#factor-models

4 notes

• DeepFM DeepFM (Guo et al., 2017) is a CTR prediction model that combines an FM component and a Deep component in parallel, jointly learning low-order (explicit) and high-order (implicit) feature interactions.
• Factorization Machine The Factorization Machine (FM) is a general-purpose prediction model proposed by Rendle (2010) that models interactions between all pairs of features as inner products of latent factor vectors.
• PNN PNN (Qu et al., 2016) is a CTR prediction model that introduces a product layer between the embedding layer and the DNN hidden layers, explicitly capturing the interactions among feature embeddings before passing them to the DNN.
• Wide and Deep Wide & Deep (Cheng et al., 2016) is a CTR prediction model that combines a linear wide component (memorization) with a DNN deep component (generalization). It was first deployed for Google Play app recommendation.