Abstract:Product reviews play an important role in guiding users' purchase decision-making in e-commerce platforms.However, it is challenging for users to find helpful reviews that meet their preferences and experiences among an overwhelming amount of reviews.While some existing personalized review recommendation models neglect an user's aspect preferences or the user-product interactions for measuring user similarity.Moreover, those works predict review helpfulness at the review-level (a review is taken as a whole); few of them consider the aspect-level.To address the above issues, this paper propose an users' aspect sentiment similarity-based personalized review recommendation model ($A2SPR$), which quantifies review helpfulness and recommends reviews that are customized for each individual.Firstly, the paper analyze users' aspect preferences from reviews and improve user similarity with users' fine-grained sentiment similarity and product relevance.Furthermore, the review helpfulness score is redefined at the aspect level, which indicates the review's reference value for users' purchase decisions. Finally, recommending the top $k$ helpful reviews for individuals based on the review helpfulness score. To validate the performance of the proposed model, eight baselines are developed and compared.Experimental results show that our model performs better than those baselines in both the coverage and precision.

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	1. Personalized Review Recommendation based on Users' Aspect Sentiment
	CHUNLI HUANG, WENJUN JIANG,JIE WU,GUOJUN WANG
	Computer Science and Technology 02 April 2020
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	Abstract:Product reviews play an important role in guiding users' purchase decision-making in e-commerce platforms.However, it is challenging for users to find helpful reviews that meet their preferences and experiences among an overwhelming amount of reviews.While some existing personalized review recommendation models neglect an user's aspect preferences or the user-product interactions for measuring user similarity.Moreover, those works predict review helpfulness at the review-level (a review is taken as a whole); few of them consider the aspect-level.To address the above issues, this paper propose an users' aspect sentiment similarity-based personalized review recommendation model ($A2SPR$), which quantifies review helpfulness and recommends reviews that are customized for each individual.Firstly, the paper analyze users' aspect preferences from reviews and improve user similarity with users' fine-grained sentiment similarity and product relevance.Furthermore, the review helpfulness score is redefined at the aspect level, which indicates the review's reference value for users' purchase decisions. Finally, recommending the top $k$ helpful reviews for individuals based on the review helpfulness score. To validate the performance of the proposed model, eight baselines are developed and compared.Experimental results show that our model performs better than those baselines in both the coverage and precision.
	TO cite this article:CHUNLI HUANG, WENJUN JIANG,JIE WU, et al. Personalized Review Recommendation based on Users' Aspect Sentiment[OL].[ 2 April 2020] http://en.paper.edu.cn/en_releasepaper/content/4751464


	2. Anomaly Detection Based on Locality-preferred Recoding of GAN Network
	Wei Huang,WANG Jianzhu,DONG Bangyi,MENG Qinglong,SHI Chuan,LI Qingyong
	Computer Science and Technology 26 March 2020
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	Abstract:Deep neural networks, including deep auto-encoder (DAE) and generative adversarial networks (GAN), have been extensively applied for anomaly detection. These models generally assume that reconstruction errors should be lower for normal samples but higher for anomalies. However, it has been found that DAE based models can sometimes reconstruct anomalies very well and thus result in miss detection. To address this problem, we propose an anomaly detection model using GAN with locality-preferred recoding, named LRGAN. LRGAN is inspired by the observation that both normal and abnormal samples are not scattered throughout the latent space but clustered separately at some local regions. Therefore, a locality-preferred recoding (LR) module is designed to compulsively represent the latent vectors of anomalies by normal ones, making the reconstructions approach to normal samples and thereby enlarging the residuals. To partly avoid latent vectors of normal samples being recoded, we further put forward to detect anomalies using GAN with an adaptive LR ($ALR$), named LRGAN+. Our proposed method is evaluated on two public datasets (i.e., MNIST and CIFAR-10) and one Fasteners dataset from practical application, considering both one-class anomaly detection and multi-class anomaly detection scenarios. Experimental results demonstrate that LRGAN is comparable with state-of-the-art methods and LRGAN+ outperforms these methods on all datasets.
	TO cite this article:Wei Huang,WANG Jianzhu,DONG Bangyi, et al. Anomaly Detection Based on Locality-preferred Recoding of GAN Network[OL].[26 March 2020] http://en.paper.edu.cn/en_releasepaper/content/4751373

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	3. A Category-Based Calibration Approach with Fault Tolerance for Air Monitoring Sensors
	WANG Rao,LI Qing-Yong,YU Hao-Min,YU Hao-Min,CHEN Ze-Chuan,ZHANG Ying-Jun,ZHANG Ling,CUI Hou-Xin,ZHANG Ke
	Computer Science and Technology 20 March 2020
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	Abstract:Air pollution monitoring has attracted much attention in recent years because of the deterioration of the environment. Standard stations, installed by governments with a high cost, can provide reliable air quality information; whereas a large number of portable air monitoring sensors with low cost are widely used and output less precise results.In this paper, we propose a category-based calibration approach (CCA) using machine learning algorithms for such portable sensors. Compared with traditional methods that often learn a single regression model, CCA includes multiple regression models according to pollutant concentration categories, and builds a more accurate mapping from sensor readings to reference. Furthermore, CCA introduces two fault-tolerance modules: classification tolerance and sample tolerance. The former mitigates the impact of misclassification for concentration category, and the latter improves the robustness of individual regression model. Our approach is evaluated on carbon monoxide (CO) and ozone (O$_{3}$) from two cities of China. The experiment results show that CCA has a better performance than traditional calibration models in both accuracy and robustness.
	TO cite this article:WANG Rao,LI Qing-Yong,YU Hao-Min, et al. A Category-Based Calibration Approach with Fault Tolerance for Air Monitoring Sensors[J].


	4. DCDN: Double Cross & Deep Network for News Recommandation
	Zhihong Yang,Yulong Wang
	Computer Science and Technology 18 March 2020
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	Abstract:The recommendation system is widely used in Internet products, and the recommendation algorithm is paid more and more attention by researchers. This paper proposes Double Cross & Deep Network (DCDN) algorithm for news recommendation. On the basis of DCN network, this algorithm proposes a new double-crossing depth network, which extracts the features of "related news" in the recommended candidate set separately, and displays the feature crossing with the user information and the seed news information respectively. The two Cross networks and Deep networks of DCDN are independent from each other. Cross Netword is used to obtain the Cross information between features, and Deep Network is used to model high-order nonlinear features. Users can change Network parameters according to the prediction requirements. Among them, SR-Cross is used to ensure the correlation between seed news and recommended news, and UR-Cross combines with user portrait to improve users\' reading interest. The experiment on two real data sets proves that the DCDN algorithm has better accuracy performance compared with other deep learning models and is practical in engineering while guaranteeing the speed.
	TO cite this article:Zhihong Yang,Yulong Wang. DCDN: Double Cross & Deep Network for News Recommandation[OL].[18 March 2020] http://en.paper.edu.cn/en_releasepaper/content/4751250


	5. Research on Recommendation Based on DeepFM and Graph Embedding
	Yang Zhixiang,Liu Xiaohong
	Computer Science and Technology 10 March 2020
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	Abstract:The traditional recommendation system usually focuses on the coupling of feature information between users and items, but fails to effectively investigate the complex networks of users and items. At the same time, the graph algorithms are often used to analyze the point-edge relationships in networks, and we can combine as many network node features as possible through graph machine learning. To this end, in this paper, by combining the graph algorithm with the recommendation algorithm, prediction is conducted by embedding information. First, we employ the DeepFM and the GNNs to perform information mining of explicit and implicit features of the feature information and the heterogeneous structure network. Then, we combine the features of the two embedding layers to construct the final embedding vector. Finally, we use a multi-layer fully connected and activation function to predict the results. Two standard data sets are used in our experiment. The results show that the new model has the best performance in the recommended field.
	TO cite this article:Yang Zhixiang,Liu Xiaohong. Research on Recommendation Based on DeepFM and Graph Embedding[OL].[10 March 2020] http://en.paper.edu.cn/en_releasepaper/content/4751142


	6. A Keyword extraction method based on Neural Networks with Joint Training
	You Huanying,She Chundong,Liu Shaohua
	Computer Science and Technology 07 March 2020
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	Abstract:Keyword extraction technology has gradually become a hot research problem in Natural Language Processing (NLP) and Information Retrieval. Many language tasks are inseparable from keyword extraction technology, such as long text classification, automatic summary, machine translation, dialogue system, etc. In this paper, we design a keyword extraction algorithm that can combine the benefits of both memorization and generalization. Our model contains a linear model and a deep neural networks. The linear model learns the relationship between statistic features and keywords, which can make full use of the memory capabilities of the shallow model. In the deep component, we feed the projection vector of words on the text to deep neural networks, which can enhance the generalization ability of the model. With the joint training of the linear model and the deep neural networks, our model achieves higher accuracy and scalability. Our method is compared with Frequency, Term Frequency-Inverse Document Frequency (TF-IDF) and TextRank. On the same batch of test dataset, our model is superior to the baseline model in Precision, Recall, and F-score, respectively.
	TO cite this article:You Huanying,She Chundong,Liu Shaohua. A Keyword extraction method based on Neural Networks with Joint Training[OL].[ 7 March 2020] http://en.paper.edu.cn/en_releasepaper/content/4751038


	7. Domain adaptive image retrieval based on region of interest
	Zhao Zhen,Ai Xinbo
	Computer Science and Technology 03 March 2020
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	Abstract:Recently, the explosive growth of image data, how to retrieve effective images has become an urgent problem. However, image retrieval often faces the following problems.In the current image retrieval model, the information of local area of interest is less considered. When images exist in two different domain distributions, cross-domain retrieval cannot be performed effectively.In view of the current existence of the above problems, this paper put forward based on the interested region of domain adaptive image retrieval methods, including the interest of the target detection technology of image area, the interference of background information filter is invalid, feature fusion method for multi-objective regional characteristics of effective at the same time to join the different domain image domain structure, realization of cross-domain retrieval.In this paper, we evaluated the effectiveness of our method on the PASCAL VOC dataset.
	TO cite this article:Zhao Zhen,Ai Xinbo. Domain adaptive image retrieval based on region of interest[OL].[ 3 March 2020] http://en.paper.edu.cn/en_releasepaper/content/4750996


	8. Conversational Recommendation System based on Sentiment Analysis
	LI Xinsheng,LI Jian
	Computer Science and Technology 25 February 2020
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	Abstract:The combination of the recommender system and dialogue system which called the conversational recommendation system is a growing interest. Tosolve the problem that it is difficult to obtain users' tastes in conversational recommendation systems. A sentiment analysis method is proposed in our conversational recommendation model to get user preferences. A sentiment analysis dataset is created and the model uses a sentiment analysis approach to obtain a movie seeker\'s preferences and make a recommendation. Experimentresults show that our sentiment analysis model yields a better performance of 0.8362(F1 score) than the baseline(0.7802) and other models. Thus, the movie recommended by our system can meet the needs of users better.
	TO cite this article:LI Xinsheng,LI Jian. Conversational Recommendation System based on Sentiment Analysis[OL].[25 February 2020] http://en.paper.edu.cn/en_releasepaper/content/4750892


	9. Formal Verification of Calculus without Limits in Coq
	Guo Liquan,Yu Wensheng
	Computer Science and Technology 12 February 2020
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	Abstract:Artificial intelligence is one of China's current major science and technology development strategies. Mathematical formalization, as an important theoretical basis for artificial intelligence, is of great significance to the development of science and technology. Based on the proof assistant Coq, this paper realizes the formal verification of calculus without limit theory, includes Coq descriptions of Uniformly Continuity, Uniformly Derivable, Strongly Derivable and Integral System. Then, this paper prove some properties of Uniformly Derivable and Valuation Theorem with Coq, all formalization processes have been verified by Coq. The formalization demonstrates that the Coq-based mechanized proof of mathematics theorem has the characteristics of readability and interactivity.
	TO cite this article:Guo Liquan,Yu Wensheng. Formal Verification of Calculus without Limits in Coq[OL].[12 February 2020] http://en.paper.edu.cn/en_releasepaper/content/4750736


	10. Multi-emotional single-track music generating model based on LSTM
	WANG Xicheng,LI Wei
	Computer Science and Technology 11 February 2020
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	Abstract:With the popularity of short video platforms, it has become very common for users to create videos for sharing. As an integral part of short videos, background music plays an important role in emotional expression. However, the background music currently in short video platforms is relatively single, and it also involves copyright issues. In this paper, by improving existing music generation model, a multi-emotional single-track music generation model is proposed. By analyzing the advantages and disadvantages of the original network and the lookback mechanism, and combining with the actual application scenario, the LB-Attention model is proposed. Note positioning information, music emotional information, and attention mechanism are introduced into the model to achieve the requirements of application scenarios. By comparing the generated results and performance indicators of the original model and the model in this paper, it is concluded that the model has excellent music generation effect. The performance of LB-Attention model is similar to the original model, and can basically meet the needs of the application scenario.
	TO cite this article:WANG Xicheng,LI Wei. Multi-emotional single-track music generating model based on LSTM[OL].[11 February 2020] http://en.paper.edu.cn/en_releasepaper/content/4750718

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