In this repository, I store all the solutions that Grand View University students build data mining challenges. The DATA MINING CUP (DMC for short) has inspired students around the world to pursue intelligent data analysis since the year 2000. For more info click here.
Before the pandemic, Johannes Gutenberg managed a flourishing little book shop in the historic city center of Mainz. He took great joy in building personal relationships with each of his clients, recommending books catered to their personal taste and assisting in widening their literary palette. In his city and beyond he developed a formidable reputation with a respectable base of loyal customers, who considered him more of a connoisseur than a traditional salesman.
Unfortunately, this loyal base of customers is not enough to make his business profitable. And so, like many traditional retailers, Johannes also relies on walk-in customers. At the beginning of the pandemic, this source of revenue vanished. To keep his employees and cover ongoing costs, Johannes had to find an alternative form of revenue. With great initial reservation, he decided to expand his business by launching an online shop, which he believed would save his beloved business from imminent bankruptcy. At first, Johannes and his employees tried their best to provide suitable recommendations for every product manually. But as the number of products increased and associates worked to keep at least some personal contact to clients via phone and email, this manual process was just not feasible.
Today, Johannes is looking for a reliable recommendation system to provide a targeted recommendation to every product page. This solution should meet his high personalization standards and only require a small amount of manual support to implement.
This year's scenario is all about Pia and Philip, a married couple. They started their new e-commerce business during the pandemic in 2020 by offering convenience goods online. They began by selling an assortment of masks and disinfectants, but quickly expanded to a wider range of various everyday commodities.
Having both a background in traditional and online retail, they are aware of how distant and impersonal online shopping can feel and, at the same time, how important customer guidance and recommendations are for long-term customer loyalty.
To differentiate themselves from the many other commodity shops, they decided to put an even more significant emphasis on personalized recommendations and offers.
One key element of this strategy is a customized weekly newsletter that personally addresses each of their clients. The newsletter includes user favorites, products similar customers liked, new additions, and special offers.
However, they quickly noticed a problem: repeated recommendations of recently purchased products. One quick workaround for this issue was implementing a filter that would exclude products from the recommendation for a fixed number of days. This, however, did not meet the high standards of Pia and Philip.
They are instead looking for a model that can reliably predict the week that a returning customer might repurchase one of their frequently purchased items.
By knowing the estimated week of replenishment, products can be added to the newsletter as a reminder, thus increasing basket sizes and profits.
Since the owners are only interested in the best possible solution, they organized a contest to benchmark competing prediction approaches.