Managing diversity of products in recommendation models based on an architecture with an attention mechanism (transformers)

Fedor Krasnov

Abstract


Millions of products are available for online buyers to choose from on Internet marketplaces. Despite all the advantages of diversity, a huge number of possible purchase options can stop and make it difficult to choose, as a result of which buyers leave without shopping at all. Such a situation does not benefit either buyers seeking to make a purchase, or sellers who have missed their benefits, or the online trading platform itself, which loses commission and attractiveness for advertisers. One of the main values of an Internet marketplace is to direct customers to the products that best match their interests and motivation. To do this, Internet marketplaces rely on recommendation systems. Using Data Science to empower sellers helps predict which products the buyer will add to the cart and place an order, and also allows you to improve the quality of customer service the next time you make purchases online. Modern recommendation systems consist of various models with different approaches, ranging from simple matrix factorization to deep artificial neural networks with an attention mechanism (transformer). However, there is no single model that could simultaneously optimize all the tasks of users, sellers and Internet marketplaces. In this study, the author focused on the task of a balanced selection of product recommendations, taking into account the interests of the user, the seller and the Internet marketplace. Improving recommendations will make navigating through seemingly endless search options easier and more attractive for buyers. To do this, the author examined the metrics of the variety of the product range such as Serendipity and showed the possibilities and limitations in optimizing diversity for models of recommendation systems based on an architecture with an attention mechanism (transformers).

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References


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