Cognitive Computing In E-Commerce
Cognitive Computing deals with simulation of human thought processing and analyzing it. Through analysis, Algorithms are generated by system in order to gain self learning capabilities. We can use cognitive computing in various platforms like Artificial Intelligence, cyber security, E-commerce, Image processing. In this paper usage of cognitive computing in E-Commerce is discussed.
Introduction To Cognitive Computing
Cognitive Computing deals with simulation of human thought processing and analysing it. Through analysis, Algorithms are generated by system in order to gain self learning capabilities. It is totally likely to give a lifeless substance a mind that observes the things and learns from the environment as a human does. Cognitive computing systems use Deep learning algorithms and neural networks to process information by comparing it to a set of data. As the system gains more data set for learning, the system learns more. And the more it learns, the more it becomes accurate over time. In cognitive computing, we can use Particle Swarm Optimization (PSO) algorithm and its variants for better results. Cognitive computing makes e-commerce very efficient. We can achieve better retailing items varying from customer to customer. Now, assume a human maintaining services for suggesting products in E-commerce website-for a group of users who shares more then 60% liking of same products. So he has to keep records of all customers’ likes and dislikes. For E-commerce websites the number of users are too many and options for their likes and dislikes are also not deterministic. So if we have a system that works as that e-commerce website employee, we can release the burden upon the employees. Now if we make a system that solves above thing, may there a problem arise. As a human we all are dynamic, our likes, dislikes changes like our goals. So how can a system know this thing even if we can’t. For this problem, system should be learning by time as human does. For these Lock like problems cognitive computing is a master key. There are many other problems in E-commerce websites which are conveyed below.
Cognitive Computing In E-commerce
To make any cognitive computing system, we need the help of big data, machine learning, and cloud computing. These are the three main technologies behind any level of cognitive computing. Here how each of these help in implementation:
- Big Data Analytics: The human brain can process a huge amount of data without even realizing the pressure. From example, understanding contextual meaning in a statement or understanding someone’s preference in movies. In case of machines, it is important to feed a huge amount of data to make this possible. This data can be both organized and unorganized thus, we need sophisticated tools to analyse this big data. The continuous increase of information and the constantimprovement in computing power of machine are irreversible obvious in the era of big data. Comparedwith the increase in traditional structured data, the increasein unstructured data such as data in social media and inmobile internet is ever-growing exponentially.
One differences between big data analysis and cognitive computing is data size. The big data analysis in allusion to some data set is not necessarily cognitive computing. The thinking of big data emphasizes on mining the value and obtaining the insight from large volume of data. Without large volume of data as base, the accuracy and the reliability ofprediction cannot be guaranteed. Considering the accumulation of data in volume in cognitive computing does not mean relying on data size. Based on cognition and judgement like human brain, the cognitive computing tries to solve the problems of fuzziness and uncertainty in biological system. Thus realizes various degrees of processes such as cognition, memory, learning, thinking and problem solving. For example, in real life, a child only needs a few times to learn to know a person. Although, the data size is not large enough but the cognitive computing can still be employed to process the data. As for common people and domain experts, it is assumed that the data are identical but the profundity of knowledge obtained by common people may differ from that obtained by domain experts. Since the height of thinking is different, the angle to interpret the data may also be different. The machine can mine more hidden meaning from limited data using cognitive computing.
- Machine Learning: Machine learning is the use of algorithms to enable computers to analyse data and make predictions based on the information fed to them. Usually, in machine learning, a training data is fed into the program and then it is tested on another data set to examine its efficiency. In case of cognitive computing, the algorithm needs to be coded to learn on its own as and when more data is supplemented to it.
- Cloud Computing: To analyse such huge amount of data in real time it is required to have extensive computing power. The pressure on the systems in cognitive computing varies on the basis of data fed into the system. Due to sudden spurts in demand, it is viable to opt for these cloud solutions. They provide scalable computing for analysing data and working on resource-intensive tasks, making them ideal for working on cognitive computing models.
A problem for suggesting products to the users according their likes and dislikes was mentioned in introduction. Now further more usage of cognitive computing in E-commerce website is shown below.
- Help E-commerce sellers to analyse customer data
Through cognitive computing, sellers can find hidden patterns in the customer’s data, they can find customers’ likes and dislikes, their preferences. From the whole customer data set, they can find anything needful for them, and become capable for serving, communicating conveniently. To analyse data and extract information out of that- is called as data mining. Thus, Cognitive computing is also associated with data mining. Today various data mining techniques are available.
- Customer control over their purchase
Consumers usually have many options regarding what they want to purchase. Also, customer can purchase things anytime anywhere. Cognitive technologies offer them several self-help applications as they shop online. They can make use of self-checkout payment platforms, mobile payment apps and reviews to improve their online shopping experiences.
- Efficient Process
Cognitive computing technologies enable retail store managers to carry out complex business processes efficiently. They also help them in identifying and reacting to issues and opportunities that require effective and fast responses. When retailers implement cognitive computing in their e-commerce models, it can help to uplift their online enterprises.
- Improved customer interactions
There are cognitive computing applications suited for improving customer interactions. Their design allows them to provide online retailers with accurate, contextual and relevant data regarding broad aspects of their businesses. These applications are also instrumental in designing user interfaces for e-commerce sites that make it easier, smarter and enjoyable for consumers to shop online.
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Cognitive computing is used in many fields as discussed in the introduction, ex: Artificial Intelligence, cyber security, E-commerce, Image processing. Here Technology dominants are listed below.
IBM Watson Commerce
IBM Watson Commerce is a software program designed to empower sellers to manage their business processes. It also targets marketers and category managers who want to improve their customer engagement initiatives. The program uses Insights Assistant, an application with cognitive capabilities that helps merchandisers to identify odd conditions in their market places and recommends appropriate actions. Below are the key innovations made through Watson Analytics, that enable business users to efficiently engage in advanced analytic:
- Natural Language Dialogue
- Single Business Analytics Experience
Watson Analytics bring out the distinctive cognitive capabilities to accelerate the everyday analytical activities in three ways:
- Semantic recognition of concepts in the data
- Recommends starting points for analysis
Chatbots are program developed to make a system communicate with a user in a contextual sense. For chatbots natural language processing is used. NLP allows system to take inputs from users, analyse it and answer logically. Generally, in chatbots, system takes care of past chats with user and by analysing them, to suggests/chats to users. Google Assistant is example of chatbots. Alexa from Amazon is also example of Trending E-commerce company.
- Sentiment analysis
The procedure of computationally recognizing and classifying assessments communicated in a bit of content, particularly so as to decide if the author’s state of mind towards a specific theme, item, and so on is certain, negative, or unbiased is called sentiment analysis. Now users can buy products according to reviews. Consumer buys something and rates it, others see and decide to buy that product or not. If the reviews can be analyzed, retailers can actually see people’s perspectives and bring changes in their products. But seeing each review and manage all things is a hard thing, so if we make our system analyze the reviews-comments, then lot of time will be saved. Sentiment analysis is part of machine learning. It can be done on a particular data set user want. Sentiment analysis can is generally done by neural network training. Many optimization techniques also are capable of doing it.
- Security and Fraud Detection
Through cognitive computing, we can make our system smart enough to decide that buying process is secure or not. System can identify unauthorized user entry and stop purchasing from that device. We can use Face detection to do so, there are many biometrics available for security purposes. Assume a user is regularly buying in budget in time to time, but suddenly one day a huge purchase is made from that user to irregular address, then cognitive system can find anomaly and verify authorization.
- Microsoft Cognitive Services
Microsoft cognitive services previously, Project Oxford is a set of APIs, SDKs, and machine learning frameworks which the developers can use to make their applications more agile and intelligent. It can help developers easily add intelligent features – such as sentiment analysis, vision and speech recognition, contextual analysis – into their applications. Basically, it can help developers create smart applications that were previously beyond their reach.
- Google DeepMind
Google has always been an evangelist in disruptive technologies. Google acquired Deep Mind in 2014 which is now considered to be a leader in AI research. DeepMind became mainstream with AlphaGo, an AI platform to play Go, a Chinese strategy game. Google has made tremendous efforts to popularize artificial intelligence. Overall it is a rapidly evolving platform with huge potential.