OpenRaiser/Intern-Atlas huggingface dataset:
Self-supervised learning for large-scale item recommendations
Authors: Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, Felix X. Yu, Ting Chen, Aditya Krishna Menon, Lichan Hong, Ed H. Chi, Steve Tjoa, Jieqi (Jay) Kang, Evan Ettinger
Venue: Conference CIKM 2021
Year: 2021

Large scale recommender models find most relevant items from huge catalogs, and they play a critical role in modern search and recommendation systems. To model the input space with large-vocab categorical features, a typical recommender model learns a joint embedding space through neural networks for both queries and items from user feedback data. However, with millions to billions of items in the corpus, users tend to provide feedback for a very small set of them, causing a power-law distribution. This makes the feedback data for long-tail items extremely sparse. Inspired by the recent success in self-supervised representation learning research in both computer vision and natural language understanding, we propose a multi-task self-supervised learning (SSL) framework for large-scale item recommendations. The framework is designed to tackle the label sparsity problem by learning better latent relationship of item features. Specifically, SSL improves item representation learning as well as serving as additional regularization to improve generalization. Furthermore, we propose a novel data augmentation method that utilizes feature correlations within the proposed framework. We evaluate our framework using two real-world datasets with 500M and 1B training examples respectively. Our results demonstrate the effectiveness of SSL regularization and show its superior performance over the state-of-the-art regularization techniques. We also have already launched the proposed techniques to a web-scale commercial app-to-app recommendation system, with significant improvements top-tier business metrics demonstrated in A/B experiments on live traffic. Our online results also verify our hypothesis that our framework indeed improves model performance even more on slices that lack supervision.
Deeprec: On-device deep learning for privacy-preserving sequential recommendation in mobile commerce
Authors: Jialiang Han, Yun Ma, Qiaozhu Mei, Xuanzhe Liu
Venue: Conference WWW 2021
Year: 2021

Sequential recommendation techniques are considered to be a promising way of providing better user experience in mobile commerce by learning sequential interests within user historical interaction behaviors. However, the recently increasing focus on privacy concerns, such as the General Data Protection Regulation (GDPR), can significantly affect the deployment of state-of-the-art sequential recommendation techniques, because user behavior data are no longer allowed to be arbitrarily used without the user’s explicit permission. To address the issue, this paper proposes DeepRec, an on-device deep learning framework of mining interaction behaviors for sequential recommendation without sending any raw data or intermediate results out of the device, preserving user privacy maximally. DeepRec constructs a global model using data collected before GDPR and fine-tunes a personal model continuously on individual mobile devices using data collected after GDPR. DeepRec employs the model pruning and embedding sparsity techniques to reduce the computation and network overhead, making the model training process practical on computation-constraint mobile devices. Evaluation results show that DeepRec can achieve comparable recommendation accuracy to existing centralized recommendation approaches with small computation overhead and up to 10x reduction in network overhead.
Debiased recommendation with noisy feedback
Authors: Haoxuan Li 0001, Chunyuan Zheng 0001, Wenjie Wang 0007, Hao Wang 0049, Fuli Feng, Xiao-Hua Zhou
Venue: Conference KDD 2024
Year: 2024

Ratings of a user to most items in recommender systems are usually missing not at random (MNAR), largely because users are free to choose which items to rate. To achieve unbiased learning of the prediction model under MNAR data, three typical solutions have been proposed, including error-imputation-based (EIB), inverse-propensity-scoring (IPS), and doubly robust (DR) methods. However, these methods ignore an alternative form of bias caused by the inconsistency between the observed ratings and the users' true preferences, also known as noisy feedback or outcome measurement errors (OME), e.g., due to public opinion or low-quality data collection process. In this work, we study intersectional threats to the unbiased learning of the prediction model from data MNAR and OME in the collected data. First, we design OME-EIB, OME-IPS, and OME-DR estimators, which largely extend the existing estimators to combat OME in real-world recommendation scenarios. Next, we theoretically prove the unbiasedness and generalization bound of the proposed estimators. We further propose an alternate denoising training approach to achieve unbiased learning of the prediction model under MNAR data with OME. Extensive experiments are conducted on three real-world datasets and one semi-synthetic dataset to show the effectiveness of our proposed approaches. The code is available at https://github.com/haoxuanli-pku/KDD24-OME-DR.
Hierarchical Invariant Learning for Domain Generalization Recommendation
Authors: Zeyu Zhang 0007, Heyang Gao, Hao Yang 0045, Xu Chen 0017
Venue: Conference KDD 2023
Year: 2023

Most cross-domain recommenders require samples on target domains or source-target overlaps to carry out domain adaptation. However, in many real-world situations, target domains are lack of such knowledge. Few works discuss this problem, whose essence is domain generalization recommendation. In this paper, we figure out domain generalization recommendation with a clear symbolized definition and propose corresponding models. Moreover, we illustrate its strong connection with zero-shot recommendation, pretrained recommendation and cold-start recommendation, distinguishing it from content-based recommendation. By analyzing its properties, we propose HIRL^+ and a series of heuristic methods to solve this problem. We propose hierarchical invariant learning to expel the specific patterns in both domain-level and environment-level, and find the common patterns in generalization space. To make the division of environments flexible, fine-grained and balanced, we put forward a learnable environment assignment method. To improve the robustness against distribution shifts inside domain generalization, we present an adversarial environment refinement method. In addition, we conduct experiments on real-word datasets to verify the effectiveness of our models, and carry out further studies on the domain distance and domain diversity. To benefit the research community and promote this direction, we discuss the future of this field.
Bias mitigation for toxicity detection via sequential decisions
Authors: Lu Cheng, Ahmadreza Mosallanezhad, Yasin N. Silva, Deborah L. Hall, Huan Liu
Venue: Conference SIGIR 2022
Year: 2022

Increased social media use has contributed to the greater prevalence of abusive, rude, and offensive textual comments. Machine learning models have been developed to detect toxic comments online, yet these models tend to show biases against users with marginalized or minority identities (e.g., females and African Americans). Established research in debiasing toxicity classifiers often (1) takes a static or batch approach, assuming that all information is available and then making a one-time decision; and (2) uses a generic strategy to mitigate different biases (e.g., gender and racial biases) that assumes the biases are independent of one another. However, in real scenarios, the input typically arrives as a sequence of comments/words over time instead of all at once. Thus, decisions based on partial information must be made while additional input is arriving. Moreover, social bias is complex by nature. Each type of bias is defined within its unique context, which, consistent with intersectionality theory within the social sciences, might be correlated with the contexts of other forms of bias. In this work, we consider debiasing toxicity detection as a sequential decision-making process where different biases can be interdependent. In particular, we study debiasing toxicity detection with two aims: (1) to examine whether different biases tend to correlate with each other; and (2) to investigate how to jointly mitigate these correlated biases in an interactive manner to minimize the total amount of bias. At the core of our approach is a framework built upon theories of sequential Markov Decision Processes that seeks to maximize the prediction accuracy and minimize the bias measures tailored to individual biases. Evaluations on two benchmark datasets empirically validate the hypothesis that biases tend to be correlated and corroborate the effectiveness of the proposed sequential debiasing strategy.
Classifying racist texts using a support vector machine
Authors: Edel Greevy, Alan F. Smeaton
Venue: Conference SIGIR 2004
Year: 2004

In this poster we present an overview of the techniques we used to develop and evaluate a text categorisation system to automatically classify racist texts. Detecting racism is difficult because the presence of indicator words is insufficient to indicate racist texts, unlike some other text classification tasks. Support Vector Machines (SVM) are used to automatically categorise web pages based on whether or not they are racist. Different interpretations of what constitutes a term are taken, and in this poster we look at three representations of a web page within an SVM -- bag-of-words, bigrams and part-of-speech tags.
Same Same, But Different: Conditional Multi-Task Learning for Demographic- Specific Toxicity Detection
Authors: Soumyajit Gupta, Sooyong Lee, Maria De-Arteaga, Matthew Lease
Venue: Conference WWW 2023
Year: 2023

Algorithmic bias often arises as a result of differential subgroup validity, in which predictive relationships vary across groups. For example, in toxic language detection, comments targeting different demographic groups can vary markedly across groups. In such settings, trained models can be dominated by the relationships that best fit the majority group, leading to disparate performance. We propose framing toxicity detection as multi-task learning (MTL), allowing a model to specialize on the relationships that are relevant to each demographic group while also leveraging shared properties across groups. With toxicity detection, each task corresponds to identifying toxicity against a particular demographic group. However, traditional MTL requires labels for all tasks to be present for every data point. To address this, we propose Conditional MTL (CondMTL), wherein only training examples relevant to the given demographic group are considered by the loss function. This lets us learn group specific representations in each branch which are not cross contaminated by irrelevant labels. Results on synthetic and real data show that using CondMTL improves predictive recall over various baselines in general and for the minority demographic group in particular, while having similar overall accuracy.
Causality guided disentanglement for cross-platform hate speech detection
Authors: Paras Sheth, Raha Moraffah, Tharindu S. Kumarage, Aman Chadha, Huan Liu
Venue: Conference WSDM 2024
Year: 2024

espite their value in promoting open discourse, social media plat- forms are often exploited to spread harmful content. Current deep learning and natural language processing models used for detect- ing this harmful content rely on domain-specific terms affecting their ability to adapt to generalizable hate speech detection. This is because they tend to focus too narrowly on particular linguistic signals or the use of certain categories of words. Another signifi- cant challenge arises when platforms lack high-quality annotated data for training, leading to a need for cross-platform models that can adapt to different distribution shifts. Our research introduces a cross-platform hate speech detection model capable of being trained on one platform's data and generalizing to multiple unseen platforms. One way to achieve good generalizability across plat- forms is to disentangle the input representations into invariant and platform-dependent features. We also argue that learning causal relationships, which remain constant across diverse environments, can significantly aid in understanding invariant representations in hate speech. By disentangling input into platform-dependent fea- tures (useful for predicting hate targets) and platform-independent features (used to predict the presence of hate), we learn invariant representations resistant to distribution shifts. These features are then used to predict hate speech across unseen platforms. Our ex- tensive experiments across four platforms highlight our model's enhanced efficacy compared to existing state-of-the-art methods in detecting generalized hate speech
Utilizing temporal patterns for estimating uncertainty in interpretable early decision making
Authors: Mohamed F. Ghalwash, Vladan Radosavljevic, Zoran Obradovic
Venue: Conference KDD 2014
Year: 2014

Early classification of time series is prevalent in many time-sensitive applications such as, but not limited to, early warning of disease outcome and early warning of crisis in stock market. \textcolor{black}{ For example,} early diagnosis allows physicians to design appropriate therapeutic strategies at early stages of diseases. However, practical adaptation of early classification of time series requires an easy to understand explanation (interpretability) and a measure of confidence of the prediction results (uncertainty estimates). These two aspects were not jointly addressed in previous time series early classification studies, such that a difficult choice of selecting one of these aspects is required. In this study, we propose a simple and yet effective method to provide uncertainty estimates for an interpretable early classification method. The question we address here is "how to provide estimates of uncertainty in regard to interpretable early prediction." In our extensive evaluation on twenty time series datasets we showed that the proposed method has several advantages over the state-of-the-art method that provides reliability estimates in early classification. Namely, the proposed method is more effective than the state-of-the-art method, is simple to implement, and provides interpretable results.
Optimizing generalized Gini indices for fairness in rankings
Authors: Virginie Do, Nicolas Usunier
Venue: Conference SIGIR 2022
Year: 2022

There is growing interest in designing recommender systems that aim at being fair towards item producers or their least satisfied users. Inspired by the domain of inequality measurement in economics, this paper explores the use of generalized Gini welfare functions (GGFs) as a means to specify the normative criterion that recommender systems should optimize for. GGFs weight individuals depending on their ranks in the population, giving more weight to worse-off individuals to promote equality. Depending on these weights, GGFs minimize the Gini index of item exposure to promote equality between items, or focus on the performance on specific quantiles of least satisfied users. GGFs for ranking are challenging to optimize because they are non-differentiable. We resolve this challenge by leveraging tools from non-smooth optimization and projection operators used in differentiable sorting. We present experiments using real datasets with up to 15k users and items, which show that our approach obtains better trade-offs than the baselines on a variety of recommendation tasks and fairness criteria.
Cpfair: Personalized consumer and producer fairness re-ranking for recommender systems
Authors: Mohammadmehdi Naghiaei, Hossein A. Rahmani, Yashar Deldjoo
Venue: Conference SIGIR 2022
Year: 2022

Recently, there has been a rising awareness that when machine learning (ML) algorithms are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or economic consequences. Recommender systems are prominent examples of such ML systems that assist users in making high-stakes judgments. A common trend in the previous literature research on fairness in recommender systems is that the majority of works treat user and item fairness concerns separately, ignoring the fact that recommender systems operate in a two-sided marketplace. In this work, we present an optimization-based re-ranking approach that seamlessly integrates fairness constraints from both the consumer and producer-side in a joint objective framework. We demonstrate through large-scale experiments on 8 datasets that our proposed method is capable of improving both consumer and producer fairness without reducing overall recommendation quality, demonstrating the role algorithms may play in minimizing data biases.
Debiasing the Human-Recommender System Feedback Loop in Collaborative Filtering
Authors: Wenlong Sun, Sami Khenissi, Olfa Nasraoui, Patrick Shafto
Venue: Conference WWW 2019
Year: 2019

Recommender Systems (RSs) are widely used to help online users discover products, books, news, music, movies, courses, restaurants, etc. Because a traditional recommendation strategy always shows the most relevant items (thus with highest predicted rating), traditional RS’s are expected to make popular items become even more popular and non-popular items become even less popular which in turn further divides the haves (popular) from the have-nots (unpopular). Therefore, a major problem with RSs is that they may introduce biases affecting the exposure of items, thus creating a popularity divide of items during the feedback loop that occurs with users, and this may lead the RS to make increasingly biased recommendations over time. In this paper, we view the RS environment as a chain of events that are the result of interactions between users and the RS. Based on that, we propose several debiasing algorithms during this chain of events, and evaluate how these algorithms impact the predictive behavior of the RS, as well as trends in the popularity distribution of items over time. We also propose a novel blind-spot-aware matrix factorization (MF) algorithm to debias the RS. Results show that propensity matrix factorization achieved a certain level of debiasing of the RS while active learning combined with the propensity MF achieved a higher debiasing effect on recommendations.
Hybrid deep sequential modeling for social text-driven stock prediction
Authors: Huizhe Wu, Wei Zhang, Weiwei Shen, Jun Wang
Venue: Conference CIKM 2018
Year: 2018

In addition to only considering stocks' price series, utilizing short and instant texts from social medias like Twitter has potential to yield better stock market prediction. While some previous approaches have explored this direction, their results are still far from satisfactory due to their reliance on performance of sentiment analysis and limited capabilities of learning direct relations between target stock trends and their daily social texts. To bridge this gap, we propose a novel Cross-modal attention based Hybrid Recurrent Neural Network (CH-RNN), which is inspired by the recent proposed DA-RNN model. Specifically, CH-RNN consists of two essential modules. One adopts DA-RNN to gain stock trend representations for different stocks. The other utilizes recurrent neural network to model daily aggregated social texts. These two modules interact seamlessly by the following two manners: 1) daily representations of target stock trends from the first module are leveraged to select trend-related social texts through a cross-modal attention mechanism, and 2) representations of text sequences and trend series are further integrated. The comprehensive experiments on the real dataset we build demonstrate the effectiveness of CH-RNN and benefit of considering social texts.
An Embedding Learning Framework for Numerical Features in CTR Prediction
Authors: Huifeng Guo, Bo Chen, Ruiming Tang, Weinan Zhang, Zhenguo Li, Xiuqiang He
Venue: Conference KDD 2021
Year: 2021

Click-Through Rate (CTR) prediction is critical for industrial recommender systems, where most deep CTR models follow an Embedding \& Feature Interaction paradigm. However, the majority of methods focus on designing network architectures to better capture feature interactions while the feature embedding, especially for numerical features, has been overlooked. Existing approaches for numerical features are difficult to capture informative knowledge because of the low capacity or hard discretization based on the offline expertise feature engineering. In this paper, we propose a novel embedding learning framework for numerical features in CTR prediction (AutoDis) with high model capacity, end-to-end training and unique representation properties preserved. AutoDis consists of three core components: meta-embeddings, automatic discretization and aggregation. Specifically, we propose meta-embeddings for each numerical field to learn global knowledge from the perspective of field with a manageable number of parameters. Then the differentiable automatic discretization performs soft discretization and captures the correlations between the numerical features and meta-embeddings. Finally, distinctive and informative embeddings are learned via an aggregation function. Comprehensive experiments on two public and one industrial datasets are conducted to validate the effectiveness of AutoDis. Moreover, AutoDis has been deployed onto a mainstream advertising platform, where online A/B test demonstrates the improvement over the base model by 2.1% and 2.7% in terms of CTR and eCPM, respectively. In addition, the code of our framework is publicly available in MindSpore(https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/recommend/autodis).
AdaFS: Adaptive feature selection in deep recommender system
Authors: Weilin Lin, Xiangyu Zhao 0001, Yejing Wang, Tong Xu 0001, Xian Wu 0001
Venue: Conference KDD 2022
Year: 2022

Feature selection plays an impactful role in deep recommender systems, which selects a subset of the most predictive features, so as to boost the recommendation performance and accelerate model optimization. The majority of existing feature selection methods, however, aim to select only a fixed subset of features. This setting cannot fit the dynamic and complex environments of practical recommender systems, where the contribution of a specific feature varies significantly across user-item interactions. In this paper, we propose an adaptive feature selection framework, AdaFS, for deep recommender systems. To be specific, we develop a novel controller network to automatically select the most relevant features from the whole feature space, which fits the dynamic recommendation environment better. Besides, different from classic feature selection approaches, the proposed controller can adaptively score each example of user-item interactions, and identify the most informative features correspondingly for subsequent recommendation tasks. We conduct extensive experiments based on two public benchmark datasets from a real-world recommender system. Experimental results demonstrate the effectiveness of AdaFS, and its excellent transferability to the most popular deep recommendation models.