Mercari AI’s research “Layout Optimizer for Personalized Home Screen based on Contextual Multi-Armed Bandit in C2C Marketplace” accepted to RecSys 2023’s Workshop
We are pleased to announce that the paper "Layout Optimizer for Personalized Home Screen based on Contextual Multi-Armed Bandit in C2C Marketplace" has been accepted to the CONSEQUENCES '23 workshop within the international conference "RecSys 2023(ACM Conference on Recommender Systems)".
RecSys is one of the most prestigious international conferences in the field of recommendation systems, held annually by researchers from around the world. This year's RecSys, in its 17th year, was held in Singapore from September 18 to 22, 2023.
Key points of presentation
In this presentation, based on how customers use Mercari, we propose a method to display more attractive content on the home screen with priority among other content, including product recommendations. We reported the results of our experiments.
In the proposed method, an exploratory screen display and machine learning are performed to collect data, without giving prior knowledge of what kind of content is attractive to which users. As a result, the screen display is automatically optimized to improve overall satisfaction.
At Mercari app, several teams, including the Recommendation Team, are developing content on the home screen.There are various types of content on the home screen, including item recommendations and campaign announcements. Furthermore, even if the same item is being recommended, there are multiple methods of recommendation and display.
Not only do the items to be recommended vary from user to user, but also the type of recommendation method and how effective the campaign announcement is depends on each user.
Summary of paper
In e-commerce services, including Mercari, the home screen is an important function that attracts many views. However, the amount of content that can be displayed is limited, and it is common practice to place attractive content at the top of the home screen as a priority.
However, since different users have different perspectives on what kind of content is attractive, it is necessary to create a personalized screen structure.
In this study, we proposed a method that applies the contextual multi-armed bandit problem algorithm and reported the details and results of our experiments.
Comments from author
ML Engineer / Backend Engineer | Yusuke Shido
Since this year's event was held offline, and in the form of a poster presentation, we were able to take the time to have direct discussions with researchers and engineers from various countries. In particular, I received a lot of interest and questions from developers and researchers belonging to companies, and I felt that the content of our paper was an issue common to many different companies. The five days at RecSys, including the workshop, were an opportunity for very meaningful input. We hope to apply the knowledge gained to Mercari's business in the future and continue to disseminate the results.
Members making a poster presentation in Singapore
About recommendation team
Mercari's recommendation team is working to improve the quality of product recommendations on each screen of the Mercari App, as well as the mechanisms involved in screen configurations such as the one in this study.