Deep neural network marketplace recommenders in online experiments
This work addresses performance measurement for recommender systems in a specific marketplace context, with incremental improvements to existing methods.
The paper tackled the challenge of measuring recommender performance in marketplaces and reported online experiment results with deep neural network recommenders, including hybrid, sequence-based, and multi-armed bandit models, which are deployed at FINN.no serving over one million daily visitors.
Recommendations are broadly used in marketplaces to match users with items relevant to their interests and needs. To understand user intent and tailor recommendations to their needs, we use deep learning to explore various heterogeneous data available in marketplaces. This paper focuses on the challenge of measuring recommender performance and summarizes the online experiment results with several promising types of deep neural network recommenders - hybrid item representation models combining features from user engagement and content, sequence-based models, and multi-armed bandit models that optimize user engagement by re-ranking proposals from multiple submodels. The recommenders are currently running in production at the leading Norwegian marketplace FINN.no and serves over one million visitors everyday.