Masters Thesis Topics – Artificial Intelligence

Following AI/ML problems are available as Masters Thesis topics.

Ranking Top Sellers

Based on the sum of order-data available in a database, we would like to derive the top-sellers on specific webshops. Such a result can, of course, be derived using SQL database queries. But we intend to solve the same problem using Machine Learning techniques.

Cleaning Product Images

Clean and beautiful product images are key to helping buyers make a purchase decision in E-Commerce. AI, particularly Computer Vision (CV) techniques can be used to clean backgrounds of product images. Cost of providing quality images on web pages can be reduced significantly as well.

User Profiling

Based on user-behavior while on a specific web-shop, we’d like to profile the user, without the user having to log in. For example, we’d like to determine if a user is male or female without he/she telling us explicitly. Similarly, we’d like to guess the approximate spending capacity of the user and promote products in that price-segment.

  • Gender
  • Spending-power, or a price-range in which the user is most likely to make a purchase decision.
  • Product-category
  • Trend List

Based on click-pattern of a user, we’d like to guess which type of products the user is interested in, and load those with a higher priority into the shop-interfaces. In other words, utilize ML to provide a highly relevant user-experience.

Demand Forecasting

Based on daily/weekly/monthly/annual sales data, predict demand during the next time-periods, to help the online-seller with purchase decisions. While analyzing sales data, consider:

  • Seasonality: Correlation between date of the year the sales happened and quantity
  • Volumes of product sold (without correlations)

Search Results

Search features, especially “enriched” results like those from Apache/SOLR can be enriched even more by learning from past click-patterns (matched with search keywords).

Image Search based on product pictures uploaded by users into webshop is a further problem that will enable “comparison shopping” situations.

Dynamic Pricing

As we perfect our odoo based Omnichannel E-Commerce solution at Simplify-ERP®, we look for more and more ways AI can help our retailer (B2C) and wholesale (B2B) customers derive ever more economic value from their data.

Liquidating inventory is a well-known trader’s strategy to maintain high liquidity, to invest cash in new products, and hence offer a better selection to buyers. ML can help sellers determine price-propositions for products, correlated to costs and gross margins, and help him “push” the product on specific online-sales-channels. Very useful for this learning would, of course, be competitive price-data on specific channels. So a sub-problem to the Dynamic Pricing problem would be to learn the market-price (on specific channel/market, ex. Amazon) for a specific product.

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