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Improve top-K recommendation by extending review analysis

14th Asia Pacific Web Technology Conference, APWeb 2012

作者： Zhu, Qing Xing, Zhe Liang, Jingfan School of Information Renmin University of China Beijing 100872 China Key Laboratory for Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing 100872 China

ISBN: (纸本)9783642292521

The Web has become the popular place for people to purchase product and acquire services, so collaborative filtering is one of the most important algorithms applied in e-commerce recommendation systems. Unfortunately, it is widely recognized that the traditional recommendation methods are inefficient when the user rating data is extremely sparse. In order to overcome the limitations, good recommendation tools are needed to help Web customers determine the products and satisfaction services. In this paper, we propose a multi-dimensional adaptive recommendation algorithm by extending opinion analysis to improve top-k recommendation. In the first step, the novel algorithm that uses extened opinion analysis, creatively combines three dimensional recommendation models: user-based, item-based and opinion-based collaborative filtering. It successfully integrates opinion mining technology with collaborative filtering algorithm. In the second step, we configured the dynamic measurement would help us determine the weight of three dimensions: user-based, item-based and opinion-based analysis, and hence get the final prediction result. The experimental results show that multi-dimensional recommendation can effectively alleviate the dataset sparsity problem and achieve better prediction accuracy compared to other traditional collaborative recommendation algorithms. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Collaborative filtering

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A Semi-supervised Bilingual Lexicon Induction Method for Distant Language Pairs Based on Bidirectional Adversarial Model 12

A Semi-supervised Bilingual Lexicon Induction Method for Dis...

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12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Zhi, Wenwu Zhang, Yuhong Key Laboratory of Knowledge Engineering With Big Data Hefei University of Technology Ministry of Education !!!School of Computer Science and Information Engineering @@@Hefei University of Technology Hefei China

ISBN: (纸本)9781665438582

Bilingual lexicon induction (BLI) can transfer knowledgefrom well- to under- resourced language, and has been widelyapplied to various NLP tasks. Recent work on BLI is projection-based that learns a mapping to connect source and target embedding spaces, with the isomorphism assumption. Unfortunately, the isomorphism assumption doesn't hold gener-ally, especially in typologically distant language pairs. Moreover, without supervised signals guiding, the training will further com-plicates BLI, making the performance of unsupervised methods unsatisfactory. To broke the restrict of isomorphism, we propose a semi-supervised method for distant BLI tasks, named A Semi-supervised Bilingual Lexicon Induction method in Latent Space based on Bidirectional Adversarial Model. First, two latent spaces are learned by two autoencoders for source and target domain independently to weaken the constraint of isomorphism in the embedding spaces. Then we add a few pairs of dictionary to learn the initial mapping to connect the Latent Space. Last, based on initial mapping, Cycle-Consistency is combined with Distance constraint constraint to maintain the geometry structure of both embedding spaces stable in the learning of bi-direction mapping based on adversarial model. By conducting extensive experiments, our method gets state-of-the-art results on most language pairs, especially with significant improvements on distant language pairs. © 2021 IEEE.

关键词： Mapping

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Detecting stealthy malware with inter-structure and imported signatures 11

Detecting stealthy malware with inter-structure and imported...

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6th International Symposium on information, Computer and Communications Security, ASIACCS 2011

作者： Liang, Bin You, Wei Shi, Wenchang Liang, Zhaohui Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9781450305648

Recent years have witnessed an increasing threat from kernel rootkits. A common feature of such attack is hiding malicious objects to conceal their presence, including processes, sockets, and kernel modules. Scanning memory with object signatures to detect the stealthy rootkit has been proven to be a powerful approach only when it is hard for adversaries to evade. However, it is difficult, if not impossible, to select fields from a single data structure as robust signatures with traditional techniques. In this paper, we propose the concepts of inter-structure signature and imported signature, and present techniques to detect stealthy malware based on these concepts. The key idea is to use cross-reference relationships of multiple data structures as signatures to detect stealthy malware, and to import some extra information into regions attached to target data structures as signatures. We have inferred four invariants as signatures to detect hidden processes, sockets, and kernel modules in Linux respectively and implemented a prototype detection system called DeepScanner. Meanwhile, we have also developed a hypervisor-based monitor to protect imported signatures. Our experimental result shows that our DeepScanner can effectively and efficiently detect stealthy objects hidden by seven real-world rootkits without any false positives and false negatives, and an adversary can hardly evade DeepScanner if he/she does not break the normal functions of target objects and the system. Copyright 2011 ACM.

关键词： Malware

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EEG-Based Joint Semi-supervised Learning for Major Depressive Disorder Detection 26th

EEG-Based Joint Semi-supervised Learning for Major Depressi...

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26th International Conference on Pattern Recognition, ICPR 2022

作者： Chen, Tao Zheng, Tong Shi, Jinlong Guo, Yanrong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9783031376597

Major Depressive Disorder (MDD) has been a major mental disease in recent years, imposing huge negative impacts on both our society and individuals. The current clinical MDD detection methods, such as self-report scales and interviews, often suffer from their subjectiveness. In contrast, electroencephalography (EEG) signals reflect brain activity and can be used as an effective information source for MDD detection. However, the learning-based MDD detection methods with EEG data usually neglect the valuable information contained in the unlabeled data, therefore possibly suffering from the label scarcity issue. In this context, we propose a simple EEG-based Joint Semi-supervised Learning (JSL) model for the MDD detection task, which fully leverages the information from unlabeled data. Specifically, by equipping the unlabeled samples with the intermediately predicted pseudo labels, the accurate position of the class-specific centroids in the feature space can be gradually refined. Based on that, the auxiliary labels based on a heuristic centroid-based classifier can be obtained, which plays an effective complementary role in predicting the sample labels. The experiments on a public dataset demonstrate the effectivenes s of our method, showing an obvious performance gain via using the proposed semi-supervised strategy. © 2023, Springer Nature Switzerland AG.

关键词： Electroencephalography

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COCA: More accurate multi-dimensional histograms out of more accurate correlations detection

COCA: More accurate multi-dimensional histograms out of more...

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9th International Conference on Web-Age information Management, WAIM 2008

作者： Wei, Cao Xiongpai, Qin Wang, Shan Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769531854

Detecting and exploiting correlations among columns in relational databases are of great value for query optimizers to generate better query execution plans (QEPs). We propose a more robust and informative metric, namely, entropy correlation coefficients, other than chi-square test to detect correlations among columns in large datasets. We introduce a novel yet simple kind of multi-dimensional synopses named COCA-Hist to cope with different correlations in databases. With the aid of the precise metric of entropy correlation coefficients, correlations of various degrees can be detected effectively;when correlation coefficients testify to mutual independence among columns, the AVI (attribute value independence) assumption can be adopted undoubtedly. COCA can also serve as a data-mining tool with superior qualities as CORDS does. We demonstrate the effectiveness and accuracy of our approach by several experiments. © 2008 IEEE.

关键词： Entropy

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SEED: Entity oriented information search and exploration 22

SEED: Entity oriented information search and exploration

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22nd International Conference on Intelligent User Interfaces, IUI 2017

作者： Chen, Jun Jacucci, Giulio Chen, Yueguo Ruotsalo, Tuukka School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Helsinki Institute for Information Technology HIIT Department of Computer Science University of Helsinki Finland

ISBN: (纸本)9781450348935

Entity search and exploration can enrich search user interfaces by presenting relevant information instantly and offering relevant exploration pointers to users. Previous research has demonstrated that large knowledge Graphs allow exploitation and recommendation of explicit links between the entities and other information to improve information access and ranking. However, less attention has been devoted to user interfaces for effectively presenting results, recommending related entities and explaining relations between entities. We introduce a system called SEED which is designed to support entity search and exploration in large knowledge Graphs. We demonstrate SEED using a dataset of hundreds of thousands of movie related entities from the DBpedia knowledge Graph. The system utilizes a graph embedding model for ranking entities and their relations, recommending related entities, and explaining their interrelations. Copyright is held by the author/owner(s).

关键词： knowledge graph

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BERT-INT: A BERT-based interaction model for knowledge graph alignment 29

BERT-INT: A BERT-based interaction model for knowledge graph...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Tang, Xiaobin Zhang, Jing Chen, Bo Yang, Yang Chen, Hong Li, Cuiping Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China China Information School Renmin University of China China Zhejiang University China

ISBN: (纸本)9780999241165

knowledge graph alignment aims to link equivalent entities across different knowledge graphs. To utilize both the graph structures and the side information such as name, description and attributes, most of the works propagate the side information especially names through linked entities by graph neural networks. However, due to the heterogeneity of different knowledge graphs, the alignment accuracy will be suffered from aggregating different neighbors. This work presents an interaction model to only leverage the side information. Instead of aggregating neighbors, we compute the interactions between neighbors which can capture fine-grained matches of neighbors. Similarly, the interactions of attributes are also modeled. Experimental results show that our model significantly outperforms the best state-of-the-art methods by 1.9-9.7% in terms of HitRatio@1 on the dataset DBP15K. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： knowledge graph

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CitationAS： A Tool of Automatic Survey Generation Based on Citation Content

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Journal of data and information Science 2018年第2期3卷 20-37页

作者： jie wang chengzhi zhang mengying zhang sanhong deng School of Information Management Nanjing University Nanjing 210023 China Department of Information Management Nanjing University of Science and Technology Nanjing 210094 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing University Nanjing 210023 China

Purpose： This study aims to build an automatic survey generation tool, named CitationAS, based on citation content as represented by the set of citing sentences in the original ***/methodology/approach： Firstly, we apply LDA to analyse topic distribution of citation content. Secondly, in CitationAS, we use bisecting K-means, Lingo and STC to cluster retrieved citation content. Then Word2Vec, Word Net and combination of them are applied to generate cluster labels. Next, we employ TF-IDF, MMR, as well as considering sentence location information, to extract important sentences, which are used to generate surveys. Finally, we adopt manual evaluation for the generated ***： In experiments, we choose 20 high-frequency phrases as search terms. Results show that Lingo-Word2Vec, STC-Word Net and bisecting K-means-Word2Vec have better clustering effects. In 5 points evaluation system, survey quality scores obtained by designing methods are close to 3, indicating surveys are within acceptable limits. When considering sentence location information, survey quality will be improved. Combination of Lingo, Word2Vec, TF-IDF or MMR can acquire higher survey *** limitations： The manual evaluation method may have a certain subjectivity. We use a simple linear function to combine Word2Vec and Word Net that may not bring out their strengths. The generated surveys may not contain some newly created knowledge of some articles which may concentrate on sentences with no *** implications： CitationAS tool can automatically generate a comprehensive, detailed and accurate survey according to user’s search terms. It can also help researchers learn about research status in a certain ***/value： Citaiton AS tool is of practicability. It merges cluster labels from semantic level to improve clustering results. The tool also considers sentence location information when calculating sentence score by TF-IDF and MMR.

关键词： Automatic survey system Citation content Clustering algorithms Labelgeneration approaches Sentence extraction methods

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Layer-Wise Learning Rate Optimization for Task-Dependent Fine-Tuning of Pre-Trained Models: An Evolutionary Approach

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-23页

作者： Bu, Chenyang Liu, Yuxin Huang, Manzong Shao, Jianxuan Ji, Shengwei Luo, Wenjian Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

The superior performance of large-scale pre-Trained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT), has received increasing attention in both academic and industrial research and has become one of the current research hotspots. A pre-Trained model refers to a model trained on large-scale unlabeled data, whose purpose is to learn general language representation or features for fine-Tuning or transfer learning in subsequent tasks. After pre-Training is complete, a small amount of labeled data can be used to fine-Tune the model for a specific task or domain. This two-stage method of "pre-Training+fine-Tuning"has achieved advanced results in natural language processing (NLP) tasks. Despite widespread adoption, existing fixed fine-Tuning schemes that adapt well to one NLP task may perform inconsistently on other NLP tasks given that different tasks have different latent semantic structures. In this article, we explore the effectiveness of automatic fine-Tuning pattern search for layer-wise learning rates from an evolutionary optimization perspective. Our goal is to use evolutionary algorithms to search for better task-dependent fine-Tuning patterns for specific NLP tasks than typical fixed fine-Tuning patterns. Experimental results on two real-world language benchmarks and three advanced pre-Training language models show the effectiveness and generality of the proposed framework. © 2024 held by the owner/author(s).

关键词： Semantics

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Graph-Based Exercise- and knowledge-Aware Learning Network for Student Performance Prediction 1st

Graph-Based Exercise- and Knowledge-Aware Learning Network ...

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Liu, Mengfan Shao, Pengyang Zhang, Kun Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9783030930455

Predicting student performance is a fundamental task in Intelligent Tutoring Systems (ITSs), by which we can learn about students’ knowledge level and provide personalized teaching strategies for them. Researchers have made plenty of efforts on this task. They either leverage educational psychology methods to predict students’ scores according to the learned knowledge proficiency, or make full use of Collaborative Filtering (CF) models to represent latent factors of students and exercises. However, most of these methods either neglect the exercise-specific characteristics (e.g., exercise materials), or cannot fully explore the high-order interactions between students, exercises, as well as knowledge concepts. To this end, we propose a Graph-based Exercise- and knowledge-Aware Learning Network for accurate student score prediction. Specifically, we learn students’ mastery of exercises and knowledge concepts respectively to model the two-fold effects of exercises and knowledge concepts. Then, to model the high-order interactions, we apply graph convolution techniques in the prediction process. Extensive experiments on two real-world datasets prove the effectiveness of our proposed Graph-EKLN. © 2021, Springer Nature Switzerland AG.

关键词： data mining

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