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: https://github.com/rakuten-nlp/category2vec). 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.
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 (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. ( https://global.rakuten.com/corp/news/press/2017/0426_01.html)
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.
On the other hand, unsupervised learning is a method in which actual data is analyzed without receiving any prior sample data so that the intrinsic structure and characteristics existing in the data are extracted. For example, in recommendation, which is an important function in EC, we often use data clustering, which is an unsupervised learning method that categorizes the customers and products to recommend. As well as supervised learning, unsupervised learning is also used for security purposes. When detecting log-in attacks, we deploy clustering to determine what kind of attack patterns there are.
As an introduction to AI, let’s look at the different applications of machine learning in e-commerce
As a first step to explaining AI, which will without a doubt hugely transform our society over the next few years, let’s walk through an overview of machine learning. The way that machine learning (ML) is used in practice in Rakuten’s e-commerce (EC) platform Rakuten Ichiba, for example, is not very well known, so here I will cover the use of ML methodically section by section.
[Beginning of Artificial Intelligence]
The term “Artificial Intelligence” is considered to be used by John McCarthy, computer scientist for the first time at Dartmouth conference in 1956. Since then it has been passed 60 years. For ordinary people the definitions and understandings of Artificial Intelligence vary, so it is often a topic of discussions what should we call as the Artificial Intelligence. However, I would like to avoid sticking to its definition but I would like to broadly capture it as a computing processing that can do the things only human can do in the past, or a computing processing that can do better than human.
[Artificial Intelligence also challenged to Quiz Champion]
Artificial Intelligence had challenged Quiz Champion. AI fought against on the famous TV quiz program “Jeopardy” in United States. The AI fight was “Watson” created by IBM. This quiz show has a style to answer as quickly as possible and type of question is known like “The capital of the country is Tokyo”. Every round there are about six topics and the difficulty of the quizzes increase as the reward amount becomes higher.
[Artificial Intelligence as a good partner]
With very rapid penetration of internet, computers around the world are being connected. When I first used internet in 1980s, there was no search engine existed. Since then, search engine was invented and we can instantly display the information in the computer around the globe.