Buying Intent Pipeline in More Detail

Statistics of the co-occurrence of normalized forms of particular words and phrases are the basis of all NLP. But to capture the meaning in a way that matches the language-independent thinking of an expert we need to have something more than just these descriptions of surface form distribution. To achieve this we regard a set of keywords or a piece of content as a guide for navigation in a semantic model for a particular domain. A query, a web page, or any other content, will be mapped to a specific neighbourhood in a semantic space representing expert knowledge for a particular domain. This mapping is based on Deep Learning and other Machine Learning techniques.

The platform consists of four internal stages of the pipeline:

  1. Crawling of the online content and the extraction of main content and metadata.
  2. Generation of contextual keywords that represent the features of the content that enable maximally precise prediction in the next phase. In essence they represent a particular set of possible sub-contexts with weights, selected automatically to maximize the precision of the next phase.
  3. Buying Intent Prediction. In this stage the system classifies over contextual variables, given the contextual keyword generation stage output.
  4. Relevance Matching, where the analyzed URLs closest to the desired target (in the contextual sense), are fetched from a database and returned as the result.

The Architecture

Our approach is based on an architecture of scalable microservices, using modern frameworks, and hosted in the cloud. These are the technical choices we have made:

Platforms: AWS, Github 

Languages: Python, Javascript, HTML, CSS

Database: MongoDB, Redis, Hbase

Message Queues: SQS, Apache Kafka.

The Team

NinaData’s technology team combines extensive industry experience with technical know-how and experience in building a scalable high-value and high-performance architecture for AI analytics of media content. Our data scientists and management combine methodological skills with practical application experience. They have worked in the past as AI engineers, Machine Learning researchers, AI business managers and co-founders of successful startups.