Collaborative filtering is one of the most commonly used methods in recommendation systems. However, the sparsity of the rating matrix, cold start-up, and most recommendation algorithms only consider the users while neglecting the relationship between the products, all of what limit the effectiveness of the recommendation algorithms. In this paper, based on the self-attention mechanism, a deep learning model, named HARSAM, is proposed for modeling user interaction data and learning the user's latent preference expression. HARSAM partitions the user's latent feedback data in different time granularity and employs the self-attention mechanism to extract the correlation among the data in each partition. Moreover, the model learns the user's latent preferences through the deep neural network. Simultaneously, the model learns the item latent representation by making use of the stacked denoising autoencoder to model the item's rating data. As the result, the model recommends items to users according to the similarities between user's preference and items. Experiments conducted on the public data demonstrate the effectiveness of the proposed model.

Sentence matching is crucial to many natural language processing (NLP) tasks. Generally, the degree of matching is measured from either of the two perspectives: topic-based match or semantic-based match. The former is to investigate if two sentences discuss the same topic, and the latter performs a deep level semantic matching of texts, which is currently the highlight in research. Deep semantic matching requires adequate modeling from the internal structure of the language objects as well as their interactions. To achieve this goal, this paper proposes a multiple-perspective semantics-crossover (MPSC) model for modeling the semantic-based match of two sentences. The model extracts the matching information of two sentences from the semantic interaction information generated from different angles, so as to calculate the matching degree of the two sentences. The MPSC model not only captures rich matching patterns at different levels but also acquires interactive features from different semantic angles. It can be used to address some important issues in NLP fields, such as information matching in text retrieval, question-answer matching in the Q&A system, and so on. The experimental results show that our proposed model of MPSC has better effectiveness than some popular semantic matching approaches.INDEX TERMS Natural language processing, neural networks, semantics matching, text analysis.