OpenRaiser/Intern-Atlas huggingface dataset:
Camus: attribute-aware counterfactual augmentation for minority users in recommendation
Authors: Yuxin Ying, Fuzhen Zhuang, Yongchun Zhu, Deqing Wang, Hongwei Zheng
Venue: Conference WWW 2023
Year: 2023

Embedding-based methods currently achieved impressive success in recommender systems. However, such methods are more likely to suffer from bias in data distribution, especially the attribute bias problem. For example, when a certain type of user, like the elderly, occupies the mainstream, the recommendation results of minority users would be seriously affected by the mainstream users’ attributes. To address this problem, most existing methods are proposed from the perspective of fairness, which focuses on eliminating unfairness but deteriorates the recommendation performance. Unlike these methods, in this paper, we focus on improving the recommendation performance for minority users of biased attributes. Along this line, we propose a novel attribute-aware Counterfactual Augmentation framework for Minority Users(CAMUS). Specifically, the CAMUS consists of a counterfactual augmenter, a confidence estimator, and a recommender. The counterfactual augmenter conducts data augmentation for the minority group by utilizing the interactions of mainstream users based on a universal counterfactual assumption. Besides, a tri-training-based confidence estimator is applied to ensure the effectiveness of augmentation. Extensive experiments on three real-world datasets have demonstrated the superior performance of the proposed methods. Further case studies verify the universality of the proposed CAMUS framework on different data sparsity, attributes, and models.
Scha-vae: Hierarchical context aggregation for few-shot generation
Authors: Giorgio Giannone, Ole Winther
Venue: Conference ICML 2022
Year: 2022

A few-shot generative model should be able to generate data from a novel distribution by only observing a limited set of examples. In few-shot learning the model is trained on data from many sets from distributions sharing some underlying properties such as sets of characters from different alphabets or objects from different categories. We extend current latent variable models for sets to a fully hierarchical approach with an attention-based point to set-level aggregation and call our method SCHA-VAE for Set-Context-Hierarchical-Aggregation Variational Autoencoder. We explore likelihood-based model comparison, iterative data sampling, and adaptation-free out-of-distribution generalization. Our results show that the hierarchical formulation better captures the intrinsic variability within the sets in the small data regime. This work generalizes deep latent variable approaches to few-shot learning, taking a step toward large-scale few-shot generation with a formulation that readily works with current state-of-the-art deep generative models.
Hierarchical reinforcement learning using Gaussian random trajectory generation in autonomous furniture assembly
Authors: Won Joon Yun, David Mohaisen, Soyi Jung, Jong-Kook Kim, Joongheon Kim
Venue: Conference CIKM 2022
Year: 2022

In this paper, we propose a Gaussian Random Trajectory guided Hierarchical Reinforcement Learning (GRT-HL) method for autonomous furniture assembly. The furniture assembly problem is formulated as a comprehensive human-like long-horizon manipulation task that requires a long-term planning and a sophisticated control. Our proposed model, GRT-HL, draws inspirations from the semi-supervised adversarial autoencoders, and learns latent representations of the position trajectories of the end-effector. The high-level policy generates an optimal trajectory for furniture assembly, considering the structural limitations of the robotic agents. Given the trajectory drawn from the high-level policy, the low-level policy makes a plan and controls the end-effector. We first evaluate the performance of GRT-HL compared to the state-of-the-art reinforcement learning methods in furniture assembly tasks. We demonstrate that GRT-HL successfully solves the long-horizon problem with extremely sparse rewards by generating the trajectory for planning.
Fair and interpretable models for survival analysis
Authors: Md. Mahmudur Rahman, Sanjay Purushotham
Venue: Conference KDD 2022
Year: 2022

Survival analysis aims to predict the risk of an event, such as death due to cancer, in the presence of censoring. Recent research has shown that existing survival techniques are prone to unintentional biases towards protected attributes such as age, race, and/or gender. For example, censoring assumed to be unrelated to the prognosis and covariates (typically violated in real data) often leads to overestimation and biased survival predictions for different protected groups. In order to attenuate harmful bias and ensure fair survival predictions, we introduce fairness definitions based on survival functions and censoring. We propose novel fair and interpretable survival models which use pseudo valued-based objective functions with fairness definitions as constraints for predicting subject-specific survival probabilities. Experiments on three real-world survival datasets demonstrate that our proposed fair survival models show significant improvement over existing survival techniques in terms of accuracy and fairness measures. We show that our proposed models provide fair predictions for protected attributes under different types and amounts of censoring. Furthermore, we study the interplay between interpretability and fairness; and investigate how fairness and censoring impact survival predictions for different protected attributes.
An Industrial Framework for Cold-Start Recommendation in Zero-Shot Scenarios
Authors: Zhaoxin Huan, Gongduo Zhang, Xiaolu Zhang, Jun Zhou, Qintong Wu, Lihong Gu, Jinjie Gu, Yong He, Yue Zhu, Linjian Mo
Venue: Conference SIGIR 2022
Year: 2022

There exists the cold-start problem in the recommendation systems when observed user-item interactions are insufficient. To alleviate this problem, most existing works aim to learn globally shared prior knowledge across all items and be fast adapted to a new item with few interactions. However, such learning techniques are data demanding and work poorly on new items with no interactions. In this applied paper, we present an industrial framework recently deployed on Alipay to address the item cold-start problem in zero-shot scenarios. The proposed framework provides both efficient and high-quality recommendations for cold items with no log data. Specifically, we formulate the cold-start problem as a zero-shot learning problem and build a highly efficient infrastructure to accomplish online zero-shot recommendations used on large-scale platforms. Extensive offline experiments and online A/B testing demonstrate that the proposed framework has superior performance and recommends cold items to preferred users more effectively than other state-of-the-art methods.
Time-constrained photography
Authors: Samuel W. Hasinoff, Kiriakos N. Kutulakos, Frédo Durand, William T. Freeman
Venue: Conference ICCV 2009
Year: 2009

Capturing multiple photos at different focus settings is a powerful approach for reducing optical blur, but how many photos should we capture within a fixed time budget? We develop a framework to analyze optimal capture strategies balancing the tradeoff between defocus and sensor noise, incorporating uncertainty in resolving scene depth. We derive analytic formulas for restoration error and use Monte Carlo integration over depth to derive optimal capture strategies for different camera designs, under a wide range of photographic scenarios. We also derive a new upper bound on how well spatial frequencies can be preserved over the depth of field. Our results show that by capturing the optimal number of photos, a standard camera can achieve performance at the level of more complex computational cameras, in all but the most demanding of cases. We also show that computational cameras, although specifically designed to improve one-shot performance, generally benefit from capturing multiple photos as well.
Optimizing Binary MRFs via Extended Roof Duality
Authors: Carsten Rother, Vladimir Kolmogorov, Victor S. Lempitsky, Martin Szummer
Venue: Conference CVPR 2007
Year: 2007

Many computer vision applications rely on the efficient optimization of challenging, so-called non-submodular, binary pairwise MRFs. A promising graph cut based approach for optimizing such MRFs known as "roof duality" was recently introduced into computer vision. We study two methods which extend this approach. First, we discuss an efficient implementation of the "probing" technique introduced recently by Bows et al. (2006). It simplifies the MRF while preserving the global optimum. Our code is 400-700 faster on some graphs than the implementation of the work of Bows et al. (2006). Second, we present a new technique which takes an arbitrary input labeling and tries to improve its energy. We give theoretical characterizations of local minima of this procedure. We applied both techniques to many applications, including image segmentation, new view synthesis, super-resolution, diagram recognition, parameter learning, texture restoration, and image deconvolution. For several applications we see that we are able to find the global minimum very efficiently, and considerably outperform the original roof duality approach. In comparison to existing techniques, such as graph cut, TRW, BP, ICM, and simulated annealing, we nearly always find a lower energy.
TWIN: Personalized clinical trial digital twin generation
Authors: Trisha Das, Zifeng Wang 0008, Jimeng Sun 0001
Venue: Conference KDD 2023
Year: 2023

Clinical trial digital twins are virtual patients that reflect personal characteristics in a high degree of granularity and can be used to simulate various patient outcomes under different conditions. With the growth of clinical trial databases captured by Electronic Data Capture (EDC) systems, there is a growing interest in using machine learning models to generate digital twins. This can benefit the drug development process by reducing the sample size required for participant recruitment, improving patient outcome predictive modeling, and mitigating privacy risks when sharing synthetic clinical trial data. However, prior research has mainly focused on generating Electronic Healthcare Records (EHRs), which often assume large training data and do not account for personalized synthetic patient record generation. In this paper, we propose a sample-efficient method TWIN for generating personalized clinical trial digital twins. TWIN can produce digital twins of patient-level clinical trial records with high fidelity to the targeting participant's record and preserves the temporal relations across visits and events. We compare our method with various baselines for generating real-world patient-level clinical trial data. The results show that TWIN generates synthetic trial data with high fidelity to facilitate patient outcome predictions in low-data scenarios and strong privacy protection against real patients from the trials.
Privacy-Preserving Synthetic Data Generation for Recommendation Systems
Authors: Fan Liu, Zhiyong Cheng, Huilin Chen, Yinwei Wei, Liqiang Nie, Mohan S. Kankanhalli
Venue: Conference SIGIR 2022
Year: 2022

Recommendation systems make predictions chiefly based on users' historical interaction data (e.g., items previously clicked or purchased). There is a risk of privacy leakage when collecting the users' behavior data for building the recommendation model. However, existing privacy-preserving solutions are designed for tackling the privacy issue only during the model training [32] and results collection [40] phases. The problem of privacy leakage still exists when directly sharing the private user interaction data with organizations or releasing them to the public. To address this problem, in this paper, we present a User Privacy Controllable Synthetic Data Generation model (short for UPC-SDG), which generates synthetic interaction data for users based on their privacy preferences. The generation model aims to provide certain privacy guarantees while maximizing the utility of the generated synthetic data at both data level and item level. Specifically, at the data level, we design a selection module that selects those items that contribute less to a user's preferences from the user's interaction data. At the item level, a synthetic data generation module is proposed to generate a synthetic item corresponding to the selected item based on the user's preferences. Furthermore, we also present a privacy-utility trade-off strategy to balance the privacy and utility of the synthetic data. Extensive experiments and ablation studies have been conducted on three publicly accessible datasets to justify our method, demonstrating its effectiveness in generating synthetic data under users' privacy preferences.
K-multiple-means: A multiple-means clustering method with specified k clusters
Authors: Feiping Nie, Cheng-Long Wang, Xuelong Li
Venue: Conference KDD 2019
Year: 2019

In this paper, we make an extension of K-means for the clustering of multiple means. The popular K-means clustering uses only one center to model each class of data. However, the assumption on the shape of the clusters prohibits it to capture the non-convex patterns. Moreover, many categories consist of multiple subclasses which obviously cannot be represented by a single prototype. We propose a K-Multiple-Means (KMM) method to group the data points with multiple sub-cluster means into specified k clusters. Unlike the methods which use the agglomerative strategies, the proposed method formalizes the multiple-means clustering problem as an optimization problem and updates the partitions of m sub-cluster means and k clusters by an alternating optimization strategy. Notably, the partition of the original data with multiple-means representation is modeled as a bipartite graph partitioning problem with the constrained Laplacian rank. We also show the theoretical analysis of the connection between our method and the K-means clustering. Meanwhile, KMM is linear scaled with respect to n. Experimental results on several synthetic and well-known real-world data sets are conducted to show the effectiveness of the proposed algorithm.
Is Yelp Actually Cleaning Up the Restaurant Industry? A Re-Analysis on the Relative Usefulness of Consumer Reviews
Authors: Kristen M. Altenburger, Daniel E. Ho
Venue: Conference WWW 2019
Year: 2019

Social media provides the government with novel methods to improve regulation. One leading case has been the use of Yelp reviews to target food safety inspections. While previous research on data from Seattle finds that Yelp reviews can predict unhygienic establishments, we provide a more cautionary perspective. First, we show that prior results are sensitive to what we call “Extreme Imbalanced Sampling”: extreme because the dataset was restricted from roughly 13k inspections to a sample of only 612 inspections with only extremely high or low inspection scores, and imbalanced by not accounting for class imbalance in the population. We show that extreme imbalanced sampling is responsible for claims about the power of Yelp information in the original classification setup. Second, a re-analysis that utilizes the full dataset of 13k inspections and models the full inspection score (regression instead of classification) shows that (a) Yelp information has lower predictive power than prior inspection history and (b) Yelp reviews do not significantly improve predictions, given existing information about restaurants and inspection history. Contrary to prior claims, Yelp reviews do not appear to aid regulatory targeting. Third, this case study highlights critical issues when using social media for predictive models in governance and corroborates recent calls for greater transparency and reproducibility in machine learning.
Deep Collaborative Filtering via Marginalized Denoising Auto-encoder
Authors: Sheng Li, Jaya Kawale, Yun Fu
Venue: Conference CIKM 2015
Year: 2015

Collaborative filtering (CF) has been widely employed within recommender systems to solve many real-world problems. Learning effective latent factors plays the most important role in collaborative filtering. Traditional CF methods based upon matrix factorization techniques learn the latent factors from the user-item ratings and suffer from the cold start problem as well as the sparsity problem. Some improved CF methods enrich the priors on the latent factors by incorporating side information as regularization. However, the learned latent factors may not be very effective due to the sparse nature of the ratings and the side information. To tackle this problem, we learn effective latent representations via deep learning. Deep learning models have emerged as very appealing in learning effective representations in many applications. In particular, we propose a general deep architecture for CF by integrating matrix factorization with deep feature learning. We provide a natural instantiations of our architecture by combining probabilistic matrix factorization with marginalized denoising stacked auto-encoders. The combined framework leads to a parsimonious fit over the latent features as indicated by its improved performance in comparison to prior state-of-art models over four large datasets for the tasks of movie/book recommendation and response prediction.
Variational autoencoders for collaborative filtering
Authors: Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara
Venue: Conference WWW 2018
Year: 2018

We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research.We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation. Despite widespread use in language modeling and economics, the multinomial likelihood receives less attention in the recommender systems literature. We introduce a different regularization parameter for the learning objective, which proves to be crucial for achieving competitive performance. Remarkably, there is an efficient way to tune the parameter using annealing. The resulting model and learning algorithm has information-theoretic connections to maximum entropy discrimination and the information bottleneck principle. Empirically, we show that the proposed approach significantly outperforms several state-of-the-art baselines, including two recently-proposed neural network approaches, on several real-world datasets. We also provide extended experiments comparing the multinomial likelihood with other commonly used likelihood functions in the latent factor collaborative filtering literature and show favorable results. Finally, we identify the pros and cons of employing a principled Bayesian inference approach and characterize settings where it provides the most significant improvements.
Generative session-based recommendation
Authors: Zhidan Wang, Wenwen Ye, Xu Chen, Wenqiang Zhang, Zhenlei Wang, Lixin Zou, Weidong Liu
Venue: Conference WWW 2022
Year: 2022

Session-based recommendation has recently attracted increasing attention from both industry and academic communities. Previous models mostly focus on designing different models to fit the observed data, which can be quite sparse in real-world scenarios. To alleviate this problem, in this paper, we propose a novel generative session-based recommendation framework. The main building block of our idea is to develop a generator to simulate user sequential behaviors, which are leveraged to train and improve the target sequential recommender model. In order to generate high quality samples, we consider two aspects: (1) the rationality as a sequence of user behaviors, and (2) the informativeness for training the target model. To satisfy these requirements, we design a doubly adversarial network. The first adversarial module aims to make the generated samples conform to the underlying patterns of the real user sequential preference (rationality requirement). The second adversarial module is targeted at widening the model experiences by generating samples which can induce larger model losses (informativeness requirement). In our model, the samples are generated based on a reinforcement learning strategy, where the reward is related with both of the above aspects. In order to stable the training process, we introduce a self-paced regularizer to learn the agent in an easy-to-hard manner. We conduct extensive experiments based on real-world datasets to demonstrate the effectiveness of our model.
Enhancing recommendation accuracy and diversity with box embedding: A universal framework
Authors: Cheng Wu, Shaoyun Shi, Chaokun Wang, Ziyang Liu, Wang Peng, Wenjin Wu, Dongying Kong, Han Li, Kun Gai
Venue: Conference WWW 2024
Year: 2024

Recommender systems have emerged as an indispensable mean to meet personalized interests of users and alleviate information overload. Despite the great success, accuracy-oriented recommendation models are creating information cocoons, i.e., it is becoming increasingly difficult for users to see other items they might be interested in. Although recent studies start paying attention to enhancing recommendation diversity, models based on point embedding fail to describe the range of user preferences and item features well, which is essential for diversified matching. To this end, we propose LCD-UC , a novel List-Check-Decide framework with UnCertainty masking based on box embedding to improve recommendation diversity with recommendation accuracy maintained. Specifically, LCD-UC creates hypercubes to represent users and items using box embedding for high model flexibility and expressiveness. Then, a hypercube similarity scoring function is designed to measure the similarity between hypercubes representing users and items. To make a balance between the accuracy and diversity of recommendations and achieve personalized diversity needs, we further develop a user-item pairwise attention mechanism as well as a user uncertainty masking mechanism in LCD-UC. Besides, we present two new metrics for better evaluation on recommendation diversity, which address the issue that existing metrics only consider the coverage of categories while ignore the frequency of categories. The extensive experiments on three real-world datasets show that LCD-UC can improve both recommendation accuracy and diversity over three base models, and is superior to six state-of-the-art recommendation models. An online 10-day AB test also demonstrates that LCD-UC can improve the performance of a real-world advertising system.
Autosvd++: An efficient hybrid collaborative filtering model via contractive auto-encoders
Authors: Shuai Zhang, Lina Yao, Xiwei Xu
Venue: Conference SIGIR 2017
Year: 2017

Collaborative filtering (CF) has been successfully used to provide users with personalized products and services. However, dealing with the increasing sparseness of user-item matrix still remains a challenge. To tackle such issue, hybrid CF such as combining with content based filtering and leveraging side information of users and items has been extensively studied to enhance performance. However, most of these approaches depend on hand-crafted feature engineering, which is usually noise-prone and biased by different feature extraction and selection schemes. In this paper, we propose a new hybrid model by generalizing contractive auto-encoder paradigm into matrix factorization framework with good scalability and computational efficiency, which jointly models content information as representations of effectiveness and compactness, and leverage implicit user feedback to make accurate recommendations. Extensive experiments conducted over three large-scale real datasets indicate the proposed approach outperforms the compared methods for item recommendation.