Abstract: A system for associating a string of natural language with items in a relational database includes a first subsystem having a pre-trained first artificial neural network configured to apply a semantic tag selected from a predefined set of semantic labels to a segment of a plurality of tokens representing the string of natural language. A second subsystem includes a second artificial neural network configured to convert the plurality of labeled tokens into a first multi-dimensional vector representing the string of natural language. A third subsystem is configured to rank the first multi-dimensional vector against a second multi-dimensional vector representing a plurality of items in the relational database.

Abstract: Example implementations include a system and method of recognizing behavior of a user. In example implementations, a first post and at least one subsequent post indicative of a product and associated with a first social media account is obtained. A relevance probability is calculated for each of the obtained first post and the at least one subsequent post. The obtained first post and the at least one subsequent post are sequentially analyzed by a second neural network to determine output values relevant to probability of purchasing the product. A probability of purchasing the product is calculated based on the determined output values associated with each post and the calculated relevance probabilities. Product-related information is transmitted to the user associated with the obtained first post based on the determined probability of purchasing the product.

Abstract: A system for associating a string of natural language with items in a relational database includes a first subsystem having a pre-trained first artificial neural network configured to apply a semantic tag selected from a predefined set of semantic labels to a segment of a plurality of tokens representing the string of natural language. A second subsystem includes a second artificial neural network configured to convert the plurality of labeled tokens into a first multi-dimensional vector representing the string of natural language. A third subsystem is configured to rank the first multi-dimensional vector against a second multi-dimensional vector representing a plurality of items in the relational database.

Abstract: Example implementations include a system and method of recognizing behavior of a user. In example implementations, a first post and at least one subsequent post indicative of a product and associated with a first social media account is obtained. A relevance probability is calculated for each of the obtained first post and the at least one subsequent post. The obtained first post and the at least one subsequent post are sequentially analyzed by a second neural network to determine output values relevant to probability of purchasing the product. A probability of purchasing the product is calculated based on the determined output values associated with each post and the calculated relevance probabilities. Product-related information is transmitted to the user associated with the obtained first post based on the determined probability of purchasing the product.