Learning to rank hotels for search and recommendation from session-based interaction logs and meta data
Malte Ludewig, Dietmar Jannach · 2019
Being able to provide high quality search and recommendation services can be a decisive success factor for online applications, e.g., in today's competitive e-commerce environments. Context-adaptive and personalized item suggestions can help to both improve the user experience and the provider's short-term and long-term revenue. However, automating this form of adaptation can be challenging, when no long-term preference profiles are available. In these situations, the user's preferences and short-term intent must be derived from the last few observed interactions.