Insights for Prediction and Recommendation Machine Learning Algorithms for E-Commerce, and Gaming
By Industry News

Popular across multiple industries, the ability to use Machine Learning Algorithms to predict future purchasing behavior has suggested exponential benefit to companies and consumers. In this article I am looking to break down some examples of prediction, regression, and recommendation and the benefit comparison to both the client and consumer.

 

E-Commerce:

Image Description: An interesting application for computer vision through Machine Learning/NLP is for image recognition generating product descriptions automatically. Research suggests that image description currently works from a set number of pre-set descriptions being applied to relevant products during the image recognition process through deep learning (Mandal & Dwivedi, 2020). As a thought to progression, automatic text generation in the field of NLP from conversational AI, could mark a breakthrough development in unique product description generation as opposed to pre-set product description.

Prediction and Recommendation: Machine Learning algorithms are currently being implemented to predict customer behavior and products bought (Hambarde, et al., 2020). This aims to increase product manufacturing efficiency, life cycle efficiency, and customer satisfaction. Initial thoughts highlight clear benefits to the manufacturers for the utilization of customer purchase data, however, my opinion is that added benefit to the consumer in the search optimization and recommendation accuracy will make E-Commerce a user-friendly navigation landscape, improving consumer experience.

Gaming:

With releases of next generation consoles on the horizon, it is a prime time within the video games industry for Machine Learning to be implemented into production models. Video game development companies are using Machine Learning algorithms to create more dynamic Nonplayer Characters resulting a more challenging environment, and non-scripted foes. While benefit to consumer is clear, this also will decrease development time for the manufacturer, due to the avoidance of hard coding on Nonplayer Character scripts. Other benefits of Machine Learning algorithms include realism in game interactions, world creation / landscape generations (Kaliappan & Sundararajan, 2020).

Closing Thoughts Regarding the Job Market:

There is clearly a presence for Machine Learning prediction and recommendation algorithms to be implemented across multiple industries, expanding much further than the topics I have touched upon in this article. Having had the opportunity to work with E-Commerce and Gaming companies, discussing their requirements for Machine Learning Engineers and Data Scientists, it is an exciting time in the industry to follow the innovative developments and watch the implementation affect our day to day lives.

References

Hambarde, K. et al., 2020. Data Analytics Implemented over E-commerce Data to Evaluate Performance of Supervised Learning Approaches in Relation to Customer Behavior. In: Soft Computing for Problem Solving. Singapore: Springer, pp. 285-293.

Kaliappan, J. & Sundararajan, K., 2020. Machine Learning in Video Games. In: Handbook of Research on Emerging Trends and Applications of Machine Learning. s.l.:IGI Global, p. 19.

Mandal, I. & Dwivedi, A., 2020. Deep Learning Algorithms for Accurate Prediction of Image Description for E-commerce Industry. Data Management, Analytics and Innovation, pp. 401-418.

Author: Dean Bonning