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  • 1.
    Andersson, Sophia
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Artificiell Intelligens inom E-handel: En studie om maskininlärning vid produktsökningar2019Independent thesis Basic level (degree of Bachelor), 20 poäng / 30 hpOppgave
    Abstract [en]

    Background: One of the most growing areas of the Internet is E-commerce, which gave consumers a flexibility that was not previously available. The advantages of digital commerce are many, where both companies and consumers can use the technology's possibilities. However, a problem area in the E-commerce area is the number of products and the consumers' difficulties finding what is demanded. One of the techniques used to solve the problem is Artificial Intelligence.

    Within the artificial intelligence there is the sub-area Machine learning. The basis of the technology is to teach a computer to perform specific tasks better and better over time. To implement machine learning, artificial neural network used, which this study is based on.

    Problem statements: In this research, machine learning will be tested to see if the technology can be used to increase the conversion rate in E-commerce. The study will also be supplemented with knowledge about the opportunities and challenges that a future implementation could bring. The questions to be answered are:

    - How can product searches based on machine learning be used to increase the conversion rate in E-commerce?

    - What are the most common opportunities and challenges associated with machine learning in E-commerce?

    Purpose: The study's purpose is to make it easier for consumers when it comes to finding the right products and to increase the knowledge base of machine learning in E- commerce.

    Results: The study concluded that search engines based on Machine Learning can be used to increase the conversion rate. The opportunities that were found were improved business strategies, more personalized approaches and increased revenue. The challenges that were discovered were resource shortages, data quality and lack of project management.

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