Design and implementation of Elasticsearch-based intelligent search for home shopping platform

Abstract

The search function of shopping software is essential, and good recommendations can increase the user's desire to buy. The fuzzy search and image similarity search proposed in this paper is a new retrieval method built on Elasticsearch, which can speed up the search and improve the retrieval's correctness. Its support for various complex texts dramatically facilitates the development of this project. This search type is used in home shopping software to improve the user's comfort significantly. The text is developed and designed based on the Golang language, whose high concurrency and excellent library functions help implement the functionality extensively. The user side is presented as a WeChat applet, which lowers the threshold of use and increases the dependency of users. With Elasticsearch's support for multiple languages and its unique vector search and text embedding features, the system can train models such as Contrastive Language-Image Pretraining (CLIP) and Natural Language Processing (NLP) on different images and languages, improving the search's accuracy. For the model generated by the training, vector search is performed to achieve the purpose of the search, and finally, the search results are returned to the front-end applet page for exhibition.