Product clustering method and apparatus

Abstract

The present application relates to electronic commerce technology, and particularly relates to a product clustering method and apparatus. The method comprises: based on similarity between products, sorting out products with similarity meeting a preset condition; determining cluster center products among the sorted out products based on a preset principle; and based on each cluster center product, classifying each non-cluster center product to a same cluster as a cluster center product with highest similarity to the non-cluster center product. The above method is not limited by the number of clusters, the product similarity needs to be calculated only once to construct a similarity network and achieve product clustering progressively based on a heuristic algorithm. In this way, not only the accuracy of the clustering result can be significantly improved, but also time complexity and spatial complexity of the product clustering can be greatly reduced, thus preventing a heavy operation load from being caused to a system and controlling the implementation cost within an ideal range. The method and apparatus are particularly applicable to large-scale product clustering.

Description

technical field [0001] The present application relates to e-commerce technology, in particular to a product clustering method and device. Background technique [0002] With the development of e-commerce technology, the number of products displayed on e-commerce websites is increasing day by day, and the calculation complexity of the similarity between products is very high. Usually, e-commerce websites have hundreds of millions of users, and user behaviors are also very rich. However, due to the huge amount of product data, the user's operation behavior on the product (such as clicking, purchasing, collecting, etc.) is very sparse. Due to the sparsity of user-to-product data, when calculating user preferences and calculating user similarity and other parameters, the coverage rate is often not high, and it also affects accuracy. [0003] In view of the above problems, under the existing technology, products that are sufficiently similar are usually aggregated into a cluster...

AI Technical Summary

Problems solved by technology

[0008] The embodiment of the present application provides a product clustering method and device to solve the problem existing in the prior art in order to achieve accurate clustering of massive products, thereby increasing the operating load of the system

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