OpenRaiser/Intern-Atlas huggingface dataset:
Simulating student interactions with two-stage imitation learning for intelligent educational systems
Authors: Guanhao Zhao, Zhenya Huang, Yan Zhuang, Jiayu Liu, Qi Liu, Zhiding Liu, Jinze Wu, Enhong Chen
Venue: Conference CIKM 2023
Year: 2023

The fundamental task of intelligent educational systems is to offer adaptive learning services to students, such as exercise recommendations and computerized adaptive testing. However, optimizing required models in these systems would always encounter the collection difficulty of high-quality interaction data in practice. Therefore, establishing a student simulator is of great value since it can generate valid interactions to help optimize models. Existing advances have achieved success but generally suffer from exposure bias and overlook long-term intentions. To tackle these problems, we propose a novel Direct-Adversarial Imitation Student Simulator (DAISim) by formulating it as a Markov Decision Process (MDP), which unifies the workflow of the simulator in training and generating to alleviate the exposure bias and single-step optimization problems. To construct the intentions underlying the complex student interactions, we first propose a direct imitation strategy to mimic the interactions with a simple reward function. Then, we propose an adversarial imitation strategy to learn a rational distribution with the reward given by a parameterized discriminator. Furthermore, we optimize the discriminator in adversarial imitation in a pairwise manner, and the theoretical analysis shows that the pairwise discriminator would improve the generation quality. We conduct extensive experiments on real-world datasets, where the results demonstrate that our DAISim can simulate high-quality student interactions whose distribution is close to real distribution and can promote several downstream services.
Stereo matching, reconstruction and refinement of 3D curves using deformable contours
Authors: Benedicte Bascle, Rachid Deriche
Venue: Conference ICCV 1993
Year: 1993

The authors propose to replace a set of connected 3-D points or line segments with a 3-D formable curve, using weighted least-squares approximation. The parameters of the 3-D energy-minimizing curve are updated so that its 2-D projections on two or three stereoscopic images converge toward the corresponding image edges. Thus, a more realistic representation of the real non-polygonal world is obtained. The case where no initial 3-D information is provided is also considered. A novel approach is proposed to match a given 2-D curve in the first image to its corresponding position in the second image. The curve is tracked between the two images by a deformable curve constrained by a 2-D motion model. The position of the 2-D curve in the second image is then refined by relaxing the motion constraint. As a result, the correspondences between the curves of both images are known. An initial estimate of the 3-D curve can be recovered using the calibration parameters of the stereoscopic system. These approaches are illustrated by experimental results.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Robust User Behavioral Sequence Representation via Multi-scale Stochastic Distribution Prediction
Authors: Chilin Fu, Weichang Wu, Xiaolu Zhang, Jun Hu, Jing Wang, Jun Zhou
Venue: Conference CIKM 2023
Year: 2023

User behavior representation learned by self-supervised pre-training tasks is widely used in various domains and applications. Conventional methods usually follow the methodology in Natural Language Processing (NLP) to set the pre-training tasks. They either randomly mask some of the behaviors in the sequence and predict the masked ones or predict the next k behaviors. These methods fit for text sequence, in which the tokens are sequentially arranged subject to linguistic criterion. However, the user behavior sequences can be stochastic with noise and randomness. The same paradigm is intractable for learning a robust user behavioral representation. Though the next user behavior can be stochastic, the behavior distribution over a period of time is much more stable and less noisy. Based on this, we propose a Multi-scale Stochastic Distribution Prediction (MSDP) algorithm for learning robust user behavioral sequence representation. Instead of using predictions on concrete behavior as pre-training tasks, we take the prediction on user's behaviors distribution over a period of time as the self-supervision signal. Moreover, inspired by the recent success of the multi-task prompt training method on Large Language Models (LLM), we propose using the window size of the predicted time period as a prompt, enabling the model to learn user behavior representations that can be applied to prediction tasks across various future time periods. We generate different window size prompts through stochastic sampling. It effectively improves the generalization capability of the learned sequence representation. Extensive experiments demonstrate that our approach can learn robust user behavior representation successfully, which significantly outperforms state-of-the-art (SOTA) baselines.
VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced Detection
Authors: Fei Xiao 0015, Shaofeng Cai, Gang Chen 0001, H. V. Jagadish, Beng Chin Ooi, Meihui Zhang 0001
Venue: Conference KDD 2024
Year: 2024

Fraud detection presents a challenging task characterized by ever-evolving fraud patterns and scarce labeled data. Existing methods predominantly rely on graph-based or sequence-based approaches. While graph-based approaches connect users through shared entities to capture structural information, they remain vulnerable to fraudsters who can disrupt or manipulate these connections. In contrast, sequence-based approaches analyze users' behavioral patterns, offering robustness against tampering but overlooking the interactions between similar users. Inspired by cohort analysis in retention and healthcare, this paper introduces VecAug, a novel cohort-augmented learning framework that addresses these challenges by enhancing the representation learning of target users with personalized cohort information. To this end, we first propose a vector burn-in technique for automatic cohort identification, which retrieves a task-specific cohort for each target user. Then, to fully exploit the cohort information, we introduce an attentive cohort aggregation technique for augmenting target user representations. To improve the robustness of such cohort augmentation, we also propose a novel label-aware cohort neighbor separation mechanism to distance negative cohort neighbors and calibrate the aggregated cohort information. By integrating this cohort information with target user representations, VecAug enhances the modeling capacity and generalization capabilities of the model to be augmented. Our framework is flexible and can be seamlessly integrated with existing fraud detection models. We deploy our framework on e-commerce platforms and evaluate it on three fraud detection datasets, and results show that VecAug improves the detection performance of base models by up to 2.48\% in AUC and 22.5\% in R@P$_{0.9}$, outperforming state-of-the-art methods significantly.
Managing bias in AI
Authors: Drew S. Roselli, Jeanna N. Matthews, Nisha Talagala
Venue: Conference WWW 2019
Year: 2019

Recent awareness of the impacts of bias in AI algorithms raises the risk for companies to deploy such algorithms, especially because the algorithms may not be explainable in the same way that non-AI algorithms are. Even with careful review of the algorithms and data sets, it may not be possible to delete all unwanted bias, particularly because AI systems learn from historical data, which encodes historical biases. In this paper, we propose a set of processes that companies can use to mitigate and manage three general classes of bias: those related to mapping the business intent into the AI implementation, those that arise due to the distribution of samples used for training, and those that are present in individual input samples. While there may be no simple or complete solution to this issue, best practices can be used to reduce the effects of bias on algorithmic outcomes.
Link prediction based on generalized cluster information
Authors: Jungeun Kim, Minsoo Choy, Daehoon Kim, U Kang
Venue: Conference WWW 2014
Year: 2014

Understanding of which new interactions among data objects are likely to occur in the future is crucial for a deeper understanding of network dynamics and evolution. This question is largely unexplored except a local neighborhood perspective, partly owing to the difficulty in finding major factors which heavily affect the link prediction problem. In this paper, we propose LPCSP, a novel link prediction method which exploits the generalized cluster information containing cluster relations and cluster evolution information. Experiments show that our proposed LPCSP is accurate, scalable, and useful for link prediction on real world graphs.
Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning
Authors: Man Wu, Shirui Pan, Lan Du, Ivor W. Tsang, Xingquan Zhu, Bo Du
Venue: Conference CIKM 2019
Year: 2019

Graph neural nets are emerging tools to represent network nodes for classification. However, existing approaches typically suffer from two limitations: (1) they only aggregate information from short distance (e.g., 1-hop neighbors) each round and fail to capturelong distance relationship in graphs; (2) they require users to label data from several classes to facilitate the learning of discriminative models; whereas in reality, users may only provide labels of a small number of nodes in a single class. To overcome these limitations, this paper presents a novel long-short distance aggregation networks (\textttLSDAN ) for positive unlabeled (PU) graph learning. Our theme is to generate multiple graphs at different distances based on the adjacency matrix, and further develop a long-short distance attention model for these graphs. The short-distance attention mechanism is used to capture the importance of neighbor nodes to a target node. The long-distance attention mechanism is used to capture the propagation of information within a localized area of each node and help model weights of different graphs for node representation learning. A non-negative risk estimator is further employed, to aggregate long- short-distance networks, for PU learning using back-propagated loss modeling. Experiments on real-world datasets validate the effectiveness of our approach.
A study of rumor control strategies on social networks
Authors: Rudra M. Tripathy, Amitabha Bagchi, Sameep Mehta
Venue: Conference CIKM 2010
Year: 2010

In this paper we study and evaluate rumor-like methods for combating the spread of rumors on a social network. We model rumor spread as a diffusion process on a network and suggest the use of an "anti-rumor" process similar to the rumor process. We study two natural models by which these anti-rumors may arise. The main metrics we study are the belief time, i.e., the duration for which a person believes the rumor to be true and point of decline, i.e., point after which anti-rumor process dominates the rumor process. We evaluate our methods by simulating rumor spread and anti-rumor spread on a data set derived from the social networking site Twitter and on a synthetic network generated according to the Watts and Strogatz model. We find that the lifetime of a rumor increases if the delay in detecting it increases, and the relationship is at least linear. Further our findings show that coupling the detection and anti-rumor strategy by embedding agents in the network, we call them beacons, is an effective means of fighting the spread of rumor, even if these beacons do not share information.
Mastering Stock Markets with Efficient Mixture of Diversified Trading Experts
Authors: Shuo Sun, Xinrun Wang, Wanqi Xue, Xiaoxuan Lou, Bo An 0001
Venue: Conference KDD 2023
Year: 2023

Quantitative stock investment is a fundamental financial task that highly relies on accurate prediction of market status and profitable investment decision making. Despite recent advances in deep learning (DL) have shown stellar performance on capturing trading opportunities in the stochastic stock market, the performance of existing DL methods is unstable with sensitivity to network initialization and hyperparameter selection. One major limitation of existing works is that investment decisions are made based on one individual neural network predictor with high uncertainty, which is inconsistent with the workflow in real-world trading firms. To tackle this limitation, we propose AlphaMix, a novel three-stage mixture-of-experts (MoE) framework for quantitative investment to mimic the efficient bottom-up hierarchical trading strategy design workflow of successful trading companies. In Stage one, we introduce an efficient ensemble learning method, whose computational and memory costs are significantly lower comparing to traditional ensemble methods, to train multiple groups of trading experts with personalised market understanding and trading styles. In Stage two, we collect diversified investment suggestions through building a pool of trading experts utilizing hyperparameter level and initialization level diversity of neural networks for post hoc ensemble construction. In Stage three, we design three different mechanisms, namely as-needed router, with-replacement selection and integrated expert soup, to dynamically pick experts from the expert pool, which takes the responsibility of a portfolio manager. Through extensive experiments on US and Chinese stock markets, we demonstrate that AlphaMix significantly outperforms many state-of-the-art baselines in terms of 7 popular financial criteria.
Efficient, direct, and restricted black-box graph evasion attacks to any-layer graph neural networks via influence function
Authors: Binghui Wang, Minhua Lin, Tianxiang Zhou, Pan Zhou, Ang Li, Meng Pang, Hai Helen Li, Yiran Chen
Venue: Conference WSDM 2024
Year: 2024

Graph neural network (GNN), the mainstream method to learn on graph data, is vulnerable to graph evasion attacks, where an attacker slightly perturbing the graph structure can fool trained GNN models. Existing work has at least one of the following drawbacks: 1) limited to directly attack two-layer GNNs; 2) inefficient; and 3) impractical, as they need to know full or part of GNN model parameters. We address the above drawbacks and propose an influence-based efficient, direct, and restricted black-box evasion attack to any-layer GNNs. Specifically, we first introduce two influence functions, i.e., feature-label influence and label influence, that are defined on GNNs and label propagation (LP), respectively. Then we observe that GNNs and LP are strongly connected in terms of our defined influences. Based on this, we can then reformulate the evasion attack to GNNs as calculating label influence on LP, which is inherently applicable to any-layer GNNs, while no need to know information about the internal GNN model. Finally, we propose an efficient algorithm to calculate label influence. Experimental results on various graph datasets show that, compared to state-of-the-art white-box attacks, our attack can achieve comparable attack performance, but has a 5-50x speedup when attacking two-layer GNNs. Moreover, our attack is effective to attack multi-layer GNNs.
Augmented variational autoencoders for collaborative filtering with auxiliary information
Authors: Wonsung Lee, Kyungwoo Song, Il-Chul Moon
Venue: Conference CIKM 2017
Year: 2017

Recommender systems offer critical services in the age of mass information. A good recommender system selects a certain item for a specific user by recognizing why the user might like the item. This awareness implies that the system should model the background of the items and the users. This background modeling for recommendation is tackled through the various models of collaborative filtering with auxiliary information. This paper presents variational approaches for collaborative filtering to deal with auxiliary information. The proposed methods encompass variational autoencoders through augmenting structures to model the auxiliary information and to model the implicit user feedback. This augmentation includes the ladder network and the generative adversarial network to extract the low-dimensional representations influenced by the auxiliary information. These two augmentations are the first trial in the venue of the variational autoencoders, and we demonstrate their significant improvement on the performances in the applications of the collaborative filtering.
Fast coreset-based diversity maximization under matroid constraints
Authors: Matteo Ceccarello, Andrea Pietracaprina, Geppino Pucci
Venue: Conference WSDM 2018
Year: 2018

Max-sum diversity is a fundamental primitive for web search and data mining. For a given set S of n elements, it returns a subset of k«l n representatives maximizing the sum of their pairwise distances, where distance models dissimilarity. An important variant of the primitive prescribes that the desired subset of representatives satisfies an additional orthogonal requirement, which can be specified as a matroid constraint (i.e., a feasible solution must be an independent set of size k). While unconstrained max-sum diversity admits efficient coreset-based strategies, the only known approaches dealing with the additional matroid constraint are inherently sequential and are based on an expensive local search over the entire input set. We devise the first coreset constructions for max-sum diversity under various matroid constraints, together with efficient sequential, MapReduce and Streaming implementations. By running the local-search on the coreset rather than on the entire input, we obtain the first practical solutions for large instances. Technically, our coresets are subsets of S containing a feasible solution which is no more than a factor 1-ε away from the optimal solution, for any fixed ε <1, and, for spaces of bounded doubling dimension, they have a small size independent of n. Extensive experiments show that, with respect to full-blown local search, our coreset-based approach yields solutions of comparable quality, with improvements of up to two orders of magnitude in the running time, also for input sets of unknown dimensionality.
User-Dependent Learning to Debias for Recommendation
Authors: Fangyuan Luo, Jun Wu
Venue: Conference SIGIR 2023
Year: 2023

In recommender systems (RSs), inverse propensity score (IPS) has been a key technique to mitigate popularity bias by decreasing the contribution of popular items in modeling user-item interactions. However, conventional IPS treats all users equally, which tends to over-debias the popularity-insensitive (PI) users and under-debias the popularity-sensitive (PS) users. Furthermore, in such a treatment, IPS only performs slightly well on the debiased test while does not work on the normal biased test. To this end, we propose a user-dependent IPS (UDIPS in short) method, which adaptively conducts propensity estimation for each user-item pair based on the user's sensitivity to item popularity. Like IPS, our theoretical analysis validates the unbiasedness of UDIPS. Remarkably, our solution is model-agnostic and can be easily used to upgrade current unbiased recommenders. We implemented it in four state-of-the-art models for unbiased recommendation, and experimental results on two benchmark datasets demonstrate the effectiveness of our method in both unbiased and normal biased test.
Molsearch: Search-based multi-objective molecular generation and property optimization
Authors: Mengying Sun, Jing Xing, Han Meng, Huijun Wang, Bin Chen, Jiayu Zhou
Venue: Conference KDD 2022
Year: 2022

Leveraging computational methods to generate small molecules with desired properties has been an active research area in the drug discovery field. Towards real-world applications, however, efficient generation of molecules that satisfy <b>multiple</b> property requirements simultaneously remains a key challenge. In this paper, we tackle this challenge using a search-based approach and propose a simple yet effective framework called MolSearch for multi-objective molecular generation (optimization). We show that given proper design and sufficient information, search-based methods can achieve performance comparable or even better than deep learning methods while being computationally efficient. Such efficiency enables massive exploration of chemical space given constrained computational resources. In particular, MolSearch starts with existing molecules and uses a two-stage search strategy to gradually modify them into new ones, based on transformation rules derived systematically and exhaustively from large compound libraries. We evaluate MolSearch in multiple benchmark generation settings and demonstrate its effectiveness and efficiency.
Real-time news cer tification system on sina weibo
Authors: Xing Zhou, Juan Cao, Zhiwei Jin, Fei Xie, Yu Su, Dafeng Chu, Xuehui Cao, Junqiang Zhang
Venue: Conference WWW 2015
Year: 2015

In this paper, we propose a novel framework for real-time news certification. Traditional methods detect rumors on message-level and analyze the credibility of one tweet. However, in most occasions, we only remember the keywords of an event and it's hard for us to completely describe an event in a tweet. Based on the keywords of an event, we gather related microblogs through a distributed data acquisition system which solves the real-time processing needs. Then, we build an ensemble model that combine user-based, propagation-based and content-based model. The experiments show that our system can give a response at 35 seconds on average per query which is critical for real-time system. Most importantly, our ensemble model boost the performance. We also offer some important information such as key users, key microblogs and timeline of events for further investigation of an event.Our system is already deployed in the Xihua News Agency for half a year. To the best of our knowledge, this is the first real-time news certification system for verifying social media contents.
PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation
Authors: Chunjing Gan, Bo Huang, Binbin Hu, Jian Ma, Zhiqiang Zhang, Jun Zhou, Guannan Zhang, Wenliang Zhong
Venue: Conference WSDM 2024
Year: 2024

To help merchants/customers to provide/access a variety of services through miniapps, online service platforms have occupied a critical position in the effective content delivery, in which how to recommend items in the new domain launched by the service provider for customers has become more urgent. However, the non-negligible gap between the source and diversified target domains poses a considerable challenge to cross-domain recommendation systems, which often leads to performance bottlenecks in industrial settings. While entity graphs have the potential to serve as a bridge between domains, rudimentary utilization still fail to distill useful knowledge and even induce the negative transfer issue. To this end, we propose PEACE, a Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation. For domain gap bridging, PEACE is built upon a multi-interest and entity-oriented pre-training architecture which could not only benefit the learning of generalized knowledge in a multi-granularity manner, but also help leverage more structural information in the entity graph. Then, we bring the prototype learning into the pre-training over source domains, so that representations of users and items are greatly improved by the contrastive prototype learning module and the prototype enhanced attention mechanism for adaptive knowledge utilization. To ease the pressure of online serving, PEACE is deployed in a lightweight manner, and significant performance improvements are observed in both online and offline environments.