Developing an automated game to win BuyBox on Amazon platform : A research on developing a dynamic adversarial decision-maker - BC-402

Preferred Disciplines: Machine Learning and Artificial Intelligence Scientist/Engineer (Masters or PhD)
Project length: 4-6 months (1 unit)
Approx. start date: As soon as possible
Location: Burnaby, BC
No. of Positions: 1
Preferences: Hands on student with strong coding background in Python on both Windows and Linux OS and preferably have experience with production and industry-level coding work.
Company: Cymax Incorporation

About Company:

Cymax is one of the fastest growing online furniture retailers with annual sales exceeding US $100 million. With over 75,000 SKUs, Cymax is a leader in online sales for all items home and office. Internet Retailer Magazine ranked Cymax within the Top 200 e-tailers in the world in both 2011 and 2012.

Recently, Cymax is incorporating data-driven solutions to increase sales and margine. Therefore, Cymax is incorporating cutting-edge machine learnig and AI algorithms into various projects ranging from pattern recognition in big-data to computer vision and natural language processing. As a result, Cymax provides “real-world” big-data and supports research and development in the field of Artificial Inteligence to advance the field with the focus on supporting scientists in the fields of supervised and reinforcement learning. 

Summary of Project:

More than 50% of Cymax sales is on Amazon marketplace. Since the key feature of Amazon is that multiple sellers can offer the same product, Cymax has many competitors to sell an item on this platform. In addition, the performance of the sales is highly dependent on how often Cymax wins the Amazon BuyBox (the box on a product detail page where the customers can begin the purchasing process ). Winning the BuyBox is about understanding all the players’ strategies based on both the current state and the history of their multi-variable data. We believe that this process can be defined as a game between Cymax and other businesses. Therefore, an adversarial network can help learn the strategies of the other players and help Cymax increase the frequency of BuyBox winning by applying the best strategy real time (an example of a reinforcement learning problem). 

Research Objectives/Sub-Objectives:

  • Critical thinking about the problem and being able to bring mathematical/computational solutions for better performance on this problem.
  • Coming up with a feasible solution for this project based on the knowledge in supervised learning and reinforcemnet learning. 

Methodology:

    • Understanding and designing an inteligent network based on understanding the patterns in the historical data using RL approach.  

    Expertise and Skills Needed:

    • Strong coding background in Python.
    • Experienced with deep-learning libraries such as TensorFlow, Keras, and PyTorch and also RL platforms such as DQN and Google’s Dopamine.
    • Interested in applying mathematical models to solve industrial DL problems.
    • Familiar with fundamentals of supervised-learning, unsupervised-learning, and reinforcement-learning  algorithms.
    • Familiar with how deep learning algorithms work. Familiar with the fandamentals of for example CNN and RNN algorithms and how they recognise patterns in the data.
    • Familiar with Generative and Adverserial algorithms and knowing how to implement and apply them to approach a problem.   
    • Critical thinking and having a can-do attitude.    

    For more info or to apply to this applied research position, please

    1. Check your eligibility and find more information about open projects.
    2. Interested students need to get the approval from their supervisor and send their CV along with a link to their supervisor’s university webpage by applying through the webform
    Program: