Sequential recommendation problems have received increasing attention in research during the past few years, leading to the inception of a large variety of algorithmic approaches. In this work, we explore how large language models (LLMs) can be used to build or improve sequential recommendation approaches.
Consumer Data Products (Codapro) at Delivery Hero is a Tech org of about 120 engineers and scientists. Our mission is to optimize the entire customer journey. One of our important products for achieving this mission is the Customer Data Platform (CDP).
Delivery Hero is a listed company with over 45,000 employees in more than 70+ countries, with several thousand employees in IT, and hundreds in data.
The data teams at Delivery Hero are scattered across different regions, countries and companies. At the beginning of 2020, teams used different technologies and several cloud providers and did not share a common infrastructure. The high-quality standards were missing, and as a result, the data quality was often poor. The question of who owns specific data was not easy to answer. The communication between the teams, as well as the exchange of data, was hampered. The proper access management process was neglected, security was solved differently from team to team, data modeling was not standardized and varied from team to team.
Delivery Hero is one of the leading global online food ordering and delivery marketplaces. We process millions of orders per day, partnering with over 600,000 restaurants and a fleet of fantastic riders. We deliver an amazing search experience to customers in more than 45 countries and 25 languages. We power 50M+ searches per day across 4 continents.
Query expansion is an essential feature used in search engines. It improves the quality recall of search results. In this article, I will attempt to define what query expansion is, and why is it important.
If you are interested in doing this type of work on a daily basis, have a look at some of our latest Consumer Discovery openings.