Investigation of the influence of textual representation of goods by means of models using artificial neural networks of deep learning on the relevance of the search for goods on the electronic trading Internet platform
Abstract
With the growing popularity of online shopping, academic research in the field of e-commerce is becoming increasingly relevant. Nevertheless, significant research problems remain, starting with classical search problems in e-commerce, for example, the comparison of text queries with multimodal representations of goods, optimization of ranking for bilateral Internet trading platforms, taking into account the interaction of buyer and seller with recommendation and search engines. These areas of research are important for understanding customer behavior, stimulating engagement, and increasing conversions. Product data plays a key role in search and recommendations. Services in which there are several suppliers (marketplaces) on the electronic trading Internet platform demonstrate high dynamics in terms of the quality and consistency of content, fraud detection and pricing. Inventory updates are often accompanied by service level agreements that guarantee that changes will be made in accordance with customer requirements within a strictly defined time frame. Finally, the goods themselves can be considered as multimodal documents, and the success of the search depends on the compliance of the client's intentions with all aspects of the goods. For the sake of specificity, we will use the term "documents" for the elements returned by the search query, although the returned elements may be more general (for example, multimedia elements).
When searching on an online electronic trading platform, the indexed products that buyers are looking for are combinations of images, videos and unstructured text (names, descriptions and reviews) and structured data (price, brand, ratings, seller's location, and logistics). This combination of data opens up prospects for research, including improving the extraction of data from a variety of sources using signals from different types of data, such as providing more detailed color information and using image similarity for recommendations. and also as a way for customers to create queries in a search engine. Data is the key to providing high-quality search and recommendations that meet the needs of customers and businesses. In this article, the author explored the possibilities of several approaches to extracting useful signals from various sources of product information to improve the customer experience in the field of e-commerce.
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