Towards Accurate Deceptive Opinion Spam Detection based on Word Order-preserving CNN

Nowadays, deep learning has been widely used. In natural language learning, the analysis of complex semantics has been achieved because of its high degree of flexibility. The deceptive opinions detection is an important application area in deep learning model, and related mechanisms have been given attention and researched. On-line opinions are quite short, varied types and content. In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions, and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis. The detection mechanism based on deep learning has better self-adaptability and can effectively identify all kinds of deceptive opinions. In this paper, we optimize the convolution neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolution neural network more suitable for various text classification and deceptive opinions detection. The TensorFlow-based experiments demonstrate that the detection mechanism proposed in this paper achieve more accurate deceptive opinion detection results

Survey for Trust-aware Recommender Systems: A Deep Learning Perspective

A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results. Thus, it becomes critical to embrace a trustworthy recommender system. This survey provides a systemic summary of three categories of trust-aware recommender systems: social-aware recommender systems that leverage users' social relationships; robust recommender systems that filter untruthful noises (e.g., spammers and fake information) or enhance attack resistance; explainable recommender systems that provide explanations of recommended items. We focus on the work based on deep learning techniques, an emerging area in the recommendation research