OpenRaiser/Intern-Atlas huggingface dataset:
Make Fairness More Fair: Fair Item Utility Estimation and Exposure Re-Distribution
Authors: Jiayin Wang, Weizhi Ma, Jiayu Li 0001, Hongyu Lu, Min Zhang 0006, Biao Li 0002, Yiqun Liu 0001, Peng Jiang 0002, Shaoping Ma
Venue: Conference KDD 2022
Year: 2022

The item fairness issue has become one of the significant concerns with the development of recommender systems in recent years, focusing on whether items' exposures are consistent with their utilities. So the measurement of item unfairness depends on the modeling of item utility, and most previous approaches estimated item utility simply based on user-item interaction logs in recommender systems. The Click-through rate (CTR) is the most popular one. However, we argue that these types of item utilities (named observed utility here) measurements may result in unfair exposures of items. The number of exposure for each item is uneven, and recommendation methods select the exposure audiences (users).
Deconfounding duration bias in watch-time prediction for video recommendation
Authors: Ruohan Zhan, Changhua Pei, Qiang Su, Jianfeng Wen, Xueliang Wang, Guanyu Mu, Dong Zheng, Peng Jiang 0002, Kun Gai
Venue: Conference KDD 2022
Year: 2022

Watch-time prediction remains to be a key factor in reinforcing user engagement via video recommendations. It has become increasingly important given the ever-growing popularity of online videos. However, prediction of watch time not only depends on the match between the user and the video but is often mislead by the duration of the video itself. With the goal of improving watch time, recommendation is always biased towards videos with long duration. Models trained on this imbalanced data face the risk of bias amplification, which misguides platforms to over-recommend videos with long duration but overlook the underlying user interests. This paper presents the first work to study duration bias in watch-time prediction for video recommendation. We employ a causal graph illuminating that duration is a confounding factor that concurrently affects video exposure and watch-time prediction -- the first effect on video causes the bias issue and should be eliminated, while the second effect on watch time originates from video intrinsic characteristics and should be preserved. To remove the undesired bias but leverage the natural effect, we propose a Duration Deconfounded Quantile-based (D2Q) watch-time prediction framework, which allows for scalability to perform on industry production systems. Through extensive offline evaluation and live experiments, we showcase the effectiveness of this duration-deconfounding framework by significantly outperforming the state-of-the-art baselines. We have fully launched our approach on Kuaishou App, which has substantially improved real-time video consumption due to more accurate watch-time predictions.
From noisy prediction to true label: Noisy prediction calibration via generative model
Authors: HeeSun Bae, Seungjae Shin, Byeonghu Na, JoonHo Jang, Kyungwoo Song, Il-Chul Moon
Venue: Conference ICML 2022
Year: 2022

Noisy labels are inevitable yet problematic in machine learning society. It ruins the generalization of a classifier by making the classifier over-fitted to noisy labels. Existing methods on noisy label have focused on modifying the classifier during the training procedure. It has two potential problems. First, these methods are not applicable to a pre-trained classifier without further access to training. Second, it is not easy to train a classifier and regularize all negative effects from noisy labels, simultaneously. We suggest a new branch of method, Noisy Prediction Calibration (NPC) in learning with noisy labels. Through the introduction and estimation of a new type of transition matrix via generative model, NPC corrects the noisy prediction from the pre-trained classifier to the true label as a post-processing scheme. We prove that NPC theoretically aligns with the transition matrix based methods. Yet, NPC empirically provides more accurate pathway to estimate true label, even without involvement in classifier learning. Also, NPC is applicable to any classifier trained with noisy label methods, if training instances and its predictions are available. Our method, NPC, boosts the classification performances of all baseline models on both synthetic and real-world datasets. The implemented code is available at https://github.com/BaeHeeSun/NPC.
Curriculum meta-learning for next poi recommendation
Authors: Yudong Chen, Xin Wang, Miao Fan, Jizhou Huang, Shengwen Yang, Wenwu Zhu
Venue: Conference KDD 2021
Year: 2021

Next point-of-interest (POI) recommendation is a hot research field where a recent emerging scenario, next POI to search recommendation, has been deployed in many online map services such as Baidu Maps. One of the key issues in this scenario is providing satisfactory recommendation services for cold-start cities with a limited number of user-POI interactions, which requires transferring the knowledge hidden in rich data from many other cities to these cold-start cities. Existing literature either does not consider the city-transfer issue or cannot simultaneously tackle the data sparsity and pattern diversity issues among various users in multiple cities. To address these issues, we explore city-transfer next POI to search recommendation that transfers the knowledge from multiple cities with rich data to cold-start cities with scarce data. We propose a novel Curriculum Hardness Aware Meta-Learning (CHAML) framework, which incorporates hard sample mining and curriculum learning into a meta-learning paradigm. Concretely, the CHAML framework considers both city-level and user-level hardness to enhance the conditional sampling during meta training, and uses an easy-to-hard curriculum for the city-sampling pool to help the meta-learner converge to a better state. Extensive experiments on two real-world map search datasets from Baidu Maps demonstrate the superiority of CHAML framework.
Multimodal entity linking with gated hierarchical fusion and contrastive training
Authors: Peng Wang, Jiangheng Wu, Xiaohang Chen
Venue: Conference SIGIR 2022
Year: 2022

Previous entity linking methods in knowledge graphs (KGs) mostly link the textual mentions to corresponding entities. However, they have deficiencies in processing numerous multimodal data, when the text is too short to provide enough context. Consequently, we conceive the idea of introducing valuable information of other modalities, and propose a novel multimodal entity linking method with gated hierarchical multimodal fusion and contrastive training (GHMFC). Firstly, in order to discover the fine-grained inter-modal correlations, GHMFC extracts the hierarchical features of text and visual co-attention through the multi-modal co-attention mechanism: textual-guided visual attention and visual-guided textual attention. The former attention obtains weighted visual features under the guidance of textual information. In contrast, the latter attention produces weighted textual features under the guidance of visual information. Afterwards, gated fusion is used to evaluate the importance of hierarchical features of different modalities and integrate them into the final multimodal representations of mentions. Subsequently, contrastive training with two types of contrastive losses is designed to learn more generic multimodal features and reduce noise. Finally, the linking entities are selected by calculating the cosine similarity between representations of mentions and entities in KGs. To evaluate the proposed method, this paper releases two new open multimodal entity linking datasets: WikiMEL and Richpedia-MEL. Experimental results demonstrate that GHMFC can learn meaningful multimodal representation and significantly outperforms most of the baseline methods.
A Contrastive Sharing Model for Multi-Task Recommendation
Authors: Ting Bai, Yudong Xiao, Bin Wu, Guojun Yang, Hongyong Yu, Jian-Yun Nie
Venue: Conference WWW 2022
Year: 2022

Multi-Task Learning (MTL) has attracted increasing attention in recommender systems. A crucial challenge in MTL is to learn suitable shared parameters among tasks and to avoid negative transfer of information. The most recent sparse sharing models use independent parameter masks, which only activate useful parameters for a task, to choose the useful subnet for each task. However, as all the subnets are optimized in parallel for each task independently, it is faced with the problem of conflict between parameter gradient updates (i.e, parameter conflict problem). To address this challenge, we propose a novel Contrastive Sharing Recommendation model in MTL learning (CSRec). Each task in CSRec learns from the subnet by the independent parameter mask as in sparse sharing models, but a contrastive mask is carefully designed to evaluate the contribution of the parameter to a specific task. The conflict parameter will be optimized relying more on the task which is more impacted by the parameter. Besides, we adopt an alternating training strategy in CSRec, making it possible to self-adaptively update the conflict parameters by fair competitions. We conduct extensive experiments on three real-world large scale datasets, i.e., Tencent Kandian, Ali-CCP and Census-income, showing better effectiveness of our model over state-of-the-art methods for both offline and online MTL recommendation scenarios.
Field-weighted factorization machines for click-through rate prediction in display advertising
Authors: Junwei Pan, Jian Xu, Alfonso Lobos Ruiz, Wenliang Zhao, Shengjun Pan, Yu Sun, Quan Lu
Venue: Conference WWW 2018
Year: 2018

Click-through rate (CTR) prediction is a critical task in online display advertising. The data involved in CTR prediction are typically multi-field categorical data, i.e., every feature is categorical and belongs to one and only one field. One of the interesting characteristics of such data is that features from one field often interact differently with features from different other fields. Recently, Field-aware Factorization Machines (FFMs) have been among the best performing models for CTR prediction by explicitly modeling such difference. However, the number of parameters in FFMs is in the order of feature number times field number, which is unacceptable in the real-world production systems. In this paper, we propose Field-weighted Factorization Machines (FwFMs) to model the different feature interactions between different fields in a much more memory-efficient way. Our experimental evaluations show that FwFMs can achieve competitive prediction performance with only as few as 4% parameters of FFMs. When using the same number of parameters, FwFMs can bring 0.92% and 0.47% AUC lift over FFMs on two real CTR prediction data sets.
Flipout: Efficient pseudo-independent weight perturbations on mini-batches
Authors: Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger B. Grosse
Venue: Conference ICLR 2018
Year: 2018

Stochastic neural net weights are used in a variety of contexts, including regularization, Bayesian neural nets, exploration in reinforcement learning, and evolution strategies. Unfortunately, due to the large number of weights, all the examples in a mini-batch typically share the same weight perturbation, thereby limiting the variance reduction effect of large mini-batches. We introduce flipout, an efficient method for decorrelating the gradients within a mini-batch by implicitly sampling pseudo-independent weight perturbations for each example. Empirically, flipout achieves the ideal linear variance reduction for fully connected networks, convolutional networks, and RNNs. We find significant speedups in training neural networks with multiplicative Gaussian perturbations. We show that flipout is effective at regularizing LSTMs, and outperforms previous methods. Flipout also enables us to vectorize evolution strategies: in our experiments, a single GPU with flipout can handle the same throughput as at least 40 CPU cores using existing methods, equivalent to a factor-of-4 cost reduction on Amazon Web Services.
Recommendations to boost content spread in social networks
Authors: Vineet Chaoji, Sayan Ranu, Rajeev Rastogi, Rushi Bhatt
Venue: Conference WWW 2012
Year: 2012

Content sharing in social networks is a powerful mechanism for discovering content on the Internet. The degree to which content is disseminated within the network depends on the connectivity relationships among network nodes. Existing schemes for recommending connections in social networks are based on the number of common neighbors, similarity of user profiles, etc. However, such similarity-based connections do not consider the amount of content discovered.
Backdoor Defense via Decoupling the Training Process
Authors: Kunzhe Huang, Yiming Li 0004, Baoyuan Wu, Zhan Qin, Kui Ren 0001
Venue: Conference ICLR 2022
Year: 2022

Recent studies have revealed that deep neural networks (DNNs) are vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by poisoning a few training samples. The attacked model behaves normally on benign samples, whereas its prediction will be maliciously changed when the backdoor is activated. We reveal that poisoned samples tend to cluster together in the feature space of the attacked DNN model, which is mostly due to the end-to-end supervised training paradigm. Inspired by this observation, we propose a novel backdoor defense via decoupling the original end-to-end training process into three stages. Specifically, we first learn the backbone of a DNN model via \emph{self-supervised learning} based on training samples without their labels. The learned backbone will map samples with the same ground-truth label to similar locations in the feature space. Then, we freeze the parameters of the learned backbone and train the remaining fully connected layers via standard training with all (labeled) training samples. Lastly, to further alleviate side-effects of poisoned samples in the second stage, we remove labels of some `low-credible' samples determined based on the learned model and conduct a \emph{semi-supervised fine-tuning} of the whole model. Extensive experiments on multiple benchmark datasets and DNN models verify that the proposed defense is effective in reducing backdoor threats while preserving high accuracy in predicting benign samples. Our code is available at \url{https://github.com/SCLBD/DBD}.
Neural attention distillation: Erasing backdoor triggers from deep neural networks
Authors: Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li 0026, Xingjun Ma
Venue: Conference ICLR 2021
Year: 2021

Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a training time attack that injects a trigger pattern into a small proportion of training data so as to control the model's prediction at the test time. Backdoor attacks are notably dangerous since they do not affect the model's performance on clean examples, yet can fool the model to make incorrect prediction whenever the trigger pattern appears during testing. In this paper, we propose a novel defense framework Neural Attention Distillation (NAD) to erase backdoor triggers from backdoored DNNs. NAD utilizes a teacher network to guide the finetuning of the backdoored student network on a small clean subset of data such that the intermediate-layer attention of the student network aligns with that of the teacher network. The teacher network can be obtained by an independent finetuning process on the same clean subset. We empirically show, against 6 state-of-the-art backdoor attacks, NAD can effectively erase the backdoor triggers using only 5\% clean training data without causing obvious performance degradation on clean examples. Code is available in https://github.com/bboylyg/NAD.
An embarrassingly simple approach for trojan attack in deep neural networks
Authors: Ruixiang Tang, Mengnan Du, Ninghao Liu 0001, Fan Yang 0023, Xia Hu 0001
Venue: Conference KDD 2020
Year: 2020

With the widespread use of deep neural networks (DNNs) in high-stake applications, the security problem of the DNN models has received extensive attention. In this paper, we investigate a specific security problem called trojan attack, which aims to attack deployed DNN systems relying on the hidden trigger patterns inserted by malicious hackers. We propose a training-free attack approach which is different from previous work, in which trojaned behaviors are injected by retraining model on a poisoned dataset. Specifically, we do not change parameters in the original model but insert a tiny trojan module (TrojanNet) into the target model. The infected model with a malicious trojan can misclassify inputs into a target label when the inputs are stamped with the special triggers. The proposed TrojanNet has several nice properties including (1) it activates by tiny trigger patterns and keeps silent for other signals, (2) it is model-agnostic and could be injected into most DNNs, dramatically expanding its attack scenarios, and (3) the training-free mechanism saves massive training efforts comparing to conventional trojan attack methods. The experimental results show that TrojanNet can inject the trojan into all labels simultaneously (all-label trojan attack) and achieves 100% attack success rate without affecting model accuracy on original tasks. Experimental analysis further demonstrates that state-of-the-art trojan detection algorithms fail to detect TrojanNet attack. The code is available at https://github.com/trx14/TrojanNet.
SelfKG: Self-supervised entity alignment in knowledge graphs
Authors: Xiao Liu, Haoyun Hong, Xinghao Wang, Zeyi Chen, Evgeny Kharlamov, Yuxiao Dong, Jie Tang
Venue: Conference WWW 2022
Year: 2022

Entity alignment, aiming to identify equivalent entities across different knowledge graphs (KGs), is a fundamental problem for constructing Web-scale KGs. Over the course of its development, the label supervision has been considered necessary for accurate alignments. Inspired by the recent progress of self-supervised learning, we explore the extent to which we can get rid of supervision for entity alignment. Commonly, the label information (positive entity pairs) is used to supervise the process of pulling the aligned entities in each positive pair closer. However, our theoretical analysis suggests that the learning of entity alignment can actually benefit more from pushing unlabeled negative pairs far away from each other than pulling labeled positive pairs close. By leveraging this discovery, we develop the self-supervised learning objective for entity alignment. We present SelfKG with efficient strategies to optimize this objective for aligning entities without label supervision. Extensive experiments on benchmark datasets demonstrate that SelfKG without supervision can match or achieve comparable results with state-of-the-art supervised baselines. The performance of SelfKG suggests that self-supervised learning offers great potential for entity alignment in KGs. The code and data are available at https://github.com/THUDM/SelfKG.
Multi-modal knowledge graphs for recommender systems
Authors: Rui Sun, Xuezhi Cao, Yan Zhao, Junchen Wan, Kun Zhou, Fuzheng Zhang, Zhongyuan Wang, Kai Zheng
Venue: Conference CIKM 2020
Year: 2020

Recommender systems have shown great potential to solve the information explosion problem and enhance user experience in various online applications. To tackle data sparsity and cold start problems in recommender systems, researchers propose knowledge graphs (KGs) based recommendations by leveraging valuable external knowledge as auxiliary information. However, most of these works ignore the variety of data types (e.g., texts and images) in multi-modal knowledge graphs (MMKGs). In this paper, we propose Multi-modal Knowledge Graph Attention Network (MKGAT) to better enhance recommender systems by leveraging multi-modal knowledge. Specifically, we propose a multi-modal graph attention technique to conduct information propagation over MMKGs, and then use the resulting aggregated embedding representation for recommendation. To the best of our knowledge, this is the first work that incorporates multi-modal knowledge graph into recommender systems. We conduct extensive experiments on two real datasets from different domains, results of which demonstrate that our model MKGAT can successfully employ MMKGs to improve the quality of recommendation system.
Reinforced active entity alignment
Authors: Weixin Zeng, Xiang Zhao, Jiuyang Tang, Changjun Fan
Venue: Conference CIKM 2021
Year: 2021

Entity alignment (EA) is the task of detecting equivalent entities from different knowledge graphs (KGs). Although this problem has been intensively studied during the last few years, the majority of the state-of-the-arts heavily rely on the labeled data, which are difficult to obtain in practice. Therefore, it calls for the study of EA with scarce supervision. To resolve this issue, we put forward a reinforced active entity alignment framework to select the entities to be manually labeled with the aim of enhancing alignment performance with minimal labeling efforts. Under this framework, we further devise an unsupervised contrastive loss to contrast different views of entity representations and augment the limited supervision signals by exploiting the vast unlabeled data. We empirically evaluate our proposal on eight popular KG pairs, and the results demonstrate that our proposed model and its components consistently boost the alignment performance under scarce supervision.
Optimistic linear support and successor features as a basis for optimal policy transfer
Authors: Lucas Nunes Alegre, Ana L. C. Bazzan, Bruno C. da Silva 0001
Venue: Conference ICML 2022
Year: 2022

In many real-world applications, reinforcement learning (RL) agents might have to solve multiple tasks, each one typically modeled via a reward function. If reward functions are expressed linearly, and the agent has previously learned a set of policies for different tasks, successor features (SFs) can be exploited to combine such policies and identify reasonable solutions for new problems. However, the identified solutions are not guaranteed to be optimal. We introduce a novel algorithm that addresses this limitation. It allows RL agents to combine existing policies and directly identify optimal policies for arbitrary new problems, without requiring any further interactions with the environment. We first show (under mild assumptions) that the transfer learning problem tackled by SFs is equivalent to the problem of learning to optimize multiple objectives in RL. We then introduce an SF-based extension of the Optimistic Linear Support algorithm to learn a set of policies whose SFs form a convex coverage set. We prove that policies in this set can be combined via generalized policy improvement to construct optimal behaviors for any new linearly-expressible tasks, without requiring any additional training samples. We empirically show that our method outperforms state-of-the-art competing algorithms both in discrete and continuous domains under value function approximation.