There’s no doubt that artificial intelligence (AI) will fundamentally change the world over the next few decades. What many do not realize however, is that in some fields, it has already become part of the status quo. One such example is e-commerce (EC).
At Rakuten, we have been working with big data for about five years – purposefully searching for new and better ways to empower Rakuten Ichiba marketplace merchants and customers through the boundless abilities of AI.
Below are a few examples of how we are leveraging various applications of AI, specifically machine learning, to advance our EC business.
- Predicting sales – At Rakuten Institute of Technology we leverage “supervised” machine learning to predict product sales. Supervised machine learning is a form of AI in which “the machine,” or algorithm, is given sample data from the past that helps train it to process the data of the future. With 200 million products being traded on Rakuten Ichiba at any given time, supervised machine learning algorithms allow us to use historical sales data to forecast the sales volume of products to a high degree of accuracy – and make surprising discoveries in a far more efficient way than a team of humans ever could.
- Marketing to the right groups – We make use of so-called “unsupervised” learning algorithms when segmenting customer groups for marketing campaigns. Traditionally, marketers have defined market segments in ways that appeared to make sense to them – by age or gender, for example. But AI is demonstrating that those are not always the most effective approaches. An unsupervised learning algorithm, working from raw real-time data only, might identify alternative means of segmentation, such as online behavior or preferences, that can serve as a more accurate predictor of interests or tastes.
- Classifying products – As mentioned above, there are more than 200 million products being traded on Rakuten Ichiba, covering many different genres. This can make categorizing a challenge. To solve the problem, we utilize a “semi-supervised” learning algorithm, which repeatedly resamples data until the algorithm learns how to process it in the most efficient way. This helps us to classify products on Ichiba accurately so customers can always find what they need.
- Analyzing ratings and reviews – Understanding user ratings and reviews is important, but it is also time-consuming. Applying “structural” machine learning algorithms, a method commonly used in the study of the structure and formation of words (morphology), we can efficiently collect and analyze product review text, both positive or negative. In addition, structural machine learning can help us mine valuable information data from page explanations and reviews.
- Improving recommendations and search – Through our use of “reinforcement” learning algorithms, we are able to process data on customer reactions in response to products they are shown – for example, whether users clicked on a product when it was served to them in search results or in a recommendation. Similar to an A/B test, reinforcement learning algorithms notice how much “reward” (positive reaction from users) is obtained when different products are displayed in response to certain circumstances (a particular search query, or a user’s browsing history, for example). Combining knowledge of past customer reactions in response to particular circumstances, the algorithm can determine the most efficient course of action when those circumstances reoccur. And with each action and reaction, the algorithm becomes smarter.
- Image recognition – On a customer-to-customer platform like PriceMinister-Rakuten, deep learning algorithms can be effectively used for the purpose of image recognition. Inspired by the structure and function of the brain, deep-learning algorithms develop the ability to recognize an object in a photo and then automatically categorize it, making it easier for users to post products for sale.
E-commerce may not be the first thing that comes to mind when people hear the words “artificial intelligence,” but there is no denying the impact it is already having on the way we buy and sell online. Through the “superhuman” abilities of machine learning, we hope to continue empowering both shoppers and merchants in our Rakuten Ichiba marketplace.
Masaya Mori joined Rakuten, Inc. in 2006. He currently serves as both an Executive Officer at Rakuten, Inc. and representative of the Rakuten Institute of Technology.