OpenRaiser/Intern-Atlas huggingface dataset:
Tiprdc: Task-independent privacy-respecting data crowdsourcing framework for deep learning with anonymized intermediate representations
Authors: Ang Li 0005, Yixiao Duan, Huanrui Yang, Yiran Chen 0001, Jianlei Yang 0001
Venue: Conference KDD 2020
Year: 2020

The success of deep learning partially benefits from the availability of various large-scale datasets. These datasets are often crowdsourced from individual users and contain private information like gender, age, etc. The emerging privacy concerns from users on data sharing hinder the generation or use of crowdsourcing datasets and lead to hunger of training data for new deep learning applications. One naive solution is to pre-process the raw data to extract features at the user-side, and then only the extracted features will be sent to the data collector. Unfortunately, attackers can still exploit these extracted features to train an adversary classifier to infer private attributes. Some prior arts leveraged game theory to protect private attributes. However, these defenses are designed for known primary learning tasks, the extracted features work poorly for unknown learning tasks. To tackle the case where the learning task may be unknown or changing, we present TIPRDC, a task-independent privacy-respecting data crowdsourcing framework with anonymized intermediate representation. The goal of this framework is to learn a feature extractor that can hide the privacy information from the intermediate representations; while maximally retaining the original information embedded in the raw data for the data collector to accomplish unknown learning tasks. We design a hybrid training method to learn the anonymized intermediate representation: (1) an adversarial training process for hiding private information from features; (2) maximally retain original information using a neural-network-based mutual information estimator. We extensively evaluate TIPRDC and compare it with existing methods using two image datasets and one text dataset. Our results show that TIPRDC substantially outperforms other existing methods. Our work is the first task-independent privacy-respecting data crowdsourcing framework.
Ballpark crowdsourcing: The wisdom of rough group comparisons
Authors: Tom Hope, Dafna Shahaf
Venue: Conference WSDM 2018
Year: 2018

Crowdsourcing has become a popular method for collecting labeled training data. However, in many practical scenarios traditional labeling can be difficult for crowdworkers(for example, if the data is high-dimensional or unintuitive, or the labels are continuous). In this work, we develop a novel model for crowdsourcing that can complement standard practices by exploiting people»s intuitions about groups and relations between them. We employ a recent machine learning setting, called Ballpark Learning, that can estimate individual labels given only coarse, aggregated signal over groups of data points. To address the important case of continuous labels, we extend the Ballpark setting(which focused on classification) to regression problems. We formulate the problem as a convex optimization problem and propose fast, simple methods with an innate robustness to outliers. We evaluate our methods on real-world datasets, demonstrating how useful constraints about groups can be harnessed from a crowd of non-experts. Our methods can rival supervised models trained on many true labels, and can obtain considerably better results from the crowd than a standard label-collection process(for a lower price). By collecting rough guesses on groups of instances and using machine learning to infer the individual labels, our lightweight framework is able to address core crowdsourcing challenges and train machine learning models in a cost-effective way.
Diversified subgraph query generation with group fairness
Authors: Hanchao Ma, Sheng Guan, Christopher Toomey, Yinghui Wu
Venue: Conference WSDM 2022
Year: 2022

This paper investigates the problem of subgraph query generation with output that satisfies both diversity and fairness constraints. Given a set of groups with associated cardinality requirements, it is to compute subgraph queries with diversified output that meanwhile covers the groups with the desired cardinality. Such need is evident in web and social search with fairness constraints. We formalize subgraph query generation as a bi-criteria optimization problem on the diversity and fairness properties of queries, and verify its hardness and approximability. We show that the problem is in Σp2 , and remains NP-complete even for single-node queries. Despite the hardness, (1) we show that approximations exist whenever a corresponding subset selection process provides good solutions, and provide feasible algorithms with performance guarantees for two practical query generation scenarios. We also present a fast heuristic algorithm for the general problem, which early terminates without enumerating queries. We experimentally verify that our algorithms can efficiently generate queries with desired diversity and coverage properties for targeted groups.
Determinantal point process likelihoods for sequential recommendation
Authors: Yuli Liu, Christian J. Walder, Lexing Xie
Venue: Conference SIGIR 2022
Year: 2022

Sequential recommendation is a popular task in academic research and close to real-world application scenarios, where the goal is to predict the next action(s) of the user based on his/her previous sequence of actions. In the training process of recommender systems, the loss function plays an essential role in guiding the optimization of recommendation models to generate accurate suggestions for users. However, most existing sequential recommendation tech- niques focus on designing algorithms or neural network architectures, and few efforts have been made to tailor loss functions that fit naturally into the practical application scenario of sequential recommender systems. Ranking-based losses, such as cross-entropy and Bayesian Personalized Ranking (BPR) are widely used in the sequential recommendation area. We argue that such objective functions suffer from two inherent drawbacks: i) the dependencies among elements of a sequence are overlooked in these loss formulations; ii) instead of balancing accuracy (quality) and diversity, only generating accurate results has been over emphasized. We therefore propose two new loss functions based on the Determinantal Point Process (DPP) likelihood, that can be adaptively applied to estimate the subsequent item or items. The DPP-distributed item set captures natural dependencies among temporal actions, and a quality vs. diversity decomposition of the DPP kernel pushes us to go beyond accuracy-oriented loss functions. Experimental results using the proposed loss functions on three real-world datasets show marked improvements over state-of-the-art sequential recommendation methods in both quality and diversity metrics.
Csrec: Rethinking sequential recommendation from a causal perspective
Authors: Xiaoyu Liu, Jiaxin Yuan, Yuhang Zhou, Jingling Li, Furong Huang, Wei Ai
Venue: Conference SIGIR 2025
Year: 2025

The essence of sequential recommender systems (RecSys) lies in understanding how users make decisions. Most existing approaches frame the task as sequential prediction based on users' historical purchase records. Although effective in capturing users' natural preferences, this formulation falls short in accurately modeling actual recommendation scenarios, particularly in accounting for how unsuccessful recommendations influence future purchases. Furthermore, the impact of the RecSys itself on users' decisions has not been appropriately isolated and quantitatively analyzed. To address these challenges, we propose a novel formulation of sequential recommendation, called Causal Sequential Recommendation. Instead of merely predicting the next item in a sequence, CSRec distinguishes between a user's natural preference and their actual purchasing decision. It predicts both aspects within a sequential context and traces how current decisions are formed and causally influenced by various factors. Applying such a causal framework can isolate the impact of recommender systems on user decisions, thereby opening new avenues for evaluation and design. This includes assessing how different strategies influence users' trust in the system and determining the optimal recommender system to maximize advertising benefits. CSRec can be seamlessly integrated into existing next-prediction-based methodologies. Experimental evaluations on both synthetic and real-world datasets demonstrate that the proposed implementation significantly improves upon state-of-the-art baselines.
Search mindsets: Understanding focused and non-focused information seeking in music search
Authors: Ang Li, Jennifer Thom, Praveen Chandar, Christine Hosey, Brian St. Thomas, Jean Garcia-Gathright
Venue: Conference WWW 2019
Year: 2019

Music listening is a commonplace activity that has transformed as users engage with online streaming platforms. When presented with anytime, anywhere access to a vast catalog of music, users face challenges in searching for what they want to hear. We propose that users who engage in domain-specific search (e.g., music search) have different information-seeking needs than in general search. Using a mixed-method approach that combines a large-scale user survey with behavior data analyses, we describe the construct of search mindset on a leading online streaming music platform and then investigate two types of search mindsets: focused, where a user is looking for one thing in particular, and non-focused, where a user is open to different results. Our results reveal that searches in the music domain are more likely to be focused than non-focused. In addition, users' behavior (e.g., clicks, streams, querying, etc.) on a music search system is influenced by their search mindset. Finally, we propose design implications for music search systems to best support their users.
Integrating representative and discriminative models for object category detection
Authors: Mario Fritz, Bastian Leibe, Barbara Caputo, Bernt Schiele
Venue: Conference ICCV 2005
Year: 2005

Category detection is a lively area of research. While categorization algorithms tend to agree in using local descriptors, they differ in the choice of the classifier, with some using generative models and others discriminative approaches. This paper presents a method for object category detection which integrates a generative model with a discriminative classifier. For each object category, we generate an appearance codebook, which becomes a common vocabulary for the generative and discriminative methods. Given a query image, the generative part of the algorithm finds a set of hypotheses and estimates their support in location and scale. Then, the discriminative part verifies each hypothesis on the same codebook activations. The new algorithm exploits the strengths of both original methods, minimizing their weaknesses. Experiments on several databases show that our new approach performs better than its building blocks taken separately. Moreover, experiments on two challenging multi-scale databases show that our new algorithm outperforms previously reported results
Learning predictive representations from a history
Authors: Eric Wiewiora
Venue: Conference ICML 2005
Year: 2005

Predictive State Representations (PSRs) have shown a great deal of promise as an alternative to Markov models. However, learning a PSR from a single stream of data generated from an environment remains a challenge. In this work, we present a formalism of PSRs and the domains they model. This formalization suggests an algorithm for learning PSRs that will (almost surely) converge to a globally optimal model given sufficient training data.
Interactive POMDP lite: Towards practical planning to predict and exploit intentions for interacting with self-interested agents
Authors: Trong Nghia Hoang, Kian Hsiang Low
Venue: Conference IJCAI 2013
Year: 2013

A key challenge in non-cooperative multi-agent systems is that of developing efficient planning algorithms for intelligent agents to interact and perform effectively among boundedly rational, self-interested agents (e.g., humans). The practicality of existing works addressing this challenge is being undermined due to either the restrictive assumptions of the other agents' behavior, the failure in accounting for their rationality, or the prohibitively expensive cost of modeling and predicting their intentions. To boost the practicality of research in this field, we investigate how intention prediction can be efficiently exploited and made practical in planning, thereby leading to efficient intention-aware planning frameworks capable of predicting the intentions of other agents and acting optimally with respect to their predicted intentions. We show that the performance losses incurred by the resulting planning policies are linearly bounded by the error of intention prediction. Empirical evaluations through a series of stochastic games demonstrate that our policies can achieve better and more robust performance than the state-of-the-art algorithms.
Rule learning with monotonicity constraints
Authors: Wojciech Kotlowski, Roman Slowinski
Venue: Conference ICML 2009
Year: 2009

In classification with monotonicity constraints, it is assumed that the class label should increase with increasing values on the attributes. In this paper we aim at formalizing the approach to learning with monotonicity constraints from statistical point of view. Motivated by the statistical analysis, we present an algorithm for learning rule ensembles. The algorithm first "monotonizes" the data using a nonparametric classification procedure and then generates a rule ensemble consistent with the training set. The procedure is justified by a theoretical analysis and verified in a computational experiment.
MEET: A Generalized Framework for Reciprocal Recommender Systems
Authors: Lei Li, Tao Li
Venue: Conference CIKM 2012
Year: 2012

Reciprocal recommender systems refer to systems from which users can obtain recommendations of other individuals by satisfying preferences of both parties being involved. Different from the traditional user-item recommendation, reciprocal recommenders focus on the preferences of both parties simultaneously, as well as some special properties in terms of "reciprocal". In this paper, we propose MEET -- a generalized framework for reciprocal recommendation, in which we model the correlations of users as a bipartite graph that maintains both local and global "reciprocal" utilities. The local utility captures users' mutual preferences, whereas the global utility manages the overall quality of the entire reciprocal network. Extensive empirical evaluation on two real-world data sets (online dating and online recruiting) demonstrates the effectiveness of our proposed framework compared with existing recommendation algorithms. Our analysis also provides deep insights into the special aspects of reciprocal recommenders that differentiate them from user-item recommender systems.
Decoupled smoothing on graphs
Authors: Alex Chin, Yatong Chen, Kristen M. Altenburger, Johan Ugander
Venue: Conference WWW 2019
Year: 2019

Graph smoothing methods are an extremely popular family of approaches for semi-supervised learning. The choice of graph used to represent relationships in these learning problems is often a more important decision than the particular algorithm or loss function used, yet this choice is less well-studied in the literature. In this work, we demonstrate that for social networks, the basic friendship graph itself may often not be the appropriate graph for predicting node attributes using graph smoothing. More specifically, standard graph smoothing is designed to harness the social phenomenon of homophily whereby individuals are similar to “the company they keep.” We present a decoupled approach to graph smoothing that decouples notions of “identity” and “preference,” resulting in an alternative social phenomenon of monophily whereby individuals are similar to “the company they're kept in,” as observed in recent empirical work. Our model results in a rigorous extension of the Gaussian Markov Random Field (GMRF) models that underlie graph smoothing, interpretable as smoothing on an appropriate auxiliary graph of weighted or unweighted two-hop relationships.
Coresets for minimum enclosing balls over sliding windows
Authors: Yanhao Wang, Yuchen Li, Kian-Lee Tan
Venue: Conference KDD 2019
Year: 2019

\emph{Coresets} are important tools to generate concise summaries of massive datasets for approximate analysis. A coreset is a small subset of points extracted from the original point set such that certain geometric properties are preserved with provable guarantees. This paper investigates the problem of maintaining a coreset to preserve the minimum enclosing ball (MEB) for a sliding window of points that are continuously updated in a data stream. Although the problem has been extensively studied in batch and append-only streaming settings, no efficient sliding-window solution is available yet. In this work, we first introduce an algorithm, called AOMEB, to build a coreset for MEB in an append-only stream. AOMEB improves the practical performance of the state-of-the-art algorithm while having the same approximation ratio. Furthermore, using AOMEB as a building block, we propose two novel algorithms, namely SWMEB and SWMEB+, to maintain coresets for MEB over the sliding window with constant approximation ratios. The proposed algorithms also support coresets for MEB in a reproducing kernel Hilbert space (RKHS). Finally, extensive experiments on real-world and synthetic datasets demonstrate that SWMEB and SWMEB+ achieve speedups of up to four orders of magnitude over the state-of-the-art batch algorithm while providing coresets for MEB with rather small errors compared to the optimal ones.
Gpop: Scalable group-level popularity prediction for online content in social networks
Authors: Minh X. Hoang, Xuan-Hong Dang, Xiang Wu, Zhenyu Yan, Ambuj K. Singh
Venue: Conference WWW 2017
Year: 2017

Predicting the popularity of online content in social networks is important in many applications, ranging from ad campaign design, web content caching and prefetching, to web-search result ranking. Earlier studies target this problem by learning models that either generalize behaviors of the entire network population or capture behaviors of each individual user. In this paper, we claim that a novel approach based on group-level popularity is necessary and more practical, given that users naturally organize themselves into clusters and that users within a cluster react to online content in a uniform manner. We develop a novel framework by first grouping users into cohesive clusters, and then adopt tensor decomposition to make predictions. In order to minimize the impact of noisy data and be more flexible in capturing changes in users' interests, our framework exploits both the network topology and interaction among users in learning a robust user clustering. The PARAFAC tensor decomposition is adapted to work with hierarchical constraint over user groups, and we show that optimizing this constrained function via gradient descent achieves faster convergence and leads to more stable solutions. Extensive experimental results over two social networks demonstrate that our framework is scalable, finds meaningful user groups, and significantly outperforms eight baseline methods in terms of prediction accuracy.
Detection of review abuse via semi-supervised binary multi-target tensor decomposition
Authors: Anil R. Yelundur, Vineet Chaoji, Bamdev Mishra
Venue: Conference KDD 2019
Year: 2019

Product reviews and ratings on e-commerce websites provide customers with detailed insights about various aspects of the product such as quality, usefulness, etc. Since they influence customers' buying decisions, product reviews have become a fertile ground for abuse by sellers (colluding with reviewers) to promote their own products or to tarnish the reputation of competitor's products. In this paper, our focus is on detecting such abusive entities (both sellers and reviewers) by applying tensor decomposition on the product reviews data. While tensor decomposition is mostly unsupervised, we formulate our problem as a semi-supervised binary multi-target tensor decomposition, to take advantage of currently known abusive entities. We empirically show that our multi-target semi-supervised model achieves higher precision and recall in detecting abusive entities as compared to unsupervised techniques. Finally, we show that our proposed stochastic partial natural gradient inference for our model empirically achieves faster convergence than stochastic gradient and Online-EM with sufficient statistics.
Modeling individual cyclic variation in human behavior
Authors: Emma Pierson, Tim Althoff, Jure Leskovec
Venue: Conference WWW 2018
Year: 2018

Cycles are fundamental to human health and behavior. Examples include mood cycles, circadian rhythms, and the menstrual cycle. However, modeling cycles in time series data is challenging because in most cases the cycles are not labeled or directly observed and need to be inferred from multidimensional measurements taken over time. Here, we present Cyclic Hidden Markov Models (CyHMMs) for detecting and modeling cycles in a collection of multidimensional heterogeneous time series data. In contrast to previous cycle modeling methods, CyHMMs deal with a number of challenges encountered in modeling real-world cycles: they can model multivariate data with both discrete and continuous dimensions; they explicitly model and are robust to missing data; and they can share information across individuals to accommodate variation both within and between individual time series. Experiments on synthetic and real-world health-tracking data demonstrate that CyHMMs infer cycle lengths more accurately than existing methods, with 58% lower error on simulated data and 63% lower error on real-world data compared to the best-performing baseline. CyHMMs can also perform functions which baselines cannot: they can model the progression of individual features/symptoms over the course of the cycle, identify the most variable features, and cluster individual time series into groups with distinct characteristics. Applying CyHMMs to two real-world health-tracking datasets -- of human menstrual cycle symptoms and physical activity tracking data -- yields important insights including which symptoms to expect at each point during the cycle. We also find that people fall into several groups with distinct cycle patterns, and that these groups differ along dimensions not provided to the model. For example, by modeling missing data in the menstrual cycles dataset, we are able to discover a medically relevant group of birth control users even though information on birth control is not given to the model.