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arXiv 2022年

作者： Wan, Changlin Shi, Zhongzhi Huizhou University Guangdong516007 China CAS Key Lab. of IIP Institute of Computing Technology Beijing100190 China

Function plays an important role in mathematics and many science branches. As the fast development of computer technology, more and more study on computational function analysis, e.g., Fast Fourier Transform, Wavelet Transform, Curve Function, are presented in these years. However, there are two main problems in these approaches: 1) hard to handle the complex functions of stationary and non-stationary, periodic and non-periodic, high order and low order;2) hard to generalize the fitting functions from training data to test data. In this paper, a multiple regression based function fitting network that solves the two main problems is introduced as a predicable function fitting technique. This technique constructs the network includes three main parts: 1) the stationary transform layer, 2) the feature encoding layers, and 3) the fine tuning regression layer. The stationary transform layer recognizes the order of input function data, and transforms nonstationary function to stationary function. The feature encoding layers encode the raw input sequential data to a novel linear regression feature that can capture both the structural and the temporal characters of the sequential data. The fine tuning regression layer then fits the features to the target ahead values. The fitting network with the linear regression feature layers and a non-linear regression layer come up with high quality fitting results and generalizable predictions. The experiments of both mathematic function examples and the real word function examples verifies the efficiency of the proposed technique. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear regression

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Long- and short-term self-attention network for sequential recommendation

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NEUROCOMPUTING 2021年 423卷 580-589页

作者： Xu, Chengfeng Feng, Jian Zhao, Pengpeng Zhuang, Fuzhen Wang, Deqing Liu, Yanchi Sheng, Victor S. Soochow Univ Sch Comp Sci & Technol Inst AI Suzhou Peoples R China Inst Comp Technol Key Lab IIP CAS Beijing Peoples R China Beihang Univ Sch Comp Sci Beijing Peoples R China NEC Labs Amer Irving TX USA Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

With great value in real applications, sequential recommendation aims to recommend users the personalized sequential actions. To achieve better performance, it is essential to consider both long-term preferences and sequential patterns (i:e., short-term dynamics). Compared to widely used Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), Self-Attention Network (SAN) obtains a surge of interest due to fewer parameters, highly parallelizable computation, and flexibility in modeling dependencies. However, existing SAN-based models are inadequate in characterizing and distinguishing users' long-term preferences and short-term demands since they do not emphasize the importance of the current interest and temporal order information of sequences. In this paper, we propose a novel multi-layer longand short-term self-attention network (LSSA) for sequential recommendation. Specifically, we first split the entire sequence of a user into multiple sub-sequences according to the timespan. Then the first self-attention layer learns the user's short-term dynamics based on the last sub-sequence, while the second one captures the user's long-term preferences through the previous sub-sequences and the last one. Finally, we integrate the longand short-term representations together to form the user's final hybrid representation. We evaluate the proposed model on three real-world datasets, and our experimental results show that LSSA outperforms state-of-the-art methods with a wide margin. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Self-attention network Sequential recommendation User preference

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Community-Aware Multi-Task Transportation Demand Prediction 35

Community-Aware Multi-Task Transportation Demand Prediction

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35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence

作者： Liu, Hao Wu, Qiyu Zhuang, Fuzhen Lu, Xinjiang Dou, Dejing Xiong, Hui Baidu Res Beijing Peoples R China Peking Univ Beijing Peoples R China Chinese Acad Sci Key Lab IIP ICT CAS Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Rutgers State Univ New Brunswick NJ USA

ISBN: (纸本)9781577358664

Transportation demand prediction is of great importance to urban governance and has become an essential function in many online applications. While many efforts have been made for regional transportation demand prediction, predicting the diversified transportation demand for different communities (e.g., the aged, the juveniles) remains an unexplored problem. However, this task is challenging because of the joint influence of spatio-temporal correlation among regions and implicit correlation among different communities. To this end, in this paper, we propose the Multi-task Spatio-Temporal Network with Mutually-supervised Adaptive task grouping (Ada-MSTNet) for community-aware transportation demand prediction. Specifically, we first construct a sequence of multi-view graphs from both spatial and community perspectives, and devise a spatio-temporal neural network to simultaneously capture the sophisticated correlations between regions and communities, respectively. Then, we propose an adaptively clustered multi-task learning module, where the prediction of each region-community specific transportation demand is regarded as distinct task. Moreover, a mutually supervised adaptive task grouping strategy is introduced to softly cluster each task into different task groups, by leveraging the supervision signal from one another graph view. In such a way, Ada-MSTNet is not only able to share common knowledge among highly related communities and regions, but also shield the noise from unrelated tasks in an end-to-end fashion. Finally, extensive experiments on two real-world datasets demonstrate the effectiveness of our approach compared with seven baselines.

关键词： Forecasting

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Collaborative self-attention network for session-based recommendation 29

Collaborative self-attention network for session-based recom...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Luo, Anjing Zhao, Pengpeng Liu, Yanchi Zhuang, Fuzhen Wang, Deqing Xu, Jiajie Fang, Junhua Sheng, Victor S. Institute of AI School of Computer Science and Technology Soochow University China Rutgers University New Jersey United States Key Lab of IIP of CAS Institute of Computing Technology Beijing China University of Chinese Academy of Sciences Beijing China School of Computer Beihang University Beijing China Texas Tech University TX United States

ISBN: (纸本)9780999241165

Session-based recommendation becomes a research hotspot for its ability to make recommendations for anonymous users. However, existing session-based methods have the following limitations: (1) They either lack the capability to learn complex dependencies or focus mostly on the current session without explicitly considering collaborative information. (2) They assume that the representation of an item is static and fixed for all users at each time step. We argue that even the same item can be represented differently for different users at the same time step. To this end, we propose a novel solution, Collaborative Self-Attention Network (CoSAN) for session-based recommendation, to learn the session representation and predict the intent of the current session by investigating neighborhood sessions. Specially, we first devise a collaborative item representation by aggregating the embedding of neighborhood sessions retrieved according to each item in the current session. Then, we apply self-attention to learn long-range dependencies between collaborative items and generate collaborative session representation. Finally, each session is represented by concatenating the collaborative session representation and the embedding of the current session. Extensive experiments on two real-world datasets show that CoSAN constantly outperforms state-of-the-art methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Embeddings

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Vector-Level and Bit-Level Feature Adjusted Factorization Machine for Sparse Prediction 25th

Vector-Level and Bit-Level Feature Adjusted Factorization Ma...

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25th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Wu, Yanghong Zhao, Pengpeng Liu, Yanchi Sheng, Victor S. Fang, Junhua Zhuang, Fuzhen Soochow Univ Inst Artificial Intelligence Sch Comp Sci & Technol Suzhou Peoples R China NEC Labs Amer Princeton NJ USA Texas Tech Univ Dept Comp Sci Lubbock TX USA Inst Comp Technol Key Lab IIP CAS Beijing Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9783030594091;9783030594107

Factorization Machines (FMs) are a series of effective solutions for sparse data prediction by considering the interactions among users, items, and auxiliary information. However, the feature representations in most state-of-the-art FMs are fixed, which reduces the prediction performance as the same feature may have unequal predictabilities under different input instances. In this paper, we propose a novel Feature-adjusted Factorization Machine (FaFM) model by adaptively adjusting the feature vector representations from both vector-level and bit-level. Specifically, we adopt a fully connected layer to adaptively learn the weight of vector-level feature adjustment. And a user-item specific gate is designed to refine the vector in bit-level and to filter noises caused by over-adaptation of the input instance. Extensive experiments on two real-world datasets demonstrate the effectiveness of FaFM. Empirical results indicate that FaFM significantly outperforms the traditional FM with a 10.89% relative improvement in terms of Root Mean Square Error (RMSE) and consistently exceeds four state-of-the-art deep learning based models.

关键词： Sparse prediction Factorization machines Feature adjustment

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A Study on Big Knowledge and Its Engineering Issues

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2019年第9期31卷 1630-1644页

作者： Lu, Ruqian Jin, Xiaolong Zhang, Songmao Qiu, Meikang Wu, Xindong Chinese Acad Sci Key Lab MADIS AMSS Inst Math Beijing 100190 Peoples R China Chinese Acad Sci Key Lab IIP Beijing 100190 Peoples R China Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci & Technol Beijing 100864 Peoples R China Univ CAS Sch Comp & Control Engn Beijing 100190 Peoples R China Columbia Univ Dept Elect Engn New York NY 10027 USA Hefei Univ Technol Res Inst Big Knowledge Hefei 230009 Anhui Peoples R China Univ Louisiana Lafayette Sch Comp & Informat Lafayette LA 70504 USA

After entering the big data era, a new term of 'big knowledge' has been coined to deal with challenges in mining a mass of knowledge from big data. While researchers used to explore the basic characteristics of big data, we have not seen any studies on the general and essential properties of big knowledge. To fill this gap, this paper studies the concepts of big knowledge, big-knowledge system, and big-knowledge engineering. Ten massiveness characteristics for big knowledge and big-knowledge systems, including massive concepts, connectedness, clean data resources, cases, confidence, capabilities, cumulativeness, concerns, consistency, and completeness, are defined and explored. Based on these characteristics, a comprehensive investigation is conducted on some large-scale knowledge engineering projects, including the Fifth Comprehensive Traffic Survey in Shanghai, the China's Xia-Shang-Zhou Chronology Project, the Troy and Trojan War Project, and the International Human Genome Project, as well as the online free encyclopedia Wikipedia. We also investigate the recent research efforts on knowledge graphs, where they are analyzed to determine which ones can be considered as big knowledge and big-knowledge systems. Further, a definition of big-knowledge engineering and its life cycle paradigm is presented. All of these projects are accordingly checked to determine whether they belong to big-knowledge engineering projects. Finally, the perspectives of big knowledge research are discussed.

关键词： Big data knowledge engineering big data knowledge engineering big knowledge massiveness characteristics big-knowledge system big-knowledge engineering life cycle

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Neural Network based Continuous Conditional Random Field for Fine-grained Crime Prediction 28

Neural Network based Continuous Conditional Random Field for...

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28th International Joint Conference on Artificial Intelligence

作者： Yi, Fei Yu, Zhiwen Zhuang, Fuzhen Guo, Bin Northwestern Polytech Univ Xian Shaanxi Peoples R China Chinese Acad Sci Key Lab IIP CAS Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Baidu Inc Baidu Res Business Intelligence Lab Beijing Peoples R China

ISBN: (纸本)9780999241141

Crime prediction has always been a crucial issue for public safety, and recent works have shown the effectiveness of taking spatial correlation, such as region similarity or interaction, for fine-grained crime modeling. In our work, we seek to reveal the relationship across regions for crime prediction using Continuous Conditional Random Field (CCRF). However, conventional CCRF would become impractical when facing a dense graph considering all relationship between regions. To deal with it, in this paper, we propose a Neural Network based CCRF (NN-CCRF) model that formulates CCRF into an end-to-end neural network framework, which could reduce the complexity in model training and improve the overall performance. We integrate CCRF with NN by introducing a Long Short-Term Memory (LSTM) component to learn the non-linear mapping from inputs to outputs of each region, and a modified Stacked Denoising AutoEncoder (SDAE) component for pairwise interactions modeling between regions. Experiments conducted on two different real-world datasets demonstrate the superiority of our proposed model over the state-of-the-art methods.

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Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation 33

Where to Go Next: A Spatio-Temporal Gated Network for Next P...

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33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence

作者： Zhao, Pengpeng Zhu, Haifeng Liu, Yanchi Xu, Jiajie Li, Zhixu Zhuang, Fuzhen Sheng, Victor S. Zhou, Xiaofang Soochow Univ Sch Comp Sci & Technol Inst Artificial Intelligence Suzhou Peoples R China Rutgers State Univ New Brunswick NJ USA Chinese Acad Sci ICT Key Lab IIP Beijing Peoples R China Univ Cent Arkansas Conway AR USA Univ Queensland Brisbane Qld Australia Univ CAS Beijing Peoples R China

ISBN: (纸本)9781577358091

Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. However, the state-of-the-art Recurrent Neural Networks (RNNs) rarely consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling user check-in behaviors in next POI recommendation. To this end, in this paper, we propose a new Spatio-Temporal Gated Network (STGN) by enhancing long-short term memory network, where spatio-temporal gates are introduced to capture the spatio-temporal relationships between successive checkins. Specifically, two pairs of time gate and distance gate are designed to control the short-term interest and the long-term interest updates, respectively. Moreover, we introduce coupled input and forget gates to reduce the number of parameters and further improve efficiency. Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks. The experimental results show that our model significantly outperforms the state-of-the-art approaches for next POI recommendation.

关键词： Location based services

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label-Sensitive Task Grouping by Bayesian Nonparametric Approach for Multi-Task Multi-label Learning 27

Label-Sensitive Task Grouping by Bayesian Nonparametric Appr...

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27th International Joint Conference on Artificial Intelligence (IJCAI)

作者： Zhang, Xiao Li, Wenzhong Nguyen, Vu Zhuang, Fuzhen Xiong, Hui Lu, Sanglu Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Deakin Univ Ctr Pattern Recognit & Data Analyt Geelong Vic Australia CAS Beijing Key Lab IIP CAS Inst Comp Technol Beijing Peoples R China Rutgers State Univ Management Sci & Informat Syst New Brunswick NJ USA Univs Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9780999241127

Multi-label learning is widely applied in many real-world applications, such as image and gene annotation. While most of the existing multi-label learning models focus on the single-task learning problem, there are always some tasks that share some commonalities, which can help each other to improve the learning performances if the knowledge in the similar tasks can be smartly shared. In this paper, we propose a label-sensitive TAsk Grouping framework, named labTAG, based on Bayesian nonparametric approach for multi-task multi-label classification. The proposed framework explores the label correlations to capture feature-label patterns, and clusters similar tasks into groups with shared knowledge, which are learned jointly to produce a strengthened multi-task multi-label model. We evaluate the model performance on three public multi-task multi-label data sets, and the results show that labTAG outperforms the compared baselines with a significant margin.

关键词： Learning systems

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Sequential Recommender System based on Hierarchical Attention Network 27

Sequential Recommender System based on Hierarchical Attentio...

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27th International Joint Conference on Artificial Intelligence (IJCAI)

作者： Ying, Haochao Zhuang, Fuzhen Zhang, Fuzheng Liu, Yanchi Xu, Guandong Xie, Xing Xiong, Hui Wu, Jian Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China CAS Beijing Inst Comp Technol CAS Key Lab IIP Beijing Peoples R China Microsoft Res Beijing Peoples R China Univ Technol Adv Analyt Inst Sydney NSW Australia Rutgers State Univ Management Sci & Informat Syst Piscataway NJ USA Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9780999241127

With a large amount of user activity data accumulated, it is crucial to exploit user sequential behavior for sequential recommendations. Conventionally, user general taste and recent demand are combined to promote recommendation performances. However, existing methods often neglect that user long-term preference keep evolving over time, and building a static representation for user general taste may not adequately reflect the dynamic characters. Moreover, they integrate user-item or item-item interactions through a linear way which limits the capability of model. To this end, in this paper, we propose a novel two-layer hierarchical attention network, which takes the above properties into account, to recommend the next item user might be interested. Specifically, the first attention layer learns user long-term preferences based on the historical purchased item representation, while the second one outputs final user representation through coupling user long-term and short-term preferences. The experimental study demonstrates the superiority of our method compared with other state-of-the-art ones.

关键词： Network layers

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