A Rating Prediction Recommendation Model Combined with the Optimizing Allocation for Information Granularity of Attributes

In recent years, graph neural networks (GNNS) have been demonstrated to be a powerful way to learn graph data. The existing recommender systems based on the implicit factor models mainly use the interactive information between users and items for training and learning. A user–item graph, a user–attribute graph, and an item–attribute graph are constructed according to the interactions between users and items. The latent factors of users and items can be learned in these graph structure data. There are many methods for learning the latent factors of users and items. Still, they do not fully consider the influence of node attribute information on the representation of the latent factors of users and items. We propose a rating prediction recommendation model, short for LNNSR, utilizing the level of information granularity allocated on each attribute by developing a granular neural network. The different granularity distribution proportion weights of each attribute can be learned in the granular neural network. The learned granularity allocation proportion weights are integrated into the latent factor representation of users and items. Thus, we can capture user-embedding representations and item-embedding representations more accurately, and it can also provide a reasonable explanation for the recommendation results. Finally, we concatenate the user latent factor-embedding and the item latent factor-embedding and then feed it into a multi-layer perceptron for rating prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework.

The Development and Validation of the Doctoral Student Identity Scale

Objective: Based on how the identity of doctoral students is recognized and understood in the context of Chinese culture, we developed a doctoral identity scale using both qualitative and quantitative analyses.Methods: The initial project of the Scale was formed through qualitative analyses and expert consultation. Nine hundred and ninety-one doctoral students were officially tested, and 982 valid questionnaires were obtained. They were randomly divided into two parts, and 491 of which were assessed for item Response Theory (IRT) and exploratory factor analysis (EFA) and 491 of which were assessed for confirmatory factor analysis (CFA). The Subjective Well-Being Scale (SWB), the Rosenberg Self-Esteem Scale (RSE), and the Psychological Sense of School Membership Scale (PSSM) were used to test its the criterion-related validity. One hundred and forty-one students were selected for retesting after 8 weeks.Results: The doctoral student identity questionnaire consisted of two factors identity exploration and identity commitment, explaining 57% of the total variance. The results of CFA showed that the two-factor model fitted the data well. The two dimensions of the Doctoral Student Identity Scale were significantly and positively correlated with the two dimensions of the SWB scale (0.32–0.66), the latent factor of the RSE scale (0.42–0.55), and the latent factor of the PSSM scale (0.52–0.62). Composite reliability values for exploration and commitment were 0.79 and 0.83 respectively, and the values of McDonald’s omega for exploration and commitment were 0.81 and 0.85 respectively. The test-retest reliability of the total questionnaire was 0.842.Conclusion: The Doctoral Student Identity Scale was developed with good reliability and validity, and can be used as a reliable tool for measuring the doctoral student identity. In addition, the questionnaire will provide corresponding ideas and methods for studying the identity issues of specific groups.

Word Sequential Using Deep LSTM and Matrix Factorization to Handle Rating Sparse Data for E-Commerce Recommender System

Recommender systems are essential engines to deliver product recommendations for e-commerce businesses. Successful adoption of recommender systems could significantly influence the growth of marketing targets. Collaborative filtering is a type of recommender system model that uses customers’ activities in the past, such as ratings. Unfortunately, the number of ratings collected from customers is sparse, amounting to less than 4%. The latent factor model is a kind of collaborative filtering that involves matrix factorization to generate rating predictions. However, using only matrix factorization would result in an inaccurate recommendation. Several models include product review documents to increase the effectiveness of their rating prediction. Most of them use methods such as TF-IDF and LDA to interpret product review documents. However, traditional models such as LDA and TF-IDF face some shortcomings, in that they show a less contextual understanding of the document. This research integrated matrix factorization and novel models to interpret and understand product review documents using LSTM and word embedding. According to the experiment report, this model significantly outperformed the traditional latent factor model by more than 16% on an average and achieved 1% on an average based on RMSE evaluation metrics, compared to the previous best performance. Contextual insight of the product review document is an important aspect to improve performance in a sparse rating matrix. In the future work, generating contextual insight using bidirectional word sequential is required to increase the performance of e-commerce recommender systems with sparse data issues.