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In recent years, many researchers take advantage of graph convolution network in multi-behavior recommendation to further alleviate the data sparsity problem. However, most of current works directly use graph convolution network, which makes time complexity of the model relatively high. These works also ignore the different weights of neighbors and the different contributions of each behavior to user' s preference . Therefore, this paper proposed a multi-behavior recommendation model based on light graph convolution and enhanced attention ( MB-LGCA) . Firstly, the model constructed a user-item bipartite graph according to the multi-behavior data, and used a light graph convolution network to aggregate the features of neighbors to obtain high-order collaborative information. At the same time, it used attention mechanism to integrate the neighbors' weights to enhance embedding representations of nodes . It used the k-order user 's embedding propagation to obtain the different importance of each behavior to user ' s preference, so that the model had better interpretability. Finally, it combined embedding representations of different layers for prediction. The experimental results on two real datasets show that the model has better performance. [ABSTRACT FROM AUTHOR]

For the high dimension and insufficient sample of side information and user comment input in the recommendation algorithm, this paper proposed an attentive collaborative side variational autoencoder recommender model (sVAE-a) for the nonlinear modeling ability of variational autoencoder and the related data enhancement ability of the attention mechanism. This model used collaborative side variational autoencoder to model the side information. At the same time, it used the attention mechanism to integrate the side information into the collaborative variational autoencoder architecture to strengthen the hidden variables and provide the decoder with cleaner features. At last, it used the variational inference to approximate the side information and the user's comments, and obtained the recommended model through the parameters training. The experimental results on the MovieLens-20M dataset show that the method can improve not only from the basic recall rate index, but also from the coverage rate and normalized damage cumulative gain (NOCG) index. The model is easy to implement. It can combine different types of input and side information, and improve recommendation performance. [ABSTRACT FROM AUTHOR]

Neighborhood-based top-N recommendation techniques for implicit users ' inte raction data are ranking models, of which similarity functions are the important ingredients. Traditional similarity functions suffer from two major issues: sparse data and high dimensional data. Sparse data hinder the recommendation system from scoring on smooth neighborhood, high dimensional data causes the curse of dimensionality problem. This paper proposed a representation-based approach named multi object representation learning ( MO) for recommendation, MO was a bipartite node representation learning algorithm, which embedded the different level of network structures and item ordering information into nodes representations to help leverage the recommendation performance. Experimental results on three real data sets of different scales show that the algorithm has higher accuracy and recall than the commonly used recommendation model based on implicit feedback, especially for large-scale data sets, which can effectively alleviate the problems of matrix sparsity and dimensionality disaster, and improve the recommended performance. [ABSTRACT FROM AUTHOR]

In order to solve the problems existing in the learning recommendation algorithm, such as ignoring the analysis of the students' mastery of knowledge points and failing to probabilize the knowledge mastery, this paper proposed a recommendation method based on multiple factors. The method focused on the comprehensive weight of knowledge points, error rate and loss rate, and built a knowledge point mastery probability model, and applied the proposed strategy to implement an online personalized learning recommendation system. In terms of the systematic evaluation, through a survey of 200 high school students, the accuracy of the top-8 knowledge points recommended by this system achieves 91. 2% and F1 achieves 78. 4%. The results of the systematic survey reflect the effectiveness and reliability of the proposed strategy. [ABSTRACT FROM AUTHOR]