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With the growing prevalence of generative artificial intelligence (AI), an increasing amount of content is no longer exclusively generated by humans but by generative AI models with human guidance. This shift presents notable challenges for the delineation of originality due to the varying degrees of human contribution in AI-assisted works. This study raises the research question of measuring human contribution in AI-assisted content generation and introduces a framework to address this question that is grounded in information theory. By calculating mutual information between human input and AI-assisted output relative to self-information of AI-assisted output, we quantify the proportional information contribution of humans in content generation. Our experimental results demonstrate that the proposed measure effectively discriminates between varying degrees of human contribution across multiple creative domains. We hope that this work lays a foundation for measuring human contributions in AI-assisted content generation in the era of generative AI.

Accurately recommending personalized candidate news articles to users has always been the core challenge of news recommendation system. News recommendations often require modeling of user interests to match candidate news. Recent efforts have primarily focused on extract local subgraph information, the lack of a comprehensive global news graph extraction has hindered the ability to utilize global news information collaboratively among similar users. To overcome these limitations, we propose an effective and efficient Long Interest Chain Modeling for News Recommendation(LICM), which combines neighbor interest with long-chain interest distilled from a global news click graph based on the collaborative of similar users to enhance news recommendation. For a global news graph based on the click history of all users, long chain interest generated from it can better utilize the high-dimensional information within it, enhancing the effectiveness of collaborative recommendations. We therefore design a comprehensive selection mechanism and interest encoder to obtain long-chain interest from the global graph. Finally, we use a gated network to integrate long-chain information with neighbor information to achieve the final user representation. Experiment results on real-world datasets validate the effectiveness and efficiency of our model to improve the performance of news recommendation.

Federated learning (FL) is an important technique for learning models from decentralized data in a privacy-preserving way. Existing FL methods usually uniformly sample clients for local model learning in each round. However, different clients may have significantly different data sizes, and the clients with more data cannot have more opportunities to contribute to model training, which may lead to inferior performance. In this paper, instead of client uniform sampling, we propose a novel data uniform sampling strategy for federated learning (FedSampling), which can effectively improve the performance of federated learning especially when client data size distribution is highly imbalanced across clients. In each federated learning round, local data on each client is randomly sampled for local model learning according to a probability based on the server desired sample size and the total sample size on all available clients. Since the data size on each client is privacy-sensitive, we propose a privacy-preserving way to estimate the total sample size with a differential privacy guarantee. Experiments on four benchmark datasets show that FedSampling can effectively improve the performance of federated learning., Comment: IJCAI 2023

Vertical federated learning (VFL) is a privacy-preserving machine learning paradigm that can learn models from features distributed on different platforms in a privacy-preserving way. Since in real-world applications the data may contain bias on fairness-sensitive features (e.g., gender), VFL models may inherit bias from training data and become unfair for some user groups. However, existing fair machine learning methods usually rely on the centralized storage of fairness-sensitive features to achieve model fairness, which are usually inapplicable in federated scenarios. In this paper, we propose a fair vertical federated learning framework (FairVFL), which can improve the fairness of VFL models. The core idea of FairVFL is to learn unified and fair representations of samples based on the decentralized feature fields in a privacy-preserving way. Specifically, each platform with fairness-insensitive features first learns local data representations from local features. Then, these local representations are uploaded to a server and aggregated into a unified representation for the target task. In order to learn a fair unified representation, we send it to each platform storing fairness-sensitive features and apply adversarial learning to remove bias from the unified representation inherited from the biased data. Moreover, for protecting user privacy, we further propose a contrastive adversarial learning method to remove private information from the unified representation in server before sending it to the platforms keeping fairness-sensitive features. Experiments on three real-world datasets validate that our method can effectively improve model fairness with user privacy well-protected.

Federated learning (FL) enables multiple clients to collaboratively train models without sharing their local data, and becomes an important privacy-preserving machine learning framework. However, classical FL faces serious security and robustness problem, e.g., malicious clients can poison model updates and at the same time claim large quantities to amplify the impact of their model updates in the model aggregation. Existing defense methods for FL, while all handling malicious model updates, either treat all quantities benign or simply ignore/truncate the quantities of all clients. The former is vulnerable to quantity-enhanced attack, while the latter leads to sub-optimal performance since the local data on different clients is usually in significantly different sizes. In this paper, we propose a robust quantity-aware aggregation algorithm for federated learning, called FedRA, to perform the aggregation with awareness of local data quantities while being able to defend against quantity-enhanced attacks. More specifically, we propose a method to filter malicious clients by jointly considering the uploaded model updates and data quantities from different clients, and performing quantity-aware weighted averaging on model updates from remaining clients. Moreover, as the number of malicious clients participating in the federated learning may dynamically change in different rounds, we also propose a malicious client number estimator to predict how many suspicious clients should be filtered in each round. Experiments on four public datasets demonstrate the effectiveness of our FedRA method in defending FL against quantity-enhanced attacks.

Contrastive learning is widely used for recommendation model learning, where selecting representative and informative negative samples is critical. Existing methods usually focus on centralized data, where abundant and high-quality negative samples are easy to obtain. However, centralized user data storage and exploitation may lead to privacy risks and concerns, while decentralized user data on a single client can be too sparse and biased for accurate contrastive learning. In this paper, we propose a federated contrastive learning method named FedCL for privacy-preserving recommendation, which can exploit high-quality negative samples for effective model training with privacy well protected. We first infer user embeddings from local user data through the local model on each client, and then perturb them with local differential privacy (LDP) before sending them to a central server for hard negative sampling. Since individual user embedding contains heavy noise due to LDP, we propose to cluster user embeddings on the server to mitigate the influence of noise, and the cluster centroids are used to retrieve hard negative samples from the item pool. These hard negative samples are delivered to user clients and mixed with the observed negative samples from local data as well as in-batch negatives constructed from positive samples for federated model training. Extensive experiments on four benchmark datasets show FedCL can empower various recommendation methods in a privacy-preserving way.

User modeling is important for news recommendation. Existing methods usually first encode user's clicked news into news embeddings independently and then aggregate them into user embedding. However, the word-level interactions across different clicked news from the same user, which contain rich detailed clues to infer user interest, are ignored by these methods. In this paper, we propose a fine-grained and fast user modeling framework (FUM) to model user interest from fine-grained behavior interactions for news recommendation. The core idea of FUM is to concatenate the clicked news into a long document and transform user modeling into a document modeling task with both intra-news and inter-news word-level interactions. Since vanilla transformer cannot efficiently handle long document, we apply an efficient transformer named Fastformer to model fine-grained behavior interactions. Extensive experiments on two real-world datasets verify that FUM can effectively and efficiently model user interest for news recommendation., Comment: SIGIR 2022

News recommendation aims to match news with personalized user interest. Existing methods for news recommendation usually model user interest from historical clicked news without the consideration of candidate news. However, each user usually has multiple interests, and it is difficult for these methods to accurately match a candidate news with a specific user interest. In this paper, we present a candidate-aware user modeling method for personalized news recommendation, which can incorporate candidate news into user modeling for better matching between candidate news and user interest. We propose a candidate-aware self-attention network that uses candidate news as clue to model candidate-aware global user interest. In addition, we propose a candidate-aware CNN network to incorporate candidate news into local behavior context modeling and learn candidate-aware short-term user interest. Besides, we use a candidate-aware attention network to aggregate previously clicked news weighted by their relevance with candidate news to build candidate-aware user representation. Experiments on real-world datasets show the effectiveness of our method in improving news recommendation performance., Comment: SIGIR 2022

News recommendation aims to help online news platform users find their preferred news articles. Existing news recommendation methods usually learn models from historical user behaviors on news. However, these behaviors are usually biased on news providers. Models trained on biased user data may capture and even amplify the biases on news providers, and are unfair for some minority news providers. In this paper, we propose a provider fairness-aware news recommendation framework (named ProFairRec), which can learn news recommendation models fair for different news providers from biased user data. The core idea of ProFairRec is to learn provider-fair news representations and provider-fair user representations to achieve provider fairness. To learn provider-fair representations from biased data, we employ provider-biased representations to inherit provider bias from data. Provider-fair and -biased news representations are learned from news content and provider IDs respectively, which are further aggregated to build fair and biased user representations based on user click history. All of these representations are used in model training while only fair representations are used for user-news matching to achieve fair news recommendation. Besides, we propose an adversarial learning task on news provider discrimination to prevent provider-fair news representation from encoding provider bias. We also propose an orthogonal regularization on provider-fair and -biased representations to better reduce provider bias in provider-fair representations. Moreover, ProFairRec is a general framework and can be applied to different news recommendation methods. Extensive experiments on a public dataset verify that our ProFairRec approach can effectively improve the provider fairness of many existing methods and meanwhile maintain their recommendation accuracy., Comment: SIGIR 2022

Ensemble knowledge distillation can extract knowledge from multiple teacher models and encode it into a single student model. Many existing methods learn and distill the student model on labeled data only. However, the teacher models are usually learned on the same labeled data, and their predictions have high correlations with groudtruth labels. Thus, they cannot provide sufficient knowledge complementary to task labels for student teaching. Distilling on unseen unlabeled data has the potential to enhance the knowledge transfer from the teachers to the student. In this paper, we propose a unified and effective ensemble knowledge distillation method that distills a single student model from an ensemble of teacher models on both labeled and unlabeled data. Since different teachers may have diverse prediction correctness on the same sample, on labeled data we weight the predictions of different teachers according to their correctness. In addition, we weight the distillation loss based on the overall prediction correctness of the teacher ensemble to distill high-quality knowledge. On unlabeled data, there is no groundtruth to evaluate prediction correctness. Fortunately, the disagreement among teachers is an indication of sample hardness, and thereby we weight the distillation loss based on teachers' disagreement to emphasize knowledge distillation on important samples. Extensive experiments on four datasets show the effectiveness of our proposed ensemble distillation method.

Single-tower models are widely used in the ranking stage of news recommendation to accurately rank candidate news according to their fine-grained relatedness with user interest indicated by user behaviors. However, these models can easily inherit the biases related to users' sensitive attributes (e.g., demographics) encoded in training click data, and may generate recommendation results that are unfair to users with certain attributes. In this paper, we propose FairRank, which is a fairness-aware single-tower ranking framework for news recommendation. Since candidate news selection can be biased, we propose to use a shared candidate-aware user model to match user interest with a real displayed candidate news and a random news, respectively, to learn a candidate-aware user embedding that reflects user interest in candidate news and a candidate-invariant user embedding that indicates intrinsic user interest. We apply adversarial learning to both of them to reduce the biases brought by sensitive user attributes. In addition, we use a KL loss to regularize the attribute labels inferred from the two user embeddings to be similar, which can make the model capture less candidate-aware bias information. Extensive experiments on two datasets show that FairRank can improve the fairness of various single-tower news ranking models with minor performance losses.

Diversity is an important factor in providing high-quality personalized news recommendations. However, most existing news recommendation methods only aim to optimize recommendation accuracy while ignoring diversity. Reranking is a widely used post-processing technique to promote the diversity of top recommendation results. However, the recommendation model is not perfect and errors may be propagated and amplified in a cascaded recommendation algorithm. In addition, the recommendation model itself is not diversity-aware, making it difficult to achieve a good tradeoff between recommendation accuracy and diversity. In this paper, we propose a news recommendation approach named LeaDivRec, which is a fully learnable model that can generate diversity-aware news recommendations in an end-to-end manner. Different from existing news recommendation methods that are usually based on point- or pair-wise ranking, in LeaDivRec we propose a more effective list-wise news recommendation model. More specifically, we propose a permutation Transformer to consider the relatedness between candidate news and meanwhile can learn different representations for similar candidate news to help improve recommendation diversity. We also propose an effective list-wise training method to learn accurate ranking models. In addition, we propose a diversity-aware regularization method to further encourage the model to make controllable diversity-aware recommendations. Extensive experiments on two real-world datasets validate the effectiveness of our approach in balancing recommendation accuracy and diversity.

Adversarial learning is a widely used technique in fair representation learning to remove the biases on sensitive attributes from data representations. It usually requires to incorporate the sensitive attribute labels as prediction targets. However, in many scenarios the sensitive attribute labels of many samples can be unknown, and it is difficult to train a strong discriminator based on the scarce data with observed attribute labels, which may lead to generate unfair representations. In this paper, we propose a semi-supervised fair representation learning approach based on adversarial variational autoencoder, which can reduce the dependency of adversarial fair models on data with labeled sensitive attributes. More specifically, we use a bias-aware model to capture inherent bias information on sensitive attribute by accurately predicting sensitive attributes from input data, and we use a bias-free model to learn debiased fair representations by using adversarial learning to remove bias information from them. The hidden representations learned by the two models are regularized to be orthogonal. In addition, the soft labels predicted by the two models are further integrated into a semi-supervised variational autoencoder to reconstruct the input data, and we apply an additional entropy regularization to encourage the attribute labels inferred from the bias-free model to be high-entropy. In this way, the bias-aware model can better capture attribute information while the bias-free model is less discriminative on sensitive attributes if the input data is well reconstructed. Extensive experiments on two datasets for different tasks validate that our approach can achieve good representation learning fairness under limited data with sensitive attribute labels.

Big models are widely used by online recommender systems to boost recommendation performance. They are usually learned on historical user behavior data to infer user interest and predict future user behaviors (e.g., clicks). In fact, the behaviors of heavy users with more historical behaviors can usually provide richer clues than cold users in interest modeling and future behavior prediction. Big models may favor heavy users by learning more from their behavior patterns and bring unfairness to cold users. In this paper, we study whether big recommendation models are fair to cold users. We empirically demonstrate that optimizing the overall performance of big recommendation models may lead to unfairness to cold users in terms of performance degradation. To solve this problem, we propose a BigFair method based on self-distillation, which uses the model predictions on original user data as a teacher to regularize predictions on augmented data with randomly dropped user behaviors, which can encourage the model to fairly capture interest distributions of heavy and cold users. Experiments on two datasets show that BigFair can effectively improve the performance fairness of big recommendation models on cold users without harming the performance on heavy users.

News recommendation is a core technique used by many online news platforms. Recommending high-quality news to users is important for keeping good user experiences and news platforms' reputations. However, existing news recommendation methods mainly aim to optimize news clicks while ignoring the quality of news they recommended, which may lead to recommending news with uninformative content or even clickbaits. In this paper, we propose a quality-aware news recommendation method named QualityRec that can effectively improve the quality of recommended news. In our approach, we first propose an effective news quality evaluation method based on the distributions of users' reading dwell time on news. Next, we propose to incorporate news quality information into user interest modeling by designing a content-quality attention network to select clicked news based on both news semantics and qualities. We further train the recommendation model with an auxiliary news quality prediction task to learn quality-aware recommendation model, and we add a recommendation quality regularization loss to encourage the model to recommend higher-quality news. Extensive experiments on two real-world datasets show that QualityRec can effectively improve the overall quality of recommended news and reduce the recommendation of low-quality news, with even slightly better recommendation accuracy.

Effectively finetuning pretrained language models (PLMs) is critical for their success in downstream tasks. However, PLMs may have risks in overfitting the pretraining tasks and data, which usually have gap with the target downstream tasks. Such gap may be difficult for existing PLM finetuning methods to overcome and lead to suboptimal performance. In this paper, we propose a very simple yet effective method named NoisyTune to help better finetune PLMs on downstream tasks by adding some noise to the parameters of PLMs before fine-tuning. More specifically, we propose a matrix-wise perturbing method which adds different uniform noises to different parameter matrices based on their standard deviations. In this way, the varied characteristics of different types of parameters in PLMs can be considered. Extensive experiments on both GLUE English benchmark and XTREME multilingual benchmark show NoisyTune can consistently empower the finetuning of different PLMs on different downstream tasks., Comment: ACL 2022

Vertical federated learning (VFL) aims to train models from cross-silo data with different feature spaces stored on different platforms. Existing VFL methods usually assume all data on each platform can be used for model training. However, due to the intrinsic privacy risks of federated learning, the total amount of involved data may be constrained. In addition, existing VFL studies usually assume only one platform has task labels and can benefit from the collaboration, making it difficult to attract other platforms to join in the collaborative learning. In this paper, we study the platform collaboration problem in VFL under privacy constraint. We propose to incent different platforms through a reciprocal collaboration, where all platforms can exploit multi-platform information in the VFL framework to benefit their own tasks. With limited privacy budgets, each platform needs to wisely allocate its data quotas for collaboration with other platforms. Thereby, they naturally form a multi-party game. There are two core problems in this game, i.e., how to appraise other platforms' data value to compute game rewards and how to optimize policies to solve the game. To evaluate the contributions of other platforms' data, each platform offers a small amount of "deposit" data to participate in the VFL. We propose a performance estimation method to predict the expected model performance when involving different amount combinations of inter-platform data. To solve the game, we propose a platform negotiation method that simulates the bargaining among platforms and locally optimizes their policies via gradient descent. Extensive experiments on two real-world datasets show that our approach can effectively facilitate the collaborative exploitation of multi-platform data in VFL under privacy restrictions., Comment: Submitted to KDD 2022

Federated learning (FL) is a feasible technique to learn personalized recommendation models from decentralized user data. Unfortunately, federated recommender systems are vulnerable to poisoning attacks by malicious clients. Existing recommender system poisoning methods mainly focus on promoting the recommendation chances of target items due to financial incentives. In fact, in real-world scenarios, the attacker may also attempt to degrade the overall performance of recommender systems. However, existing general FL poisoning methods for degrading model performance are either ineffective or not concealed in poisoning federated recommender systems. In this paper, we propose a simple yet effective and covert poisoning attack method on federated recommendation, named FedAttack. Its core idea is using globally hardest samples to subvert model training. More specifically, the malicious clients first infer user embeddings based on local user profiles. Next, they choose the candidate items that are most relevant to the user embeddings as hardest negative samples, and find the candidates farthest from the user embeddings as hardest positive samples. The model gradients inferred from these poisoned samples are then uploaded to the server for aggregation and model update. Since the behaviors of malicious clients are somewhat similar to users with diverse interests, they cannot be effectively distinguished from normal clients by the server. Extensive experiments on two benchmark datasets show that FedAttack can effectively degrade the performance of various federated recommender systems, meanwhile cannot be effectively detected nor defended by many existing methods., Comment: Submitted to KDD 2022