OpenRaiser/Intern-Atlas huggingface dataset:
Moving cast shadow detection using physics-based features
Authors: Jia-Bin Huang 0001, Chu-Song Chen
Venue: Conference CVPR 2009
Year: 2009

Cast shadows induced by moving objects often cause serious problems to many vision applications. We present in this paper an online statistical learning approach to model the background appearance variations under cast shadows. Based on the bi-illuminant (i.e. direct light sources and ambient illumination) dichromatic reflection model, we derive physics-based color features under the assumptions of constant ambient illumination and light sources with common spectral power distributions. We first use one Gaussian mixture model (GMM) to learn the color features, which are constant regardless of the background surfaces or illuminant colors in a scene. Then, we build up one pixel based GMM for each pixel to learn the local shadow features. To overcome the slow convergence rate in the conventional GMM learning, we update the pixel-based GMMs through confidence-rated learning. The proposed method can rapidly learn model parameters in an unsupervised way and adapt to illumination conditions or environment changes. Furthermore, we demonstrate that our method is robust to scenes with few foreground activities and videos captured at low or unsteady frame rates.
A New Class of Polynomial Activation Functions of Deep Learning for Precipitation Forecasting
Authors: Jiachuan Wang, Lei Chen, Charles Wang Wai Ng
Venue: Conference WSDM 2022
Year: 2022

Precipitation forecasting, modeled as an important chaotic system in earth system science, is not explicitly solved with theory-driven models. In recent years, deep learning models have achieved great success in various applications including rainfall prediction. However, these models work in an image processing manner regardless of the nature of a physical system. We found that the non-linearity relationships learned by deep learning models, which mostly rely on the activation functions, are commonly weighted piecewise continuous functions with bounded first-order derivatives. In contrast, the polynomial is one of the most widely used classes of functions for theory-driven models, applied to numerical approximation, dynamic system modeling, etc.. Researchers started to use the polynomial activation functions (Pacs in short) for neural networks from the 1990s. In recent years, with bloomed researches that apply deep learning to scientific problems, it is weird that such a powerful class of basis functions is rarely used. In this paper, we investigate it and argue that, even though polynomials are good at information extraction, it is too fragile to train stably. We finally solve its serious data flow explosion problem with Chebyshev polynomials and prepended normalization, which enables networks to go deep with Pacs. To enhance the robustness of training, a normalization called Range Norm is further proposed. Performance on synthetic dataset and summer precipitation prediction task validates the necessity of such a class of activation functions to simulate complex physical mechanisms. The new tool for deep learning enlightens a new way of automatic theoretical physics analysis.
Bipartite Graph Embedding via Mutual Information Maximization
Authors: Jiangxia Cao, Xixun Lin, Shu Guo, Luchen Liu, Tingwen Liu, Bin Wang
Venue: Conference WSDM 2021
Year: 2021

Bipartite graph embedding has recently attracted much attention due to the fact that bipartite graphs are widely used in various application domains. Most previous methods, which adopt random walk-based or reconstruction-based objectives, are typically effective to learn local graph structures. However, the global properties of bipartite graph, including community structures of homogeneous nodes and long-range dependencies of heterogeneous nodes, are not well preserved. In this paper, we propose a bipartite graph embedding called BiGI to capture such global properties by introducing a novel local-global infomax objective. Specifically, BiGI first generates a global representation which is composed of two prototype representations. BiGI then encodes sampled edges as local representations via the proposed subgraph-level attention mechanism. Through maximizing the mutual information between local and global representations, BiGI enables nodes in bipartite graph to be globally relevant. Our model is evaluated on various benchmark datasets for the tasks of top-K recommendation and link prediction. Extensive experiments demonstrate that BiGI achieves consistent and significant improvements over state-of-the-art baselines. Detailed analyses verify the high effectiveness of modeling the global properties of bipartite graph.
A generalized co-hits algorithm and its application to bipartite graphs
Authors: Hongbo Deng, Michael R. Lyu, Irwin King
Venue: Conference KDD 2009
Year: 2009

Recently many data types arising from data mining and Web search applications can be modeled as bipartite graphs. Examples include queries and URLs in query logs, and authors and papers in scientific literature. However, one of the issues is that previous algorithms only consider the content and link information from one side of the bipartite graph. There is a lack of constraints to make sure the final relevance of the score propagation on the graph, as there are many noisy edges within the bipartite graph. In this paper, we propose a novel and general Co-HITS algorithm to incorporate the bipartite graph with the content information from both sides as well as the constraints of relevance. Moreover, we investigate the algorithm based on two frameworks, including the iterative and the regularization frameworks, and illustrate the generalized Co-HITS algorithm from different views. For the iterative framework, it contains HITS and personalized PageRank as special cases. In the regularization framework, we successfully build a connection with HITS, and develop a new cost function to consider the direct relationship between two entity sets, which leads to a significant improvement over the baseline method. To illustrate our methodology, we apply the Co-HITS algorithm, with many different settings, to the application of query suggestion by mining the AOL query log data. Experimental results demonstrate that CoRegu-0.5 (i.e., a model of the regularization framework) achieves the best performance with consistent and promising improvements.
Generate Neural Template Explanations for Recommendation
Authors: Lei Li, Yongfeng Zhang, Li Chen
Venue: Conference CIKM 2020
Year: 2020

Personalized recommender systems are important to assist user decision-making in the era of information overload. Meanwhile, explanations of the recommendations further help users to better understand the recommended items so as to make informed choices, which gives rise to the importance of explainable recommendation research. Textual sentence-based explanation has been an important form of explanations for recommender systems due to its advantage in communicating rich information to users. However, current approaches to generating sentence explanations are either limited to predefined sentence templates, which restricts the sentence expressiveness, or opt for free-style sentence generation, which makes it difficult for sentence quality control. In an attempt to benefit both sentence expressiveness and quality, we propose a Neural Template (NETE) explanation generation framework, which brings the best of both worlds by learning sentence templates from data and generating template-controlled sentences that comment about specific features. Experimental results on real-world datasets show that NETE consistently outperforms state-of-the-art explanation generation approaches in terms of sentence quality and expressiveness. Further analysis on case study also shows the advantages of NETE on generating diverse and controllable explanations.
Operationalizing the legal principle of data minimization for personalization
Authors: Asia J. Biega, Peter Potash, Hal Daumé III, Fernando Diaz 0001, Michèle Finck
Venue: Conference SIGIR 2020
Year: 2020

Article 5(1)(c) of the European Union's General Data Protection Regulation (GDPR) requires that "personal data shall be [...] adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed (`data minimisation')". To date, the legal and computational definitions of `purpose limitation' and `data minimization' remain largely unclear. In particular, the interpretation of these principles is an open issue for information access systems that optimize for user experience through personalization and do not strictly require personal data collection for the delivery of basic service. In this paper, we identify a lack of a homogeneous interpretation of the data minimization principle and explore two operational definitions applicable in the context of personalization. The focus of our empirical study in the domain of recommender systems is on providing foundational insights about the (i) feasibility of different data minimization definitions, (ii) robustness of different recommendation algorithms to minimization, and (iii) performance of different minimization strategies.We find that the performance decrease incurred by data minimization might not be substantial, but that it might disparately impact different users---a finding which has implications for the viability of different formal minimization definitions. Overall, our analysis uncovers the complexities of the data minimization problem in the context of personalization and maps the remaining computational and regulatory challenges.
Crowdsourcing the annotation of rumourous conversations in social media
Authors: Arkaitz Zubiaga, Maria Liakata, Rob Procter, Kalina Bontcheva, Peter Tolmie
Venue: Conference WWW 2015
Year: 2015

Social media are frequently rife with rumours, and the study of rumour conversational aspects can provide valuable knowledge about how rumours evolve over time and are discussed by others who support or deny them. In this work, we present a new annotation scheme for capturing rumour-bearing conversational threads, as well as the crowdsourcing methodology used to create high quality, human annotated datasets of rumourous conversations from social media. The rumour annotation scheme is validated through comparison between crowdsourced and reference annotations. We also found that only a third of the tweets in rumourous conversations contribute towards determining the veracity of rumours, which reinforces the need for developing methods to extract the relevant pieces of information automatically.
Robust imitation learning against variations in environment dynamics
Authors: Jongseong Chae, Seungyul Han, Whiyoung Jung, Myungsik Cho, Sungho Choi, Youngchul Sung
Venue: Conference ICML 2022
Year: 2022

In this paper, we propose a robust imitation learning (IL) framework that improves the robustness of IL when environment dynamics are perturbed. The existing IL framework trained in a single environment can catastrophically fail with perturbations in environment dynamics because it does not capture the situation that underlying environment dynamics can be changed. Our framework effectively deals with environments with varying dynamics by imitating multiple experts in sampled environment dynamics to enhance the robustness in general variations in environment dynamics. In order to robustly imitate the multiple sample experts, we minimize the risk with respect to the Jensen-Shannon divergence between the agent's policy and each of the sample experts. Numerical results show that our algorithm significantly improves robustness against dynamics perturbations compared to conventional IL baselines.
Large Language Models for Recommendation: Progresses and Future Directions
Authors: Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He
Venue: Conference WWW 2024
Year: 2024

Large language models (LLMs) have significantly influenced recommender systems. Both academia and industry have shown growing interest in developing LLMs for recommendation purposes, an approach commonly referred to as LLM4Rec. This involves efforts such as utilizing LLMs for generative item retrieval and ranking, along with the potential for creating universal LLMs for varied recommendation tasks, signaling a possible paradigm shift in recommender systems. This tutorial is designed to review the progression of LLM4Rec and provide an in-depth analysis of the prevailing studies. We will discuss how LLMs advance recommender systems in model architecture, learning paradigms, and capabilities like conversation, generalization, planning, and content generation. Additionally, the tutorial will highlight open problems and challenges in this nascent field, addressing concerns related to trustworthiness, efficiency, online training, and recommendation data modeling. Concluding with a summary of the takeaways from previous research, the tutorial will suggest avenues for future investigations. Our aim is to help the audience grasp the developments in LLM4Rec, as well as to spark inspiration for further research. By doing so, we expect to contribute to the growth and success of LLM4Rec, possibly leading to a fundamental change in recommender paradigms.
FORM: Follow the Online Regularized Meta-Leader for Cold-Start Recommendation
Authors: Xuehan Sun, Tianyao Shi, Xiaofeng Gao 0001, Yanrong Kang, Guihai Chen
Venue: Conference SIGIR 2021
Year: 2021

Meta-learning based recommendation systems alleviate the cold-start problem through a bi-level meta-optimization process. Recommendation borrows prior experience from pre-trained static system-level parameters and fine-tunes the model in user-level for new users. However, it is more natural for the system to sample users in a dynamic online sequence in most real-world recommendation systems, which brings further challenges for existing meta-learning based recommendation: system-level updates begins before user-level recommendation models have converged on the whole time series; stable and randomness-resistant bi-level gradient descent approaches are missing in the current meta-learning framework; evaluation on learning abilities across different users are lacked for exploring the diversities of different users.
Distributionally Robust Sequential Recommnedation
Authors: Rui Zhou, Xian Wu, Zhaopeng Qiu, Yefeng Zheng, Xu Chen
Venue: Conference SIGIR 2023
Year: 2023

Modeling user sequential behaviors have been demonstrated to be effective in promoting the recommendation performance. While previous work has achieved remarkable successes, they mostly assume that the training and testing distributions are consistent, which may contradict with the diverse and complex user preferences, and limit the recommendation performance in real-world scenarios. To alleviate this problem, in this paper, we propose a robust sequential recommender framework to overcome the potential distribution shift between the training and testing sets. In specific, we firstly simulate different training distributions via sample reweighting. Then, we minimize the largest loss induced by these distributions to optimize the 'worst-case' loss for improving the model robustness. Considering that there can be too many sample weights, which may introduce too much flexibility and be hard to optimize, we cluster the training samples based on both hard and soft strategies, and assign each cluster with a unified weight. At last, we analyze our framework by presenting the generalization error bound of the above minimax objective, which help us to better understand the proposed framework from the theoretical perspective. We conduct extensive experiments based on three real-world datasets to demonstrate the effectiveness of our proposed framework. To reproduce our experiments and promote this research direction, we have released our project at https://anonymousrsr.github.io/RSR/.
An exploratory study of stock price movements from earnings calls
Authors: Sourav Medya, Mohammad Rasoolinejad, Yang Yang, Brian Uzzi
Venue: Conference WWW 2022
Year: 2022

Financial market analysis has focused primarily on extracting signals from accounting, stock price, and other numerical “hard” data reported in P&L statements or earnings per share reports. Yet, it is well-known that decision-makers routinely use “soft” text-based documents that interpret the hard data they narrate. Recent advances in computational methods for analyzing unstructured and soft text-based data at scale offer possibilities for understanding financial market behavior that could improve investments and market equity. A critical and ubiquitous form of soft data are earnings calls. Earnings calls are periodic (often quarterly) statements usually by CEOs who attempt to influence investors’ expectations of a company’s past and future performance. Here, we study the statistical relationship between earnings calls, company sales, stock performance, and analysts’ recommendations. Our study covers a decade of observations with approximately 100,000 transcripts of earnings calls from 6,300 public companies from January 2010 to December 2019. In this study, we report three novel findings. First, the buy, sell and hold recommendations from professional analysts made prior to the earnings have low correlation with stock price movements after the earnings call. Second, using our graph neural network based method that processes the semantic features of earnings calls, we reliably and accurately predict stock price movements in five major areas of the economy. Third, the semantic features of transcripts are more predictive of stock price movements than sales and earnings per share, i.e., traditional hard data in most of the cases.
WISE: Wavelet based Interpretable Stock Embedding for Risk-Averse Portfolio Management
Authors: Mengying Zhu, Yan Wang, Fei Wu, Mengyuan Yang, Cheng Chen, Qianqiao Liang, Xiaolin Zheng
Venue: Conference WWW 2022
Year: 2022

Markowitz’s portfolio theory is the cornerstone of the risk-averse portfolio selection (RPS) problem, the core of which lies in minimizing the risk, i.e., a value calculated based on a portfolio risk matrix. Because the real risk matrix is unobservable, usual practices compromise to utilize the covariance matrix of all stocks in the portfolio based on their historical prices to estimate the risk matrix, which, however, lack the interpretability of the computed risk degree. In this paper, we propose a novel RPS method named WISE based on wavelet decomposition, which not only fully exploits stock time series from the perspectives of the time domain and frequency domain, but also has the advantage of providing interpretability on the portfolio decision from different frequency angles. In addition, in WISE, we design a theoretically guaranteed wavelet basis selection mechanism and three auxiliary enhancement tasks to adaptively find the suitable wavelet parameters and improve the representation ability of the stock embeddings respectively. Extensive experiments conducted on three real-world datasets demonstrate WISE’s superiority over the state-of-the-art portfolio selection methods in terms of return and risk. In addition, we introduce a qualitative analysis of the computed risk matrices of portfolios to indicate the interpretability of WISE on the computed risk degree from different frequency angles.
A game theoretic formulation of the service provisioning problem in cloud systems
Authors: Danilo Ardagna, Barbara Panicucci, Mauro Passacantando
Venue: Conference WWW 2011
Year: 2011

Cloud computing is an emerging paradigm which allows the on-demand delivering of software, hardware, and data as services. As cloud-based services are more numerous and dynamic, the development of efficient service provisioning policies become increasingly challenging. Game theoretic approaches have shown to gain a thorough analytical understanding of the service provisioning problem.\n\nIn this paper we take the perspective of Software as a Service (SaaS) providers which host their applications at an Infrastructure as a Service (IaaS) provider. Each SaaS needs to comply with quality of service requirements, specified in Service Level Agreement (SLA) contracts with the end-users, which determine the revenues and penalties on the basis of the achieved performance level. SaaS providers want to maximize their revenues from SLAs, while minimizing the cost of use of resources supplied by the IaaS provider. Moreover, SaaS providers compete and bid for the use of infrastructural resources. On the other hand, the IaaS wants to maximize the revenues obtained providing virtualized resources. In this paper we model the service provisioning problem as a Generalized Nash game, and we propose an efficient algorithm for the run time management and allocation of IaaS resources to competing SaaSs.
A reproducibility study of deep and surface machine learning methods for human-related trajectory prediction
Authors: Bardh Prenkaj, Paola Velardi, Damiano Distante, Stefano Faralli
Venue: Conference CIKM 2020
Year: 2020

In this paper, we compare several deep and surface state-of-the-art machine learning methods for risk prediction in problems that can be modelled as a trajectory of events separated by irregular time intervals. Trajectories are the abstract representation of many real-life data, such as patient records, student e-tivities, online financial transactions, and many others. Given the continuously increasing number of machine learning methods to predict future high-risk events in these contexts, we aim to provide more insight into reproducibility and applicability of these methods when changing datasets, parameters, and evaluation measures. As an additional contribution, we release to the community the implementations of all compared methods.
Eye gaze tracking under natural head movements
Authors: Zhiwei Zhu, Qiang Ji
Venue: Conference CVPR 2005
Year: 2005

Most available remote eye gaze trackers based on pupil center corneal reflection (PCCR) technique have two characteristics that prevent them from being widely used as an important computer input device for human computer interaction. First, they must often be calibrated repeatedly for each individual; second, they have low tolerance for head movements and require the user to hold the head uncomfortably still. In this paper, we propose a solution for the classical PCCR technique that simplify the calibration procedure and allow free head movements. The core of our method is to analytically obtain a head mapping function to compensate head movement. Specifically, the head mapping function allows to automatically map the eye movement measurement under an arbitrary head position to a reference head position so that the gaze can be estimated from the mapped eye measurement with respect to the reference head position. Furthermore, our method minimizes the calibration procedure to only one time for each individual. Our proposed method significantly improves the usability of the eye gaze tracking technology, which is a major step for eye tracker to be accepted as a natural computer input device.