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Aspect-level sentiment classification aims to discriminate the sentiment polarity of a specific aspect in a sentence. Although attention-based recurrent neural network models perform well among existing solutions, they are not ideal for processing "semantically ambiguous" sentences that are short and contain many neologisms and polysemous words. Therefore, this paper proposes a neural network model based on knowledge graph and attention mechanism. The basic idea is to use a knowledge base to obtain a relevant concept set of aspect words and integrate external information to enhance the semantic representation of the text. Firstly, the output of bidirectional long short-term memory network is combined with self-attention mechanism to generate context representation. Then, the upper and lower context representations are combined to use dual attention to obtain external knowledge from the knowledge graph and obtain knowledge vectors related to aspect words. Finally, the two parts of content are input together into a fully connected network to calculate the aspect-level sentiment tendency. Experimental results show that compared with other models, the proposed model significantly improves classification performance. [ABSTRACT FROM AUTHOR]

Knowledge Graph (KG) owns rich structured information, which can effectively alleviate the sparsity and cold start problem of recommendation model and improve the accuracy and interpretability of recommendation. In recent years, the end-to-end recommendation models incorporating knowledge graph have become a technological trend. This paper proposes a KG recommendation model incorporating the influence effects of similar users. The proposed method expands the interaction between users and items to effectively utilize the knowledge graph. Firstly, a graph neural network neighbor aggregation strategy and an attention mechanism are used to capture two higher-order representations of users and items on the knowledge graph respectively. Then, an influence enhancement layer is designed to capture potential representations of similar users􀆳 influence effects according to their influence effects. Finally, these three representations are fed together into a multi-layer perceptron to output prediction scores. Experimental results on real datasets show the effectiveness and efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

Recurrent neural networks play an important role in sequence recommendation. However, in recommendation, the users behavior sequences are far more complicated than the sentences in natural language processing or images in computer vision. A single recurrent neural network structure is difficult to fully mine user preferences, so this paper proposes a new sequence recommendation algorithm that takes into account both the time information and content information of the sequence. This algorithm is mainly divided into two parts： improved item embedding and sequence preference learning. Firstly，an item embedding method that incorporates a knowledge graph is used to generate high-quality item vectors. Secondly，a sequence modeling method combining convolutional neural networks with long-term and short-term memory neural networks is proposed. Furthermore, an attention-based framework is proposed, which dynamically combines users points of interest. This algorithm is compared with the traditional methods and the existing advanced methods of the same type on the public data set MovieLenslOM. The experimental results show that the average reciprocal ranking MRR@N of the recommended evaluation index and the recall rate Recall@N is improved significantly, which verifies the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]