Reviewerly/exHarmony huggingface dataset:
Clustered Embedding Learning for Recommender Systems

In recent years, recommender systems have advanced rapidly, where embedding learning for users and items plays a critical role. A standard method learns a unique embedding vector for each user and item. However, such a method has two important limitations in real-world applications: 1) it is hard to learn embeddings that generalize well for users and items with rare interactions; and 2) it may incur unbearably high memory costs when the number of users and items scales up. Existing approaches either can only address one of the limitations or have flawed overall performances. In this paper, we propose Clustered Embedding Learning (CEL) as an integrated solution to these two problems. CEL is a plug-and-play embedding learning framework that can be combined with any differentiable feature interaction model. It is capable of achieving improved performance, especially for cold users and items, with reduced memory cost. CEL enables automatic and dynamic clustering of users and items in a top-down fashion, where clustered entities jointly learn a shared embedding. The accelerated version of CEL has an optimal time complexity, which supports efficient online updates. Theoretically, we prove the identifiability and the existence of a unique optimal number of clusters for CEL in the context of nonnegative matrix factorization. Empirically, we validate the effectiveness of CEL on three public datasets and one business dataset, showing its consistently superior performance against current state-of-the-art methods. In particular, when incorporating CEL into the business model, it brings an improvement of in AUC, which translates into a significant revenue gain; meanwhile, the size of the embedding table gets 2650 times smaller.1
Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning

In recent years, seeking health information on the web has become a preferred way for healthcare consumers to support their information needs. Generally, healthcare consumers use long and detailed questions with several peripheral details to express their healthcare concerns, contributing to natural language understanding challenges. One way to address this challenge is by summarizing the questions. However, most of the existing abstractive summarization systems generate impeccably fluent yet factually incorrect summaries. In this paper, we present a semantically-enhanced contrastive learning-based framework for generating abstractive question summaries that are faithful and factually correct. We devised multiple strategies based on question semantics to generate the erroneous (negative) summaries, such that the model has the understanding of plausible and incorrect perturbations of the original summary. Our extensive experimental results on two benchmark consumer health question summarization datasets confirm the effectiveness of our proposed method by achieving state-of-the-art performance and generating factually correct and fluent summaries, as measured by human evaluation.
Multi-head Variational Graph Autoencoder Constrained by Sum-product Networks

Variational graph autoencoder (VGAE) is a promising deep probabilistic model in graph representation learning. However, most existing VGAEs adopt the mean-field assumption, and cannot characterize the graphs with noise well. In this paper, we propose a novel deep probabilistic model for graph analysis, termed Multi-head Variational Graph Autoencoder Constrained by Sum-product Networks (named SPN-MVGAE), which helps to relax the mean-field assumption and learns better latent representation with fault tolerance. Our proposed model SPN-MVGAE uses conditional sum-product networks as constraints to learn the dependencies between latent factors in an end-to-end manner. Furthermore, we introduce the superposition of the latent representations learned by multiple variational networks to represent the final latent representations of nodes. Our model is the first use sum-product networks for graph representation learning, extending the scope of sum-product networks applications. Experimental results show that compared with other baseline methods, our model has competitive advantages in link prediction, fault tolerance, node classification, and graph visualization on real datasets.
SeqCare: Sequential Training with External Medical Knowledge Graph for Diagnosis Prediction in Healthcare Data

Deep learning techniques are capable of capturing complex input-output relationships, and have been widely applied to the diagnosis prediction task based on web-based patient electronic health records (EHR) data. To improve the prediction and interpretability of pure data-driven deep learning with only a limited amount of labeled data, a pervasive trend is to assist the model training with knowledge priors from online medical knowledge graphs. However, they marginally investigated the label imbalance and the task-irrelevant noise in the external knowledge graph. The imbalanced label distribution would bias the learning and knowledge extraction towards the majority categories. The task-irrelevant noise introduces extra uncertainty to the model performance. To this end, aiming at by-passing the bias-variance trade-off dilemma, we introduce a new sequential learning framework, dubbed SeqCare, for diagnosis prediction with online medical knowledge graphs. Concretely, in the first step, SeqCare learns a bias-reduced space through a self-supervised graph contrastive learning task. Secondly, SeqCare reduces the learning uncertainty by refining the supervision signal and the graph structure of the knowledge graph simultaneously. Lastly, SeqCare trains the model in the bias-variance reduced space with a self-distillation to further filter out irrelevant information in the data. Experimental evaluations on two real-world datasets show that SeqCare outperforms state-of-the-art approaches. Case studies exemplify the interpretability of SeqCare. Moreover, the medical findings discovered by SeqCare are consistent with experts and medical literature.
CapEnrich: Enriching Caption Semantics for Web Images via Cross-modal Pre-trained Knowledge

Automatically generating textual descriptions for massive unlabeled images on the web can greatly benefit realistic web applications, e.g. multimodal retrieval and recommendation. However, existing models suffer from the problem of generating “over-generic” descriptions, such as their tendency to generate repetitive sentences with common concepts for different images. These generic descriptions fail to provide sufficient textual semantics for ever-changing web images. Inspired by the recent success of Vision-Language Pre-training (VLP) models that learn diverse image-text concept alignment during pretraining, we explore leveraging their cross-modal pre-trained knowledge to automatically enrich the textual semantics of image descriptions. With no need for additional human annotations, we propose a plug-and-play framework, i.e CapEnrich, to complement the generic image descriptions with more semantic details. Specifically, we first propose an automatic data-building strategy to get desired training sentences, based on which we then adopt prompting strategies, i.e. learnable and template prompts, to incentivize VLP models to generate more textual details. For learnable templates, we fix the whole VLP model and only tune the prompt vectors, which leads to two advantages: 1) the pre-training knowledge of VLP models can be reserved as much as possible to describe diverse visual concepts; 2) only lightweight trainable parameters are required, so it is friendly to low data resources. Extensive experiments show that our method significantly improves the descriptiveness and diversity of generated sentences for web images. The code is available at https://github.com/yaolinli/CapEnrich.
Dynalogue: A Transformer-Based Dialogue System with Dynamic Attention

Businesses face a range of cyber risks, both external threats and internal vulnerabilities that continue to evolve over time. As cyber attacks continue to increase in complexity and sophistication, more organisations will experience them. For this reason, it is important that organisations seek timely consultancy from cyber professionals so that they can respond to and recover from cyber attacks as quickly as possible. However, huge surges in cyber attacks have long left cyber professionals short of what is required to cover the security needs. This problem is getting worse when an increasing number of people choose to work from home during the pandemic because this situation usually yields extra communication cost.
DDPC: Automated Data-Driven Power-Performance Controller Design on-the-fly for Latency-sensitive Web Services

Traditional power reduction techniques such as DVFS or RAPL are challenging to use with web services because they significantly affect the services’ latency and throughput. Previous work suggested the use of controllers based on control theory or machine learning to reduce performance degradation under constrained power. However, generating these controllers is challenging as every web service applications running in a data center requires a power-performance model and a fine-tuned controller. In this paper, we present DDPC, a system for autonomic data-driven controller generation for power-latency management. DDPC automates the process of designing and deploying controllers for dynamic power allocation to manage the power-performance trade-offs for latency-sensitive web applications such as a social network. For each application, DDPC uses system identification techniques to learn an adaptive power-performance model that captures the application’s power-latency trade-offs which is then used to generate and deploy a Proportional-Integral (PI) power controller with gain-scheduling to dynamically manage the power allocation to the server running application using RAPL. We evaluate DDPC with two realistic latency-sensitive web applications under varying load scenarios. Our results show that DDPC is capable of autonomically generating and deploying controllers within a few minutes reducing the active power allocation of a web-server by more than 50% compared to state-of-the-art techniques while maintaining the latency well below the target of the application.
Link Topics from Q&amp;A Platforms using Wikidata: A Tool for Cross-platform Hierarchical Classification

This paper proposes a novel rule-based topic classification tool for questions on Q&A platforms mediated by the Wikidata ontology – an open and accessible multilingual ontology curated by a large community of online users. Q&A platforms are important sources of information on the Web and often appear as part of Web search results. By adopting Wikidata taxonomic relations as references, our tool can categories the Web content from different platforms in a unified coarse-to-fine mode based on their domain coverage. To validate and demonstrate the potential applicability of our tool, a set of use cases and experiments are carried out on two popular Q&A platforms – Zhihu and Quora, where the impact of topic categories on question lifecycles is explored. Furthermore, we compare our results with the output generated by GPT-3 classifier. This tool sheds light on how structured knowledge bases can enable data interoperability and serve as a filtering functionality to mitigate classification bias of OpenAI.
Robust Graph Representation Learning for Local Corruption Recovery

The performance of graph representation learning is affected by the quality of graph input. While existing research usually pursues a globally smoothed graph embedding, we believe the rarely observed anomalies are as well harmful to an accurate prediction. This work establishes a graph learning scheme that automatically detects (locally) corrupted feature attributes and recovers robust embedding for prediction tasks. The detection operation leverages a graph autoencoder, which does not make any assumptions about the distribution of the local corruptions. It pinpoints the positions of the anomalous node attributes in an unbiased mask matrix, where robust estimations are recovered with sparsity promoting regularizer. The optimizer approaches a new embedding that is sparse in the framelet domain and conditionally close to input observations. Extensive experiments are provided to validate our proposed model can recover a robust graph representation from black-box poisoning and achieve excellent performance.
Beyond Fine-Tuning: Efficient and Effective Fed-Tuning for Mobile/Web Users

Fine-tuning is a typical mechanism to achieve model adaptation for mobile/web users, where a model trained by the cloud is further retrained to fit the target user task. While traditional fine-tuning has been proved effective, it only utilizes local data to achieve adaptation, failing to take advantage of the valuable knowledge from other mobile/web users. In this paper, we attempt to extend the local-user fine-tuning to multi-user fed-tuning with the help of Federated Learning (FL). Following the new paradigm, we propose EEFT, a framework aiming to achieve Efficient and Effective Fed-Tuning for mobile/web users. The key idea is to introduce lightweight but effective adaptation modules to the pre-trained model, such that we can freeze the pre-trained model and just focus on optimizing the modules to achieve cost reduction and selective task cooperation. Extensive experiments on our constructed benchmark demonstrate the effectiveness and efficiency of the proposed framework.
TRAVERS: A Diversity-Based Dynamic Approach to Iterative Relevance Search over Knowledge Graphs

Relevance search over knowledge graphs seeks top-ranked answer entities that are most relevant to a query entity. Since the semantics of relevance varies with the user need and its formalization is difficult for non-experts, existing methods infer semantics from user-provided example answer entities. However, a user may provide very few examples, even none at the beginning of interaction, thereby limiting the effectiveness of such methods. In this paper, we vision a more practical scenario called labeling-based iterative relevance search: instead of effortfully inputting example answer entities, the user effortlessly (e.g., implicitly) labels current answer entities, and is rewarded with improved answer entities in the next iteration. To realize the scenario, our approach TRAVERS incorporates two rankers: a diversity-oriented ranker for supporting cold start and avoiding converging to sub-optimum caused by noisy labels, and a relevance-oriented ranker capable of handling unbalanced labels. Moreover, the two rankers and their combination dynamically evolve over iterations. TRAVERS outperformed a variety of baselines in experiments with simulated and real user behavior.
Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection

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.
Unlearning Graph Classifiers with Limited Data Resources

As the demand for user privacy grows, controlled data removal (machine unlearning) is becoming an important feature of machine learning models for data-sensitive Web applications such as social networks and recommender systems. Nevertheless, at this point it is still largely unknown how to perform efficient machine unlearning of graph neural networks (GNNs); this is especially the case when the number of training samples is small, in which case unlearning can seriously compromise the performance of the model. To address this issue, we initiate the study of unlearning the Graph Scattering Transform (GST), a mathematical framework that is efficient, provably stable under feature or graph topology perturbations, and offers graph classification performance comparable to that of GNNs. Our main contribution is the first known nonlinear approximate graph unlearning method based on GSTs. Our second contribution is a theoretical analysis of the computational complexity of the proposed unlearning mechanism, which is hard to replicate for deep neural networks. Our third contribution are extensive simulation results which show that, compared to complete retraining of GNNs after each removal request, the new GST-based approach offers, on average, a 10.38x speed-up and leads to a 2.6% increase in test accuracy during unlearning of 90 out of 100 training graphs from the IMDB dataset (10% training ratio). Our implementation is available online at https://doi.org/10.5281/zenodo.7613150.
MassNE: Exploring Higher-Order Interactions with Marginal Effect for Massive Battle Outcome Prediction

In online games, predicting massive battle outcomes is a fundamental task of many applications, such as team optimization and tactical formulation. Existing works do not pay adequate attention to the massive battle. They either seek to evaluate individuals in isolation or mine simple pair-wise interactions between individuals, neither of which effectively captures the intricate interactions between massive units (e.g., individuals). Furthermore, as the team size increases, the phenomenon of diminishing marginal utility of units emerges. Such a diminishing pattern is rarely noticed in previous work, and how to capture it from data remains a challenge. To this end, we propose a novel Massive battle outcome predictor with margiNal Effect modules, namely MassNE, which comprehensively incorporates individual effects, cooperation effects (i.e., intra-team interactions) and suppression effects (i.e., inter-team interactions) for predicting battle outcomes. Specifically, we design marginal effect modules to learn how units’ marginal utility changing respect to their number, where the monotonicity assumption is applied to ensure rationality. In addition, we evaluate the current classical models and provide mathematical proofs that MassNE is able to generalize several earlier works in massive settings. Massive battle datasets generated by StarCraft II APIs are adopted to evaluate the performances of MassNE. Extensive experiments empirically demonstrate the effectiveness of MassNE, and MassNE can reveal reasonable cooperation effects, suppression effects, and marginal utilities of combat units from the data.
A Reference-Dependent Model for Web Search Evaluation

Previous researches demonstrate that users’ actions in search interaction are associated with relative gains and losses to reference points, known as the reference dependence effect. However, this widely confirmed effect is not represented in most user models underpinning existing search evaluation metrics. In this study, we propose a new evaluation metric framework, namely Reference Dependent Metric (ReDeM), for assessing query-level search by incorporating the effect of reference dependence into the modelling of user search behavior. To test the overall effectiveness of the proposed framework, (1) we evaluate the performance, in terms of correlation with user satisfaction, of ReDeMs built upon different reference points against that of the widely-used metrics on three search datasets; (2) we examine the performance of ReDeMs under different task states, like task difficulty and task urgency; and (3) we analyze the statistical reliability of ReDeMs in terms of discriminative power. Experimental results indicate that: (1) ReDeMs integrated with a proper reference point achieve better correlations with user satisfaction than most of the existing metrics, like Discounted Cumulative Gain (DCG) and Rank-Biased Precision (RBP), even though their parameters have already been well-tuned; (2) ReDeMs reach relatively better performance compared to existing metrics when the task triggers a high-level cognitive load; (3) the discriminative power of ReDeMs is far stronger than Expected Reciprocal Rank (ERR), slightly stronger than Precision and similar to DCG, RBP and INST. To our knowledge, this study is the first to explicitly incorporate the reference dependence effect into the user browsing model and offline evaluation metrics. Our work illustrates a promising approach to leveraging the insights about user biases from cognitive psychology in better evaluating user search experience and enhancing user models.
Automatic Discovery of Emerging Browser Fingerprinting Techniques

With the progression of modern browsers, online tracking has become the most concerning issue for preserving privacy on the web. As major browser vendors plan to or already ban third-party cookies, trackers have to shift towards browser fingerprinting by incorporating novel browser APIs into their tracking arsenal. Understanding how new browser APIs are abused in browser fingerprinting techniques is a significant step toward ensuring protection from online tracking.
Expressive and Efficient Representation Learning for Ranking Links in Temporal Graphs

Temporal graph representation learning (T-GRL) aims to learn representations that model how graph edges evolve over time. While recent works on T-GRL have improved link prediction accuracy in temporal settings, their methods optimize a point-wise loss function independently over future links rather than optimize jointly over a candidate set per node. In applications where resources (e.g., attention) are allocated based on ranking links by likelihood, the use of a ranking loss is preferred. However it is not straightforward to develop a T-GRL method to optimize a ranking loss due to a tradeoff between model expressivity and scalability. In this work, we address these issues and propose a Temporal Graph network for Ranking (TGRank), which significantly improves performance for link prediction tasks by (i) optimizing a list-wise loss for improved ranking, and (ii) incorporating a labeling approach designed to allow for efficient inference over the candidate set jointly, while provably boosting expressivity. We extensively evaluate TGRank over six real networks. TGRank outperforms the state-of-the-art baselines on average by 14.21%↑ (transductive) and 16.25% ↑ (inductive) in ranking metrics while being more efficient (up-to 65 × speed-up) to make inference on large networks.