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Recommendation algorithms are widely adopted in marketplaces to help usersfind the items they are looking for. The sparsity of the items by user matrixand the cold-start issue in marketplaces pose challenges for the off-the-shelfmatrix factorization based recommender systems. To understand user intent andtailor recommendations to their needs, we use deep learning to explore variousheterogeneous data available in marketplaces. This paper summarizes fivelessons we learned from experimenting with state-of-the-art deep learningrecommenders at the leading Norwegian marketplace FINN.no. We design a hybridrecommender system that takes the user-generated contents of a marketplace(including text, images and meta attributes) and combines them with userbehavior data such as page views and messages to provide recommendations formarketplace items. Among various tactics we experimented with, the followingfive show the best impact: staged training instead of end-to-end training,leveraging rich user behaviors beyond page views, using user behaviors as noisylabels to train embeddings, using transfer learning to solve the unbalanceddata problem, and using attention mechanisms in the hybrid model. This systemis currently running with around 20% click-through-rate in production atFINN.no and serves over one million visitors everyday.

Five lessons from building a deep neural network recommender