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27 Incredible Examples Of AI And Machine Learning In Practice

#artificialintelligence

There are so many amazing ways artificial intelligence and machine learning are used behind the scenes to impact our everyday lives and inform business decisions and optimize operations for some of the world's leading companies. Here are 27 amazing practical examples of AI and machine learning. Using natural language processing, machine learning and advanced analytics, Hello Barbie listens and responds to a child. A microphone on Barbie's necklace records what is said and transmits it to the servers at ToyTalk. There, the recording is analyzed to determine the appropriate response from 8,000 lines of dialogue.


27 Incredible Examples Of AI And Machine Learning In Practice

#artificialintelligence

There are so many amazing ways artificial intelligence and machine learning are used behind the scenes to impact our everyday lives and inform business decisions and optimize operations for some of the world's leading companies. Here are 27 amazing practical examples of AI and machine learning. Using natural language processing, machine learning and advanced analytics, Hello Barbie listens and responds to a child. A microphone on Barbie's necklace records what is said and transmits it to the servers at ToyTalk. There, the recording is analyzed to determine the appropriate response from 8,000 lines of dialogue.


Deep Learning Sentiment Analysis of Amazon.com Reviews and Ratings

arXiv.org Machine Learning

Our study employs sentiment analysis to evaluate the compatibility of Amazon.com reviews with their corresponding ratings. Sentiment analysis is the task of identifying and classifying the sentiment expressed in a piece of text as being positive or negative. On e-commerce websites such as Amazon.com, consumers can submit their reviews along with a specific polarity rating. In some instances, there is a mismatch between the review and the rating. To identify the reviews with mismatched ratings we performed sentiment analysis using deep learning on Amazon.com product review data. Product reviews were converted to vectors using paragraph vector, which then was used to train a recurrent neural network with gated recurrent unit. Our model incorporated both semantic relationship of review text and product information. We also developed a web service application that predicts the rating score for a submitted review using the trained model and if there is a mismatch between predicted rating score and submitted rating score, it provides feedback to the reviewer.


Learning and Transferring IDs Representation in E-commerce

arXiv.org Machine Learning

Many machine intelligence techniques are developed in E-commerce and one of the most essential components is the representation of IDs, including user ID, item ID, product ID, store ID, brand ID, category ID etc. The classical encoding based methods (like one-hot encoding) are inefficient in that it suffers sparsity problems due to its high dimension, and it cannot reflect the relationships among IDs, either homogeneous or heterogeneous ones. In this paper, we propose an embedding based framework to learn and transfer the representation of IDs. As the the implicit feedbacks of users, a tremendous amount of item ID sequences can be easily collected from the interactive sessions. By jointly using these informative sequences and the structural connections among IDs, all types of IDs can be embedded into one low-dimensional semantic space. Subsequently, the learned representations are utilized and transferred in four scenarios: (i) measuring the similarity between items, (ii) transferring from seen items to unseen items, (iii) transferring across different domains, (iv) transferring across different tasks. We deploy and evaluate the proposed approach in Hema App and the results validate its effectiveness.


Product Characterisation towards Personalisation: Learning Attributes from Unstructured Data to Recommend Fashion Products

arXiv.org Machine Learning

In this paper, we describe a solution to tackle a common set of challenges in e-commerce, which arise from the fact that new products are continually being added to the catalogue. The challenges involve properly personalising the customer experience, forecasting demand and planning the product range. We argue that the foundational piece to solve all of these problems is having consistent and detailed information about each product, information that is rarely available or consistent given the multitude of suppliers and types of products. We describe in detail the architecture and methodology implemented at ASOS, one of the world's largest fashion e-commerce retailers, to tackle this problem. We then show how this quantitative understanding of the products can be leveraged to improve recommendations in a hybrid recommender system approach.