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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Sun, Fei Guo, Jiafeng Lan, Yanyan Xu, Jun Xueqi, Cheng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

Recently, Word2Vec tool has attracted a lot of interest for its promising performances in a variety of natural language processing (NLP) tasks. However, a critical issue is that the dense word representations learned in Word2Vec are lacking of interpretability. It is natural to ask if one could improve their interpretability while keeping their performances. Inspired by the success of sparse models in enhancing interpretability, we propose to introduce sparse constraint into Word2Vec. Specifically, we take the Continuous Bag of Words (CBOW) model as an example in our study and add the l regularizer into its learning objective. One challenge of optimization lies in that stochastic gradient descent (SGD) cannot directly produce sparse solutions with l regularizer in online training. To solve this problem, we employ the Regularized Dual Averaging (RDA) method, an online optimization algorithm for regularized stochastic learning. In this way, the learning process is very efficient and our model can scale up to very large corpus to derive sparse word representations. The proposed model is evaluated on both expressive power and interpretability. The results show that, compared with the original CBOW model, the proposed model can obtain state-of-the-art results with better interpretability using less than 10% non-zero elements.

关键词： Optimization

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Multi-task representation learning for demographic prediction 38th

Multi-task representation learning for demographic predictio...

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38th European Conference on Information Retrieval Research, ECIR 2016

作者： Wang, Pengfei Guo, Jiafeng Lan, Yanyan Xu, Jun Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Beijing China

ISBN: (纸本)9783319306704

Demographic attributes are important resources for market analysis, which are widely used to characterize different types of users. However, such signals are only available for a small fraction of users due to the difficulty in manual collection process by retailers. Most previous work on this problem explores different types of features and usually predicts different attributes independently. However, manually defined features require professional knowledge and often suffer from under specification. Meanwhile, modeling the tasks separately may lose the ability to leverage the correlations among different attributes. In this paper, we propose a novel Multi-task Representation Learning (MTRL) model to predict users’ demographic attributes. Comparing with the previous methods, our model conveys the following merits: (1) By using a multitask approach to learn the tasks, our model leverages the large amounts of cross-task data, which is helpful to the task with limited data;(2) MTRL uses a supervised way to learn the shared semantic representation across multiple tasks, thus it can obtain a more general and robust representation by considering the constraints among tasks. Experiments are conducted on a real-world retail dataset where three attributes (gender, marital status, and education background) are predicted. The empirical results show that our MTRL model can improve the performance significantly compared with the state-of-the-art baselines. © Springer International Publishing Switzerland 2016.

关键词： Semantics

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An ensemble method for job recommender systems 10

An ensemble method for job recommender systems

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10th ACM Conference on Recommender Systems Challenge, RecSys Challenge 2016

作者： Zhang, Chenrui Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781450348010

In this paper, we present an ensemble method for job recommendation to ACM RecSys Challenge 2016. Given a user, the goal of a job recommendation system is to predict those job postings that are likely to be relevant to the user1. Firstly, we analyze the train dataset and find several interesting patterns. Secondly, we describe our solution, which is an ensemble of two filters, combining the merits of traditional collaborative filtering and content-based filtering. Our approach finally achieved a score of 1632828.82, ranked at the 10th place on the public leaderboard. © 2016 ACM.

关键词： Collaborative filtering

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Dynamic-K recommendation with personalized decision boundary 23rd

Dynamic-K recommendation with personalized decision boundary

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23rd China conference on Information Retrieval, CCIR 2017

作者： Gao, Yan Guo, Jiafeng Lan, Yanyan Liao, Huaming CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783319686981

In this paper, we investigate the recommendation task in the most common scenario with implicit feedback (e.g., clicks, purchases). State-of-the-art methods in this direction usually cast the problem as to learn a personalized ranking on a set of items (e.g., webpages, products). The top-N results are then provided to users as recommendations, where the N is usually a fixed number pre-defined by the system according to some heuristic criteria (e.g., page size, screen size). There is one major assumption underlying this fixed-number recommendation scheme, i.e., there are always sufficient relevant items to users’ preferences. Unfortunately, this assumption may not always hold in real-world scenarios. In some applications, there might be very limited candidate items to recommend, and some users may have very high relevance requirement in recommendation. In this way, even the top-1 ranked item may not be relevant to a user’s preference. Therefore, we argue that it is critical to provide a dynamic-K recommendation, where the K should be different with respect to the candidate item set and the target user. We formulate this dynamic-K recommendation task as a joint learning problem with both ranking and classification objectives. The ranking objective is the same as existing methods, i.e., to create a ranking list of items according to users’ interests. The classification objective is unique in this work, which aims to learn a personalized decision boundary to differentiate the relevant items from irrelevant items. Based on these ideas, we extend two state-of-the-art ranking-based recommendation methods, i.e., BPRMF and HRM, to the corresponding dynamic-K versions, namely DK-BPRMF and DK-HRM. Our experimental results on two datasets show that the dynamic-K models are more effective than the original fixed-N recommendation methods. © Springer International Publishing AG 2017.

关键词： Websites

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Trusted Clustering Based Federated Learning in Edge networks

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IEEE Transactions on Mobile computing 2025年

作者： Liu, Yi-Jing Zhang, Long Li, Xiaoqian Du, Hongyang Feng, Gang Qin, Shuang Wang, Jiacheng University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China Tsinghua University Sichuan Energy Internet Research Institute Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Nanyang Technological University College of Computing and Data Science Singapore

Federated learning (FL) is integral to advancing edge intelligence by enabling collaborative machine learning. In FL-empowered edge networks, computing nodes first train local models and then send them to an or multiple aggregation node(s) for global model collaboration. However, the trustworthiness of both local and global models in conventional FL frameworks is compromised due to inadequate model security and transparency. Distributed ledger technique (DLT) can address this issue by leveraging multi-nodes trust capabilities to support distributed consensus. However, model training and consensus performance of DLT may significantly degrade due to instability and resource constraints of edge networks. Sharding technique provides an effective approach by dividing the ledger into smaller and manageable shards. In this paper, to improve model training and consensus performance, we propose a trusted FL framework by incorporating sharding DLT into FL frameworks. We construct a theoretical model to investigate the relationship between model training performance, consensus efficiency, and capacity of edge nodes regarding storage, computing and communications. Based on the theoretical model, we propose a trusted clustering scheme to aggregate local models. Numerical results show that our proposed scheme significantly improves network throughput for transmitting models while guaranteeing model learning performance in comparison with some classical baselines. © 2002-2012 IEEE.

关键词： Distributed ledger

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Neural or statistical: An empirical study on language models for Chinese input recommendation on mobile 23rd

Neural or statistical: An empirical study on language models...

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23rd China conference on Information Retrieval, CCIR 2017

作者： Zhang, Hainan Lan, Yanyan Guo, Jiafeng Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783319686981

Chinese input recommendation plays an important role in alleviating human cost in typing Chinese words, especially in the scenario of mobile applications. The fundamental problem is to predict the conditional probability of the next word given the sequence of previous words. Therefore, statistical language models, i.e.n-grams based models, have been extensively used on this task in real application. However, the characteristics of extremely different typing behaviors usually lead to serious sparsity problem, even n-gram with smoothing will fail. A reasonable approach to tackle this problem is to use the recently proposed neural models, such as probabilistic neural language model, recurrent neural network and word2vec. They can leverage more semantically similar words for estimating the probability. However, there is no conclusion on which approach of the two will work better in real application. In this paper, we conduct an extensive empirical study to show the differences between statistical and neural language models. The experimental results show that the two different approach have individual advantages, and a hybrid approach will bring a significant improvement. © Springer International Publishing AG 2017.

关键词： Recurrent neural networks

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Beyond Precision: A Study on Recall of Initial Retrieval with Neural Representations 1

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28th China Conference on Information Retrieval, CCIR 2022

作者： Xiao, Yan Fan, Yixing Zhang, Ruqing Guo, Jiafeng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (数字)9783031247552

ISBN: (纸本)9783031247545

Vocabulary mismatch is a central problem in information retrieval (IR), i.e., the relevant documents may not contain the same (symbolic) terms of the query. Recently, neural representations have shown great success in capturing semantic relatedness, leading to new possibilities to alleviate the vocabulary mismatch problem in IR. However, most existing efforts in this direction have been devoted to the re-ranking stage. That is to leverage neural representations to help re-rank a set of candidate documents, which are typically obtained from an initial retrieval stage based on some symbolic index and search scheme (e.g., BM25 over the inverted index). This naturally raises a question: if the relevant documents have not been found in the initial retrieval stage due to vocabulary mismatch, there would be no chance to re-rank them to the top positions later. Therefore, in this paper, we study the problem how to employ neural representations to improve the recall of relevant documents in the initial retrieval stage. Specifically, to meet the efficiency requirement of the initial stage, we introduce a neural index for the neural representations of documents, and propose two hybrid search schemes based on both neural and symbolic indices, namely the parallel search scheme and the sequential search scheme. Our experiments show that both hybrid index and search schemes can improve the recall of the initial retrieval stage with small overhead. © 2023, Springer Nature Switzerland AG.

关键词： Semantics

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Academic access data analysis for literature recommendation 23rd

Academic access data analysis for literature recommendation

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23rd China conference on Information Retrieval, CCIR 2017

作者： Fan, Yixing Guo, Jiafeng Lan, Yanyan Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783319686981

Academic reading plays an important role in researchers’ daily life. To alleviate the burden of seeking relevant literature from rapidly growing academic repository, different kinds of recommender systems have been introduced in recent years. However, most existing work focused on adopting traditional recommendation techniques, like content-based filtering or collaborative filtering, in the literature recommendation scenario. Little work has yet been done on analyzing the academic reading behaviors to understand the reading patterns and information needs of real-world academic users, which would be a foundation for improving existing recommender systems or designing new ones. In this paper, we aim to tackle this problem by carrying out empirical analysis over large scale academic access data, which can be viewed as a proxy of academic reading behaviors. We conduct global, group-based and sequence-based analysis to address the following questions: (1) Are there any regularities in users’ academic reading behaviors? (2) Will users with different levels of activeness exhibit different information needs? (3) How to correlate one’s future demands with his/her historical behaviors? By answering these questions, we not only unveil useful patterns and strategies for literature recommendation, but also identify some challenging problems for future development. © Springer International Publishing AG 2017.

关键词： Recommender systems

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Understanding and Improving Neural Ranking Models from a Term Dependence View 15th

Understanding and Improving Neural Ranking Models from a Ter...

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15th Asia Information Retrieval Societies Conference, AIRS 2019

作者： Fan, Yixing Guo, Jiafeng Lan, Yanyan Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783030428341

Recently, neural information retrieval (NeuIR) has attracted a lot of interests, where a variety of neural models have been proposed for the core ranking problem. Beyond the continuous refresh of the state-of-the-art neural ranking performance, the community calls for more analysis and understanding of the emerging neural ranking models. In this paper, we attempt to analyze these new models from a traditional view, namely term dependence. Without loss of generality, most existing neural ranking models could be categorized into three categories with respect to their underlying assumption on query term dependence, i.e., independent models, dependent models, and hybrid models. We conduct rigorous empirical experiments over several representative models from these three categories on a benchmark dataset and a large click-through dataset. Interestingly, we find that no single type of model can achieve a consistent win over others on different search queries. An oracle model which can select the right model for each query can obtain significant performance improvement. Based on the analysis we introduce an adaptive strategy for neural ranking models. We hypothesize that the term dependence in a query could be measured through the divergence between its independent and dependent representations. We thus propose a dependence gate based on such divergence representation to softly select neural ranking models for each query accordingly. Experimental results verify the effectiveness of the adaptive strategy. © Springer Nature Switzerland AG 2020.

关键词： Large dataset

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RobustPFL: Robust Personalized Federated Learning

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Chen, Guorong Wang, Wei Wu, Yufang Li, Chao Xu, Guangquan Ji, Shouling Li, Tao Shen, Meng Han, Yufei Xi'an Jiaotong University Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an China Beijing Jiaotong University 3 Shangyuancun Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Tianjin University Tianjin Key Laboratory of Advanced Networking College of Intelligence and Computing Tianjin300350 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China ITAI Haihe Lab Tianjin China Nankai University College of Computer Science Tianjin300350 China Beijing Institute of Technology School of Cyberspace Security Beijing100081 China Peng Cheng Laboratory Cyberspace Security Research Center Shenzhen518066 China INRIA Bretagne Rennes35042 France

Conventional federated learning (FL) coordinated by a central server focuses on training a global model and protecting the privacy of clients' training data by storing it locally. However, the statistical heterogeneity hinders the global model from adapting to the non-IID distributions among clients. Moreover, untrusted and unreliable central servers and malicious clients may compromise model integrity and availability, thus degrading the robustness of FL. To address these challenges, we present RobustPFL, a decentralized personalized federated learning (PFL) approach that combines α-based Layer-position Normalized Similarity (α-LNS) and local collaborative training to improve personalized performance while utilizing a blockchain-based committee mechanism to coordinate the aggregation process, thereby achieving high personalized accuracy and robustness. Extensive experiments show that our RobustPFL approach outperforms multiple algorithms, including Local training, FedAvg, FedReptile, Per-FedAvg, FedBN, and SPFL, on MNIST, CIFAR10, EMNIST, and N-BaIoT datasets in four non-IID settings. We also evaluate RobustPFL's effectiveness against attacks - poisoning attacks and free-riding attacks. Particularly, for three prevalent poisoning attacks (backdoor, label flipping, and model poisoning attacks), we compare non-defensive (FedAvg) and defensive (Krum, trimmed mean, Bulyan, FedBN, FLAME, and FangTrmean) methods with our proposed RobustPFL. The results show that our approach achieves significant defensive effects. © 2004-2012 IEEE.

关键词： Federated learning

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