765 research outputs found
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Learning sophisticated feature interactions behind user behaviors is critical
in maximizing CTR for recommender systems. Despite great progress, existing
methods seem to have a strong bias towards low- or high-order interactions, or
require expertise feature engineering. In this paper, we show that it is
possible to derive an end-to-end learning model that emphasizes both low- and
high-order feature interactions. The proposed model, DeepFM, combines the power
of factorization machines for recommendation and deep learning for feature
learning in a new neural network architecture. Compared to the latest Wide \&
Deep model from Google, DeepFM has a shared input to its "wide" and "deep"
parts, with no need of feature engineering besides raw features. Comprehensive
experiments are conducted to demonstrate the effectiveness and efficiency of
DeepFM over the existing models for CTR prediction, on both benchmark data and
commercial data
Learning to Prove Trigonometric Identities
Automatic theorem proving with deep learning methods has attracted attentions
recently. In this paper, we construct an automatic proof system for
trigonometric identities. We define the normalized form of trigonometric
identities, design a set of rules for the proof and put forward a method which
can generate theoretically infinite trigonometric identities. Our goal is not
only to complete the proof, but to complete the proof in as few steps as
possible. For this reason, we design a model to learn proof data generated by
random BFS (rBFS), and it is proved theoretically and experimentally that the
model can outperform rBFS after a simple imitation learning. After further
improvement through reinforcement learning, we get AutoTrig, which can give
proof steps for identities in almost as short steps as BFS (theoretically
shortest method), with a time cost of only one-thousandth. In addition,
AutoTrig also beats Sympy, Matlab and human in the synthetic dataset, and
performs well in many generalization tasks
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