Technology
Technology behind
The most popular approach to implementing the recommendation in e-commerce is using out-of-the-box solutions as they are easy to start and offer no additional time to integrate or adjust. For most e-commerce companies, this would be the best solution to begin with, but it comes with a cost. Companies differ in their products’ characteristics, the scope of information available to build the recommender, and the ability to identify customers (are they identifiable or anonymous?). Even for a single e-commerce company, a different approach could be optimal for different product groups, depending on seasonality and offer dynamics. There is no one-size-fits-all solution that suits all e-commerce companies. So, if you have already reached the limit of the one-size-fits-all solution, it’s time to get a competitive advantage through Artificial Intelligence.
We offer deployment of the Artificial Intelligence state-of-the-art solutions to get the highest possible leverage on your data. We take your business to the next level by engaging Reinforcement Learning, Natural Language Processing, and Visual Recognition.
We look at your data, the characteristics of your products, seasonality, the offer dynamics of each product, and the competitive environment to find the best algorithm configuration to boost your product’s conversion and revenue.
Our recommendation engine enables the deployment of alternative tools and algorithms to challenge your specific requirements. The core of our solution consists of five proprietarily modules:
Our unique value proposition
We offer dynamic recommendations with the support of NLP and Visual Recognition. Our technology addresses the challenge of a dynamically changing environment (where the products are offered) and provide a solution that is constantly searching for the optimal strategy to recommend products. It accounts for changes in customers’ preferences, current market trends, and webpage content modification.
Modules:
Reinforcement Learning Module: Application of so-called Contextual Bandit Algorithms provides dynamic recommendations for an ever-changing environment (webpage content).
Natural Language Processing Module – support finding similar products by utilizing their descriptions. This is especially helpful when offering recommendations that suit blog and article content.
Visual Recognition Module – support finding similar products based on images as well as the product’s tagging.
Classic Recommendation Module – this module includes Collaborative Filtering, Content base recommendation, and Basket Analysis, which are all well-established and traditional methods for handling recommendations.
Connector Module – enables communication with your database and website.
