Nowadays,the personalized recommendation has become a research hotspot for addressing information *** this,generating effective recommendations from sparse data remains a ***,auxiliary information has been widely used...
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Nowadays,the personalized recommendation has become a research hotspot for addressing information *** this,generating effective recommendations from sparse data remains a ***,auxiliary information has been widely used to address data sparsity,but most models using auxiliary information are linear and have limited *** to the advantages of feature extraction and no-label requirements,autoencoder-based methods have become quite ***,most existing autoencoder-based methods discard the reconstruction of auxiliary information,which poses huge challenges for better representation learning and model *** address these problems,we propose Serial-Autoencoder for Personalized Recommendation(SAPR),which aims to reduce the loss of critical information and enhance the learning of feature ***,we first combine the original rating matrix and item attribute features and feed them into the first autoencoder for generating a higher-level representation of the ***,we use a second autoencoder to enhance the reconstruction of the data representation of the prediciton rating *** output rating information is used for recommendation *** experiments on the MovieTweetings and MovieLens datasets have verified the effectiveness of SAPR compared to state-of-the-art models.
Breast cancer is an extremely serious disease that predominantly affects women. Nevertheless, the likelihood of mortality decreases significantly when the condition is detected and managed promptly during its early st...
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Intelligent devices often produce time series data that suffer from significant data quality issues. While the utilization of data dependency in error detection and data repair has been somewhat beneficial, it remains...
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Skin disease detection has undergone significant advancements with the advent of deep learning-based image segmentation techniques. In this paper, we provide a comprehensive overview of the evolution of skin disease d...
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Multi-label text classification is a fundamental task in the field of natural language processing. Currently, there are issues in the Chinese multi-label text classification tasks, such as insufficient extraction of t...
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In this pivotal study, we delve into the imperative realm of Diabetic Retinopathy (DR), a sight-threatening eye disease, introducing a nuanced and comprehensive approach to its detection through cutting-edge deep lear...
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The key-value separation is renowned for its significant mitigation of the write amplification inherent in traditional LSM trees. However, KV separation potentially increases performance overhead in the management of ...
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Can artists be recognized from the way they render certain materials, such as fabric, skin, or hair? In this paper, we study this problem with a focus on recognizing works by Rembrandt, Van Dyck, and other Dutch and F...
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Rough set theory has a very good effect in information processing and knowledge *** an information table,the current scholars regard all objects as a universe,and then establish various rough set ***,in the analysis o...
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Rough set theory has a very good effect in information processing and knowledge *** an information table,the current scholars regard all objects as a universe,and then establish various rough set ***,in the analysis of many data problems,it is more reasonable to select parts of objects which are useful to us or can meet the actual needs as a ***,in order to make up for the deficiency of traditional models,a new model is introduced from the perspective of variable ***,some interesting properties of this model,such as approximation sets,reduct of attributes and maximum part of universe,are *** the study of this paper,it can be seen that the model developed in our paper is not only more accurate but also more effective in describing uncertain knowledge.
Due to the powerful automatic feature extraction, deep learning-based vulnerability detection methods have evolved significantly in recent years. However, almost all current work focuses on detecting vulnerabilities a...
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Due to the powerful automatic feature extraction, deep learning-based vulnerability detection methods have evolved significantly in recent years. However, almost all current work focuses on detecting vulnerabilities at a single granularity (i.e., slice-level or function-level). In practice, slice-level vulnerability detection is fine-grained but may contain incomplete vulnerability details. Function-level vulnerability detection includes full vulnerability semantics but may contain vulnerability-unrelated statements. Meanwhile, they pay more attention to predicting whether the source code is vulnerable and cannot pinpoint which statements are more likely to be vulnerable. In this paper, we design mVulPreter, a multi-granularity vulnerability detector that can provide interpretations of detection results. Specifically, we propose a novel technique to effectively blend the advantages of function-level and slice-level vulnerability detection models and output the detection results' interpretation only by the model itself. We evaluate mVulPreter on a dataset containing 5,310 vulnerable functions and 7,601 non-vulnerable functions. The experimental results indicate that mVulPreter outperforms existing state-of-the-art vulnerability detection approaches (i.e., Checkmarx, FlawFinder, RATS, TokenCNN, StatementLSTM, SySeVR, and Devign). IEEE
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