  Online reviews play an important role in influencing buyers' daily purchase
decisions. However, fake and meaningless reviews, which cannot reflect users'
genuine purchase experience and opinions, widely exist on the Web and pose
great challenges for users to make right choices. Therefore,it is desirable to
build a fair model that evaluates the quality of products by distinguishing
spamming reviews. We present an end-to-end trainable unified model to leverage
the appealing properties from Autoencoder and random forest. A stochastic
decision tree model is implemented to guide the global parameter learning
process. Extensive experiments were conducted on a large Amazon review dataset.
The proposed model consistently outperforms a series of compared methods.
