Configuring products with natural language: a simple yet effective approach based on text embeddings and multilayer perceptron

Abstract

Product configurators are recognised as critical toolkits enabling customers to co-create products with companies. Most available product configurators require customers to select suitable product attributes from predefined options. However, customers usually find the selection processes frustrating due to their lack of product knowledge. In view of the fact that customers often express their needs in imprecise and vague natural language, we define a new needs-based configuration mechanism and propose an implementation approach based on text embeddings and multilayer perceptron. Specifically, we leverage the massive amount of product reviews by encoding them into text embeddings. A multilayer perceptron is trained to map text embeddings to product attribute options. Experiment results indicate that the mapping has good generalisation capability to map customer needs into product configurations. The performance of our approach is comparable to that of deep learning-based approaches but with much higher efficiency in terms of computational complexity. Our needs-based configuration thus provides a quick and effective means of facilitating product customisation. It also demonstrates an innovative way of utilising customer resources in unstructured text to co-create products with companies.

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