AI Shopping Assistant explained at The National Convention of "Society of Global Business"

, Masayuki Chatani, AI

    Since Rakuten AI platform has been launched in April 2017, we have launched 40+ AI Chatbot for external customers (consumers and business partners of Rakuten) as well as internal employees. But those are based on FAQ or frequently asked questions and provide answers to relatively simple questions.

Rakuten's Intellectual Property activities for future innovations

, Masayuki Chatani, AI

    Recently I have received five Certificates of Patent from Japan Patent Office as shown in the picture below.

There Is No Artificial Intelligence

, Laurent Ach, AI

All the recent amazing achievements of artificial intelligence are categorized as narrow intelligence, but there is a lot of speculation about general and super intelligence. A recent, famous and impressive example of narrow intelligence is AlphaGo (and later AlphaGo Zero) by DeepMind, which can beat the top human players of the game of Go, considered for decades as very difficult for computers to play.

Eight ways AI applications are already making conventional EC smarter – from chatbots to creative AI

, Masaya Mori, AI

    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 a large part of the status quo. One such example is e-commerce (EC).

Rakuten’s First Personalized Bot & Beyond

, Masayuki Chatani, AI

    I am happy to share the news that Rakuten had introduced our first personalized chatbot based on Rakuten AI Platform. This AI Platform was announced in April, 2017 and is DevOp-ed by AI Promotion Department at Rakuten.

Collaborative Ecosystems with Data and AI at Rakuten Institute of Technology

, Masaya Mori, AI

New Approach to Innovation – A Collaborative Ecosystem

    Data is one of the most important resources for the economy in the present age. And what’s more, AI is now bringing new value to society at a significant pace.

AI-nization at Rakuten

, Masayuki Chatani, AI

Introduction of Rakuten AI Platform

    At Rakuten, AI Promotion Department was established in August, 2016 and accelerates the commercial deployment of AI functionalities to various Rakuten services.

Machine learning applications in the e-commerce domain (4)


(Continued from the previous chapter)

Deep learning

  In relation to deep learning, the two-layer network Word2Vec developed by Google was revolutionary. If you input text, you can obtain a vector set as the output, i.e., the feature vector of words in the text. By grouping the words and creating vectors, you can make judgments on similarity, and infer the meaning. Since it uses figures, it is scalable by processing in parallel and this is one of the great advantages of Word2Vec. It is not only applicable to the field of text analysis, but to any kind of data. At Rakuten, we have developed an extended version called Category2Vec and have released it as OSS (ref: We are training it with various kinds of EC data and are beginning to see the possibility of application on a broad scale for product/user analysis and categorization, and estimation of loss data. While Word2Vec is not deep learning itself, it is worth understanding this as a technology in numerical form that can be used in a deep neural network.

Machine learning applications in the e-commerce domain (3)


(Continued from the previous chapter)

Online learning

  When studying machine learning, you will sometimes come across the term “online learning”. For example, in supervised learning such as SVM, first you train with all of the sample data given. Budepending on the volume of sample data or the application, it may not be appropriate to learn all the data at once. As mentioned above, all businesses have time restrictions. There are also system limitations on the volume of data that can be handled, such as the computer processing capacity and memory capacity. There are also cases where the sample data is provided in stages. In these cases, it can be convenient to optimize the parameters for each data set provided, and then revise it and retrain. This kind of method is called online learning. You could also call it “consecutive leaning”. Typical online learning methods are perceptron, CW, AROW and SCW. There is also a method called Streaming Random Forests, which is Random Forest by online learning.

Omotenashi & AI

, Chatani Masayuki, AI

Omotenashi (Hospitality) & AI

This blog is based on my speech at IBM Watson Summit on April 28th, 2017 following to joint press release of Rakuten AI Platform by Rakuten and IBM. (

The Rakuten AI Platform has intended to facilitate various AI applications such as automated chat bot for customer services to be utilized in wide spectrum of Rakuten services including E-commerce, FinTech, Digital content and etc.