinds of goods to choose from. It is also a challenge for consumers to choose among tens of thousands of product categories. The emergence of recommender systems solves such problems for consumers to a certain extent because it would recommend products that may fulfil their needs for consumers. Finding user preferences or rankings for a specific item can be aided by recommender systems. It uses specialized software and techniques to offer suggestions for items utilized by various users. To increase customer satisfaction or sell different things for e-commerce platforms, recommender systems advise consumers about specific items they may be interested in or desire to utilize. Graph neural networks generalize effectively to new datasets because they can learn the graph structure of various datasets. To put it another way, this makes Graph Neural Networks (GNNs) the perfect recommender systems for various real-world issues. As a result, the growth and usage of neural graph networks in recommender systems and the limitations of graph neural networks will be covered in detail in this paper." @default.
- W4383560219 created "2023-07-08" @default.
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- W4383560219 date "2023-06-14" @default.
- W4383560219 modified "2023-10-07" @default.
- W4383560219 title "The advance of recommendation system with graph neural network" @default.
- W4383560219 doi "https://doi.org/10.54254/2755-2721/5/20230700" @default.
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