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• We take advantage of recent distributed word representation technology to help summarize text chunks. • The word embeddings in this research are utilized both in semantically and numerically. • We explore the relationships between natural language phrases in semantic view to extract latent knowledge domains. • Users' expertise is determined on historical performance, and data sparseness is alleviated by matrix factorization. • Evaluation is conducted on large scale dataset by comprehensive experiments, where preferable output is generated. Community Question Answering (CQA) websites can be claimed as the most major venues for knowledge sharing, and the most effective way of exchanging knowledge at present. Considering that massive amount of users are participating online and generating huge amount data, management of knowledge here systematically can be challenging. Expert recommendation is one of the major challenges, as it highlights users in CQA with potential expertise, which may help match unresolved questions with existing high quality answers while at the same time may help external services like human resource systems as another reference to evaluate their candidates. In this paper, we in this work we propose to exploring experts in CQA websites. We take advantage of recent distributed word representation technology to help summarize text chunks, and in a semantic view exploiting the relationships between natural language phrases to extract latent knowledge domains. By domains, the users' expertise is determined on their historical performance, and a rank can be compute to given recommendation accordingly. In particular, Stack Overflow is chosen as our dataset to test and evaluate our work, where inclusive experiment shows our competence. [ABSTRACT FROM AUTHOR]

• We employ statistical analysis to define a list of quality features. • We propose a joint model by fusing autoencoder and random forest in an end-to-end trainable way for fake review detection. • Experimental results on the real dataset demonstrate the efficiency of the model. 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. [ABSTRACT FROM AUTHOR]