Intelligent Recommendation Algorithm of Multimedia English Distance Education Resources Based on User Model

Based on the understanding and comparison of various main recommendation algorithms, this paper focuses on the collaborative filtering algorithm and proposes a collaborative filtering recommendation algorithm with improved user model. Firstly, the algorithm considers the score difference caused by different user scoring habits when expressing preferences and adopts the decoupling normalization method to normalize the user scoring data; secondly, considering the forgetting shift of user interest with time, the forgetting function is used to simulate the forgetting law of score, and the weight of time forgetting is introduced into user score to improve the accuracy of recommendation; finally, the similarity calculation is improved when calculating the nearest neighbor set. Based on the Pearson similarity calculation, the effective weight factor is introduced to obtain a more accurate and reliable nearest neighbor set. The algorithm establishes an offline user model, which makes the algorithm have better recommendation efficiency. Two groups of experiments were designed based on the mean absolute error (MAE). One group of experiments tested the parameters in the algorithm, and the other group of experiments compared the proposed algorithm with other algorithms. The experimental results show that the proposed method has better performance in recommendation accuracy and recommendation efficiency.

ACE-GCN: A Fast Data-driven FPGA Accelerator for GCN Embedding

ACE-GCN is a fast and resource/energy-efficient FPGA accelerator for graph convolutional embedding under data-driven and in-place processing conditions. Our accelerator exploits the inherent power law distribution and high sparsity commonly exhibited by real-world graphs datasets. Contrary to other hardware implementations of GCN, on which traditional optimization techniques are employed to bypass the problem of dataset sparsity, our architecture is designed to take advantage of this very same situation. We propose and implement an innovative acceleration approach supported by our “implicit-processing-by-association” concept, in conjunction with a dataset-customized convolutional operator. The computational relief and consequential acceleration effect arise from the possibility of replacing rather complex convolutional operations for a faster embedding result estimation. Based on a computationally inexpensive and super-expedited similarity calculation, our accelerator is able to decide from the automatic embedding estimation or the unavoidable direct convolution operation. Evaluations demonstrate that our approach presents excellent applicability and competitive acceleration value. Depending on the dataset and efficiency level at the target, between 23× and 4,930× PyG baseline, coming close to AWB-GCN by 46% to 81% on smaller datasets and noticeable surpassing AWB-GCN for larger datasets and with controllable accuracy loss levels. We further demonstrate the unique hardware optimization characteristics of our approach and discuss its multi-processing potentiality.

A Personalized Recommendation Algorithm Based on Weighted Information Entropy and Particle Swarm Optimization

Similarity calculation is the most important basic algorithm in collaborative filtering recommendation. It plays an important role in calculating the similarity between users (items), finding nearest neighbors, and predicting scores. However, the existing similarity calculation is affected by over reliance on item scores and data sparsity, resulting in low accuracy of recommendation results. This paper proposes a personalized recommendation algorithm based on information entropy and particle swarm optimization, which takes into account the similarity of users’ score and preference characteristics. It uses random particle swarm optimization to optimize their weights to obtain the comprehensive similarity value. Experimental results on public data sets show that the proposed method can effectively improve the accuracy of recommendation results on the premise of ensuring recommendation coverage.

A Correlation Analysis of Geomagnetic Field Characteristics in Geomagnetic Perceiving Navigation

It is well-known that geomagnetic fields have multiple components or parameters, and that these geomagnetic parameters are related to each other. In this paper, a parameter selection method is proposed, and this paper mainly discusses the correlation of geomagnetic field parameters for geomagnetic navigation technology. For the correlation analysis between geomagnetic parameters, the similarity calculation of the correlation coefficient is firstly introduced for geomagnetic navigation technology, and the grouped results are obtained by data analysis. At the same time, the search algorithm (Hex-path algorithm) is used to verify the correlation analysis results. The results show the same convergent state for the approximate correlation coefficient. In other words, the simulation results are in agreement with the similarity calculation results.

Identification of miRNA-Small Molecule Associations by Continuous Feature Representation Using Auto-Encoders

MicroRNAs (miRNAs) are short non-coding RNAs that play important roles in the body and affect various diseases, including cancers. Controlling miRNAs with small molecules is studied herein to provide new drug repurposing perspectives for miRNA-related diseases. Experimental methods are time- and effort-consuming, so computational techniques have been applied, relying mostly on biological feature similarities and a network-based scheme to infer new miRNA–small molecule associations. Collecting such features is time-consuming and may be impractical. Here we suggest an alternative method of similarity calculation, representing miRNAs and small molecules through continuous feature representation. This representation is learned by the proposed deep learning auto-encoder architecture. Our suggested representation was compared to previous works and achieved comparable results using 5-fold cross validation (92% identified within top 25% predictions), and better predictions for most of the case studies (avg. of 31% vs. 25% identified within the top 25% of predictions). The results proved the effectiveness of our proposed method to replace previous time- and effort-consuming methods.

Application of the Deep Learning Algorithm and Similarity Calculation Model in Optimization of Personalized Online Teaching System of English Course

This study provides an in-depth study and analysis of English course recommendation techniques through a combination of bee colony algorithm and neural network algorithm. In this study, the acquired text is trained with a document vector by a deep learning model and combined with a collaborative filtering method to recommend suitable courses for users. Based on the analysis of the current research status and development of the technology related to course resource recommendation, the deep learning technology is applied to the course resource recommendation based on the current problems of sparse data and low accuracy of the course recommendation. For the problem that the importance of learning resources to users changes with time, this study proposes to fuse the time information into the neural collaborative filtering algorithm through the clustering classification algorithm and proposes a deep learning-based course resource recommendation algorithm to better recommend the course that users want to learn at this stage promptly. Secondly, the course cosine similarity calculation model is improved for the course recommendation algorithm. Considering the impact of the number of times users rate courses and the time interval between users rating different courses on the course similarity calculation, the contribution of active users to the cosine similarity is reduced and a time decay penalty is given to users rating courses at different periods. By improving the hybrid recommendation algorithm and similarity calculation model, the error value, recall, and accuracy of course recommendation results outperform other algorithmic models. The requirements analysis identifies the personalized online teaching system with rural primary and secondary school students as the main service target and then designs the overall architecture and functional modules of the recommendation system and the database table structure to implement the user registration, login, and personal center functional modules, course publishing, popular recommendation, personalized recommendation, Q&A, and rating functional modules.

Traditional Chinese Medicine Text Similarity Calculation Model Based on the Bidirectional Temporal Siamese Network

The text similarity calculation plays a crucial role as the core work of artificial intelligence commercial applications such as traditional Chinese medicine (TCM) auxiliary diagnosis, intelligent question and answer, and prescription recommendation. However, TCM texts have problems such as short sentence expression, inaccurate word segmentation, strong semantic relevance, high feature dimension, and sparseness. This study comprehensively considers the temporal information of sentence context and proposes a TCM text similarity calculation model based on the bidirectional temporal Siamese network (BTSN). We used the enhanced representation through knowledge integration (ERNIE) pretrained language model to train character vectors instead of word vectors and solved the problem of inaccurate word segmentation in TCM. In the Siamese network, the traditional fully connected neural network was replaced by a deep bidirectional long short-term memory (BLSTM) to capture the contextual semantics of the current word information. The improved similarity BLSTM was used to map the sentence that is to be tested into two sets of low-dimensional numerical vectors. Then, we performed similarity calculation training. Experiments on the two datasets of financial and TCM show that the performance of the BTSN model in this study was better than that of other similarity calculation models. When the number of layers of the BLSTM reached 6 layers, the accuracy of the model was the highest. This verifies that the text similarity calculation model proposed in this study has high engineering value.

A Self-Supervised Tibetan-Chinese Vocabulary Alignment Method

Tibetan is a low-resource language. In order to alleviate the shortage of parallel corpus between Tibetan and Chinese, this paper uses two monolingual corpora and a small number of seed dictionaries to learn the semi-supervised method with seed dictionaries and self-supervised adversarial training method through the similarity calculation of word clusters in different embedded spaces and puts forward an improved selfsupervised adversarial learning method of Tibetan and Chinese monolingual data alignment only. The experimental results are as follows. The seed dictionary of semi-supervised method made before 10 predicted word accuracy of 66.5 (Tibetan - Chinese) and 74.8 (Chinese - Tibetan) results, to improve the self-supervision methods in both language directions have reached 53.5 accuracy.