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31st ACM Web Conference (WWW)

作者： Liu, Jiongnan Dou, Zhicheng Zhu, Qiannan Wen, Ji-Rong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China

ISBN: (纸本)9781450390965

Product search has been an important way for people to find products on online shopping platforms. Existing approaches in personalized product search mainly embed user preferences into one single vector. However, this simple strategy easily results in sub-optimal representations, failing to model and disentangle user's multiple preferences. To overcome this problem, we proposed a category-aware multi-interest model to encode users as multiple preference embeddings to represent user-specific interests. Specifically, we also capture the category indications for each preference to indicate the distribution of categories it focuses on, which is derived from rich relations between users, products, and attributes. Based on these category indications, we develop a category attention mechanism to aggregate these various preference embeddings considering current queries and items as the user's comprehensive representation. By this means, we can use this representation to calculate matching scores of retrieved items to determine whether they meet the user's search intent. Besides, we introduce a homogenization regularization term to avoid the redundancy between user interests. Experimental results show that the proposed method significantly outperforms existing approaches.

关键词： personalized product search multi-interest knowledge graph

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Causal Interventional Training for Image Recognition

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IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 1033-1044页

作者： Qin, Wei Zhang, Hanwang Hong, Richang Lim, Ee-Peng Sun, Qianru Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ Hefei 230006 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230088 Peoples R China Nanyang Technol Univ Sch Comp Sci Singapore 639798 Singapore Singapore Management Univ Sch Informat Syst Singapore 178902 Singapore

Deep learning models often fit undesired dataset bias in training. In this paper, we formulate the bias using causal inference, which helps us uncover the ever-elusive causalities among the key factors in training, and thus pursue the desired causal effect without the bias. We start from revisiting the process of building a visual recognition system, and then propose a structural causal model (SCM) for the key variables involved in dataset collection and recognition model: object, common sense, bias, context, and label prediction. Based on the SCM, one can observe that there are "good " and "bad " biases. Intuitively, in the image where a car is driving on a high way in a desert, the "good " bias denoting the common-sense context is the highway, and the "bad " bias accounting for the noisy context factor is the desert. We tackle this problem with a novel causal interventional training (CIT) approach, where we control the observed context in each object class. We offer theoretical justifications for CIT and validate it with extensive classification experiments on CIFAR-10, CIFAR-100 and ImageNet, e.g., surpassing the standard deep neural networks ResNet-34 and ResNet-50, respectively, by 0.95% and 0.70% accuracies on the ImageNet. Our code is open-sourced on the GitHub https://***/qinwei-hfut/CIT.

关键词： Training Automobiles Task analysis Visualization Road transportation Image recognition data models Causality causal intervention deep learning imagenet image recognition

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Impact of the China Railway Express on Employment Structure - Evidence from Urban Economic data 11

Impact of the China Railway Express on Employment Structure ...

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11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Yan, Bochao Jiang, Hailiang Wu, Junfeng Tian, Xin China Agricultural University Beijing100083 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Shanghai WinJoin Information Technology Co. Ltd. Shanghai200126 China

As the main international trade channel of China's "westward development", the opening of China Railway Express has significantly improved the trade facilitation of countries along the route. This paper investigates the impact of the opening of China Railway Express sites on the employment choices of individuals by using the difference in difference method based on the economic panel data of China's prefecture-level cities and the data on the opening of China Railway Express during the period of 2007~2019, which are matched by the correlation. The results of the study show that the opening of the China Railway Express increases the probability of individuals within the radiation area to enter the private sector for employment. The empirical results provide a new explanatory mechanism for the opening of the China Railway Express on economic growth, which has certain theoretical and policy value. © 2024 The Authors.

关键词： Railroads

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Real Estate with AI: An agent based on LangChain 11

Real Estate with AI: An agent based on LangChain

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11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Haurum, Kasper Raupach Ma, Ruiqi Long, Wen Business School Aalborg University Aalborg9220 Denmark Sino-Danish College University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China

Recent developments in large language models (LLMs) have opened new avenues for the real estate industry. These models not only understand language but also function as intelligent agents, engaging with investors through open-ended conversations and influencing their decision-making. Utilizing unstructured data from a professional Danish real estate website, we developed a real estate AI agent in both English and Danish using LangChain and Pinecone. Through testing and evaluation, our agent has demonstrated superior professional and concise outputs compared to other LLMs like Doubao and ChatGPT 4 and shown excellent performance and effectiveness. Our work serves as a reference for AI in real estate investment-related research and proposes new solutions to the "unprofessional foundation " and " expensive consulting fee" problems encountered by ordinary investors in their investment decisions. © 2024 The Authors.

关键词： Chatbots

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A faster stochastic alternating direction method for large scale convex composite problems

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APPLIED INTELLIGENCE 2022年第12期52卷 14233-14245页

作者： Hu, Jia Guo, Tiande Zhao, Tong Univ Chinese Acad Sci Sch Math Sci 19A Yuquan Rd Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management 80 Zhonguancun East Rd Beijing 100190 Peoples R China

Inspired by the fact that certain randomization schemes incorporated into the stochastic (proximal) gradient methods allow for a large reduction in computational time, we incorporate such a scheme into stochastic alternating direction method of multipliers (ADMM), yielding a faster stochastic alternating direction method (FSADM) for solving a class of large scale convex composite problems. In the numerical experiments, we observe a reduction of this method in computational time compared to previous methods. More importantly, we unify the stochastic ADMM for solving general convex and strongly convex composite problems (i.e., the iterative scheme does not change when the the problem goes from strongly convex to general convex). In addition, we establish the convergence rates of FSADM for these two cases.

关键词： Alternating direction method of multipliers Convex composite optimization Stochastic approximation Variance reduction

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Facial depth descend: A generation paradigm for facial depth map

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NEUROCOMPUTING 2021年 466卷 298-310页

作者： Yan, Yao Han, Congying Qin, Jin Chen, Hanqin Guo, Tiande Univ Chinese Acad Sci UCAS Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

Over the past decades, three-dimensional(3D) face recognition has developed rapidly due to its intrinsic invariance to pose and illumination changes. Yet despite this, deep learning is rarely used in 3D face recognition due to lack of training data. Noting that the majority of the recognition processes are based on facial depth map instead of real 3D data, we focus on the generation of facial depth map to surmount the shortage of training data. In this paper, we present a novel paradigm, Facial Depth Descend, which generates facial depth map from existing 3D face data. The key to this generation paradigm is to recombine facial components from existing faces and thereafter generate brand new faces based on them. We then propose a learning framework RPC to generate recognition-friendly faces. First, it extracts three facial components (eyes, nose and mouth) from the 3D data of real faces. Then, with these components as input, a Relative Location Estimator (RLE) is used to predict the relative location among them so that they can be composed in a reasonable manner. Finally, the method feeds a Pix2Pix network with these composed facial components to extrapolate areas surrounding them and output a full facial depth map. We here enforce an identity preserving loss on the generation network to make the facial depth map more discriminative and favorable for recognition tasks. Since we can choose different identities' different components as input, RPC is theoretically able to generate a vast number of novel 2.5D faces. More specifically, RPC can generate an enlarged face dataset as large as N-3 identities, where N is the number of identities in the original one. Experiments show that, adding the generated depth maps to the training dataset can help improve the recognition rate in 2.5D face recognition. (C) 2021 Published by Elsevier B.V.

关键词： Face generation Facial depth map Parameters pre-train

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Comparative research on multi-source heterogeneous data fusion technologies

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Procedia Computer Science 2024年 242卷 1089-1095页

作者： Wen Long Jing Gao Man Guo School of Economics and Management University of Chinese Academy of Sciences Beijing 100090 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Both structured market data and unstructured financial news data can significantly affect stock price fluctuations. Therefore, relying only on a single data source for stock price trend prediction may produce information bias. This article combines these two types of heterogeneous data and systematically compares the effectiveness of different data fusion technologies to establish the optimal data integration strategy, thereby improving the accuracy of stock price prediction. Specifically, this paper innovatively proposes a stock price trend prediction model named MVL-SVM. The model successfully combines multi-perspective learning with support vector machine, and realizes the effective fusion of stock market data and financial news data. It is worth mentioning that the prediction accuracy of MVL-SVM model using direct fusion method is more than 30% higher than that of index modeling method, which indicates that direct fusion method can fully learn effective information from multiple information sources, thus significantly improving the prediction effect.

关键词： Multi-view Learning MVL-SVM Market data Financial news data Heterogeneous data fusion

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A Comparative Study of the Effects of Two Monetary Policy Tools on Money Market Interest Rates 11

A Comparative Study of the Effects of Two Monetary Policy To...

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11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Pan, Shengjie Peng, Shuyang Wan, Jiayu Qu, Yi Zhejiang Mercantile Exchange Co. Ltd. Zhoushan City316000 China School of Economics and Management Southeast University Nanjing211189 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management Nanjing Forestry University Nanjing210037 China

This paper focuses on the regulation effect of money market interest rates from monetary policy tools. By employing Time-Varying Parameter Stochastic Volatility Vector Autoregressive model, we comparatively analyze the effects of the Standing Lending Facility (SLF) and the 7-Day Reverse Repurchase Agreements in the Open Market (RR7D) on the 7-Day Repo Rate of Deposit-taking Financial Institutions (DR007). It is found that SLF can marginally improve DR007 in the short term but can effectively lower it in the medium to long term by acting as the ceiling of the interest rate corridor. RR7D can improve DR007 with a stronger impact, compared to the SLF. The effectiveness of these monetary policy tools remains relatively stable under different socioeconomic backgrounds, achieving the intended objectives of the central bank. Finally, the study proposes suggestions, close integration of the interest rate corridor and open market operations in monetary policy regulation, and encourages the use of SLFs to support the high-quality development of the national economy. © 2024 The Authors.

关键词： Stochastic systems

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OPP-Miner: Order-Preserving Sequential Pattern mining for Time Series

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第5期53卷 3288-3300页

作者： Wu, Youxi Hu, Qian Li, Yan Guo, Lei Zhu, Xingquan Wu, Xindong Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China Hebei Univ Technol Hebei Key Lab Big Data Comp Tianjin 300401 Peoples R China Hebei Univ Technol Sch Econ & Management Tianjin 300401 Peoples R China Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300401 Peoples R China Florida Atlant Univ Dept Comp Elect Engn & Comp Sci Boca Raton FL 33431 USA Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ China Hefei 230009 Peoples R China Mininglamp Acad Sci Mininglamp Technol Beijing 100084 Peoples R China

Traditional sequential pattern mining methods were designed for symbolic sequence. As a collection of measurements in chronological order, a time series needs to be discretized into symbolic sequences, and then users can apply sequential pattern mining methods to discover interesting patterns in time series. The discretization will not only cause the loss of some important information, which partially destroys the continuity of time series, but also ignore the order relations between time-series values. Inspired by order-preserving matching, this article explores a new method called order-preserving sequential pattern (OPP) mining, which does not need to discretize time series into symbolic sequences and represents patterns based on the order relations of time series. An inherent advantage of such representation is that the trend of a time series can be represented by the relative order of the values underneath time series. We propose an OPP-Miner algorithm to mine frequent patterns in time series with the same relative order. OPP-Miner employs the filtration and verification strategies to calculate the support and uses the pattern fusion strategy to generate candidate patterns. To compress the result set, we also study to find the maximal OPPs. Experimental results validate that OPP-Miner is not only efficient but can also discover similar subsequences in time series. In addition, case studies show that our algorithms have high utility in analyzing the COVID-19 epidemic by identifying critical trends and improve the clustering performance. The algorithms and data can be downloaded from https://***/wuc567/Pattern-mining/tree/master/OPP-Miner.

关键词： Time series analysis data mining Pattern matching Market research Symbols Time complexity Clustering algorithms COVID-19 order-preserving relative order sequential pattern mining time series

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Image recognition and empirical application of desert plant species based on convolutional neural network

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Journal of Arid Land 2022年第12期14卷 1440-1455页

作者： LI Jicai SUN Shiding JIANG Haoran TIAN Yingjie XU Xiaoliang School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China Xinjiang Institute of Ecology and Geography Chinese Academy of SciencesUrumqi 830011China

In recent years,deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching *** plant taxonomic identification requires high expertise,which is *** nature reserves have problems such as incomplete species surveys,inaccurate taxonomic identification,and untimely updating of status *** and accurate recognition of plant images can be achieved by applying convolutional neural network technology to explore the best network *** 24 typical desert plant species that are widely distributed in the nature reserves in Xinjiang Uygur Autonomous Region of China as the research objects,this study established an image database and select the optimal network model for the image recognition of desert plant species to provide decision support for fine management in the nature reserves in Xinjiang,such as species investigation and monitoring,by using deep *** desert plant species were not included in the public dataset,the images used in this study were mainly obtained through field shooting and downloaded from the Plant Photo Bank of China(PPBC).After the sorting process and statistical analysis,a total of 2331 plant images were finally collected(2071 images from field collection and 260 images from the PPBC),including 24 plant species belonging to 14 families and 22 genera.A large number of numerical experiments were also carried out to compare a series of 37 convolutional neural network models with good performance,from different perspectives,to find the optimal network model that is most suitable for the image recognition of desert plant species in *** results revealed 24 models with a recognition Accuracy,of greater than 70.000%.Among which,Residual Network X_8GF(RegNetX_8GF)performs the best,with Accuracy,Precision,Recall,and F1(which refers to the harmonic mean of the Precision and Recall values)values of 78.33%,77.65%,69.55%,and 71.26%,*** t

关键词： desert plants image recognition deep learning convolutional neural network Residual Network X_8GF(RegNetX_8GF) Mobile Network V2(MobileNetV2) nature reserves

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