OpenRaiser/Intern-Atlas huggingface dataset:
Know Your Victim: Tor Browser Setting Identification via Network Traffic Analysis
Authors: Chun-Ming Chang, Hsu-Chun Hsiao, Timothy M. Lynar, Tatsuya Mori
Venue: WWW
Year: 2022

Network traffic analysis (NTA) is widely researched to fingerprint users’ behavior by analyzing network traffic with machine learning algorithms. It has introduced new lines of de-anonymizing attacks [1] in the Tor network, inclusive of Website Fingerprinting (WF) and Hidden Service Fingerprinting (HSF). Previous work [4] observed that the Tor browser version may affect network traffic and claimed that having identical browsing settings between the users and adversaries is one of the challenges in WF and HSF. Based on this observation, we propose a NTA method to identify users’ browser settings in the Tor network. We confirm that browser settings have notable impacts on network traffic and create a classifier to identify the browser settings. The classifier can establish over 99% accuracy under the closed-world assumption. The open-world assumption results indicate classification success except for one security setting option. Last, we provide our observations and insights through feature analysis and changelog inspection.
Hypermedea: A Framework for Web (of Things) Agents
Authors: Victor Charpenay, Antoine Zimmermann, Maxime Lefrançois, Olivier Boissier
Venue: WWW
Year: 2022

Hypermedea is an extension of the JaCaMo multi-agent programming framework to act on Web and Web of Things environments. In this demo, the performance of Hypermedea’s Linked Data navigation and planning components are evaluated, both encapsulating computation-intensive algorithms.
OffDQ: An Offline Deep Learning Framework for QoS Prediction
Authors: Soumi Chattopadhyay, Richik Chanda, Suraj Kumar, Chandranath Adak
Venue: WWW
Year: 2022

With the increasing trend of web services over the Internet, developing a robust Quality of Service (QoS) prediction algorithm for recommending services in real-time is becoming a challenge today. Designing an efficient QoS prediction algorithm achieving high accuracy, while supporting faster prediction to enable the algorithm to be integrated into a real-time system, is one of the primary focuses in the domain of Services Computing. The major state-of-the-art QoS prediction methods are yet to efficiently meet both criteria simultaneously, possibly due to the lack of analysis of challenges involved in designing the prediction algorithm. In this paper, we systematically analyze the various challenges associated with the QoS prediction algorithm and propose solution strategies to overcome the challenges, and thereby propose a novel offline framework using deep neural architectures for QoS prediction to achieve our goals. Our framework, on the one hand, handles the sparsity of the dataset, captures the non-linear relationship among data, figures out the correlation between users and services to achieve desirable prediction accuracy. On the other hand, our framework being an offline prediction strategy enables faster responsiveness. We performed extensive experiments on the publicly available WS-DREAM dataset to show the trade-off between prediction performance and prediction time. Furthermore, we observed our framework significantly improved one of the parameters (prediction accuracy or responsiveness) without considerably compromising the other as compared to the state-of-the-art methods.
Efficient Online Learning to Rank for Sequential Music Recommendation
Authors: Pedro Dalla Vecchia Chaves, Bruno L. Pereira, Rodrygo L. T. Santos
Venue: WWW
Year: 2022

Music streaming services heavily rely upon recommender systems to acquire, engage, and retain users. One notable component of these services are playlists, which can be dynamically generated in a sequential manner based on the user’s feedback during a listening session. Online learning to rank approaches have recently been shown effective at leveraging such feedback to learn users’ preferences in the space of song features. Nevertheless, these approaches can suffer from slow convergence as a result of their random exploration component and get stuck in local minima as a result of their session-agnostic exploitation component. To overcome these limitations, we propose a novel online learning to rank approach which efficiently explores the space of candidate recommendation models by restricting itself to the orthogonal complement of the subspace of previous underperforming exploration directions. Moreover, to help overcome local minima, we propose a session-aware exploitation component which adaptively leverages the current best model during model updates. Our thorough evaluation using simulated listening sessions from Last.fm demonstrates substantial improvements over state-of-the-art approaches regarding early-stage performance and overall long-term convergence.
Recommendation Unlearning
Authors: Chong Chen, Fei Sun, Min Zhang, Bolin Ding
Venue: WWW
Year: 2022

Recommender systems provide essential web services by learning users’ personal preferences from collected data. However, in many cases, systems also need to forget some training data. From the perspective of privacy, users desire a tool to erase the impacts of their sensitive data from the trained models. From the perspective of utility, if a system’s utility is damaged by some bad data, the system needs to forget such data to regain utility. While unlearning is very important, it has not been well-considered in existing recommender systems. Although there are some researches have studied the problem of machine unlearning, existing methods can not be directly applied to recommendation as they are unable to consider the collaborative information. In this paper, we propose RecEraser, a general and efficient machine unlearning framework tailored to recommendation tasks. The main idea of RecEraser is to divide the training set into multiple shards and train submodels with these shards. Specifically, to keep the collaborative information of the data, we first design three novel data partition algorithms to divide training data into balanced groups. We then further propose an adaptive aggregation method to improve the global model utility. Experimental results on three public benchmarks show that RecEraser can not only achieve efficient unlearning but also outperform the state-of-the-art unlearning methods in terms of model utility. The source code can be found at https://github.com/chenchongthu/Recommendation-Unlearning
A Spatio-Temporal Data-Driven Automatic Control Method for Smart Home Services
Authors: Jinrong Chen, Zheyi Chen, Longhai Zheng, Xing Chen
Venue: WWW
Year: 2022

With the rapid development of smart home technologies, various smart devices have entered and brought convenience to people’s daily life. Meanwhile, higher demands for smart home services have gradually emerged, which cannot be well satisfied by using traditional service provisioning manners. This is because traditional smart home control systems commonly rely on manual operations and fixed rules, which cannot satisfy changeable user demands and may seriously degrade the user experience. Therefore, it is necessary to capture user preferences based on their historical behavior data. To address the above problems, a temporal knowledge graph is first proposed to support the acquisition of user-perceived environmental data and user behavior data. Next, a user-oriented smart home service prediction model is designed based on the temporal knowledge graph, which can predict the service status and automatically perform the corresponding service for each user. Finally, a prototype system is built according to a real-world smart home environment. The experimental results show that the proposed method can provide personalized smart home services and well satisfy user demands.
An Overview of Financial Technology Innovation
Authors: Chung-Chi Chen, Hen-Hsen Huang, Hiroya Takamura, Hsin-Hsi Chen
Venue: WWW
Year: 2022

In this paper, we provide an overview of financial technology (FinTech) innovation based on our experience of organizing multiple FinTech-related events since 2018, including the FinNLP workshop series, FinWeb workshop series, and FinNum shared task series. These event series aim to provide a forum for blending the research of FinTech and artificial intelligence (AI), and further accelerating the development in the FinTech domain. We hope that with the researchers’ sharing in these events, the challenging problems will be identified, and the future research direction will be shaped. Both the development of the technology and the trend of the focused topics in the past four years are discussed in this paper. We also propose a research agenda with the plan of our FinTech-related events.
EvidenceNet: Evidence Fusion Network for Fact Verification
Authors: Zhendong Chen, Siu Cheung Hui, Fuzhen Zhuang, Lejian Liao, Fei Li, Meihuizi Jia, Jiaqi Li
Venue: WWW
Year: 2022

Fact verification is a challenging task that requires the retrieval of multiple pieces of evidence from a reliable corpus for verifying the truthfulness of a claim. Although the current methods have achieved satisfactory performance, they still suffer from one or more of the following three problems: (1) unable to extract sufficient contextual information from the evidence sentences; (2) containing redundant evidence information and (3) incapable of capturing the interaction between claim and evidence. To tackle the problems, we propose an evidence fusion network called EvidenceNet. The proposed EvidenceNet model captures global contextual information from various levels of evidence information for deep understanding. Moreover, a gating mechanism is designed to filter out redundant information in evidence. In addition, a symmetrical interaction attention mechanism is also proposed for identifying the interaction between claim and evidence. We conduct extensive experiments based on the FEVER dataset. The experimental results have shown that the proposed EvidenceNet model outperforms the current fact verification methods and achieves the state-of-the-art performance.
Learning Recommenders for Implicit Feedback with Importance Resampling
Authors: Jin Chen, Defu Lian, Binbin Jin, Kai Zheng, Enhong Chen
Venue: WWW
Year: 2022

Recommendation is prevalently studied for implicit feedback recently, but it seriously suffers from the lack of negative samples, which has a significant impact on the training of recommendation models. Existing negative sampling is based on the static or adaptive probability distributions. Sampling from the adaptive probability receives more attention, since it tends to generate more hard examples, to make recommender training faster to converge. However, item sampling becomes much more time-consuming particularly for complex recommendation models. In this paper, we propose an Adaptive Sampling method based on Importance Resampling (AdaSIR for short), which is not only almost equally efficient and accurate for any recommender models, but also can robustly accommodate arbitrary proposal distributions. More concretely, AdaSIR maintains a contextualized sample pool of fixed-size with importance resampling, from which items are only uniformly sampled. Such a simple sampling method can be proved to provide approximately accurate adaptive sampling under some conditions. The sample pool plays two extra important roles in (1) reusing historical hard samples with certain probabilities; (2) estimating the rank of positive samples for weighting, such that recommender training can concentrate more on difficult positive samples. Extensive empirical experiments demonstrate that AdaSIR outperforms state-of-the-art methods in terms of sampling efficiency and effectiveness.
A Decision Model for Designing NLP Applications
Authors: Eason Chen, Yuen-Hsien Tseng
Venue: WWW
Year: 2022

Among the NLP models’ usages, some applications provide multiple output options, and some offer only a single result to the end-users. However, there is little research about which situations providing multiple outputs from NLP models will benefit the user experience. Therefore, in this position paper, we summarize the progress of NLP applications, which shows parallel outputs from the NLP model at once to users. Then a decision model is presented that can assist in deciding whether a given condition is suitable to show multiple outputs at once from the NLP model. We hope developers and UX designers can examine the decision model and create an easy-to-use interface that can present numerous results from the NLP model at once. Moreover, we hope future researchers can reference the decision model from this paper to explore the potential of other NLP models’ usage that can show parallel outputs at once to create a more satisfactory user experience.
Graph Neural Transport Networks with Non-local Attentions for Recommender Systems
Authors: Huiyuan Chen, Chin-Chia Michael Yeh, Fei Wang, Hao Yang
Venue: WWW
Year: 2022

Graph Neural Networks (GNNs) have emerged as powerful tools for collaborative filtering. A key challenge of recommendations is to distill long-range collaborative signals from user-item graphs. Typically, GNNs generate embeddings of users/items by propagating and aggregating the messages between local neighbors. Thus, the ability of GNNs to capture long-range dependencies heavily depends on their depths. However, simply training deep GNNs has several bottleneck effects, e.g., over-fitting & over-smoothing, which may lead to unexpected results if GNNs are not well regularized. Here we present Graph Optimal Transport Networks (GOTNet) to capture long-range dependencies without increasing the depths of GNNs. Specifically, we perform k-Means clustering on nodes’ GNN embeddings to obtain graph-level representations (e.g., centroids). We then compute node-centroid attentions, which enable long-range messages to be communicated among distant but similar nodes. Our non-local attention operators work seamlessly with local operators in original GNNs. As such, GOTNet is able to capture both local and non-local messages in graphs by only using shallow GNNs, which avoids the bottleneck effects of deep GNNs. Experimental results demonstrate that GOTNet achieves better performance compared with state-of-the-art GNNs.
Towards Automatic Discovering of Deep Hybrid Network Architecture for Sequential Recommendation
Authors: Mingyue Cheng, Zhiding Liu, Qi Liu, Shenyang Ge, Enhong Chen
Venue: WWW
Year: 2022

Recent years have witnessed great success in deep learning-based sequential recommendation (SR), which can provide more timely and accurate recommendations. One of the most effective deep SR architectures is to stack high-performance residual blocks, e.g., prevalent self-attentive and convolutional operations, for capturing long- and short-range dependence of sequential behaviors. By carefully revisiting previous models, we observe: 1) simple architecture modification of gating each residual connection can help us train deeper SR models and yield significant improvements; 2) compared with self-attention mechanism, stacking of convolution layers also can cover each item of the whole sequential behaviors and achieve competitive or even superior performance. Guided by these findings, it is meaningful to design a deeper hybrid SR model to ensemble the capacity of both self-attentive and convolutional architectures for SR tasks. In this work, we aim to achieve this goal in the automatic algorithm sense, and propose NASR, an efficient neural architecture search (NAS) method that can automatically select the architecture operation on each layer. Specifically, we firstly design a Table-like search space, involving both self-attentive and convolutional-based SR architectures in a flexible manner. In the search phase, we leverage weight-sharing supernets to encode the entire search space, and further propose to factorize the whole supernet into blocks to ensure the potential candidate SR architectures can be fully trained. Owning to lacking supervisions, we train each block-wise supernet with a self-supervised contrastive optimization scheme, in which the training signals are constructed by conducting data augmentation on original sequential behaviors. The empirical studies show that the discovered deep hybrid network architectures can exhibit substantial improvements over compared baselines, indicating the practicality of searching deep hybrid network architectures on SR tasks. Notably, we show the discovered architecture also enjoys good generalizability and transferability among different datasets.
Genre-Controllable Story Generation via Supervised Contrastive Learning
Authors: Jin-Uk Cho, Min-Su Jeong, JinYeong Bak, Yun-Gyung Cheong
Venue: WWW
Year: 2022

While controllable text generation has received attention due to the recent advances in large-scale pre-trained language models, there is a lack of research that focuses on story-specific controllability. To address this, we present Story Control via Supervised Contrastive learning model (SCSC), to create a story conditioned on genre. For this, we design a supervised contrastive objective combined with log-likelihood objective, to capture the intrinsic differences among the stories in different genres. The results of our automated evaluation and user study demonstrate that the proposed method is effective in genre-controlled story generation.
Stochastic-Expert Variational Autoencoder for Collaborative Filtering
Authors: Yoon-Sik Cho, Min-hwan Oh
Venue: WWW
Year: 2022

Motivated by the recent successes of deep generative models used for collaborative filtering, we propose a novel framework of VAE for collaborative filtering using multiple experts and stochastic expert selection, which allows the model to learn a richer and more complex latent representation of user preferences. In our method, individual experts are sampled stochastically at each user-item interaction which can effectively utilize the variability among multiple experts. While we propose this framework in the context of collaborative filtering, the proposed stochastic expert technique can be used to enhance VAEs in general beyond the application of collaborative filtering. Hence, this novel technique can be of independent interest. We comprehensively evaluate our proposed method, Stochastic-Expert Variational Autoencoder (SE-VAE) on numerical experiments on the real-world benchmark datasets from MovieLens and Netflix and show that it consistently outperforms the existing state-of-the-art methods across all metrics. Our proposed stochastic expert framework is generic and adaptable to any VAE architecture. The experimental results show that the adaptations to various architectures provided performance gains over the existing methods.
MiDaS: Representative Sampling from Real-world Hypergraphs
Authors: Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, Kijung Shin
Venue: WWW
Year: 2022

Graphs are widely used for representing pairwise interactions in complex systems. Since such real-world graphs are large and often evergrowing, sampling a small representative subgraph is indispensable for various purposes: simulation, visualization, stream processing, representation learning, crawling, to name a few. However, many complex systems consist of group interactions (e.g., collaborations of researchers and discussions on online Q&A platforms), and thus they can be represented more naturally and accurately by hypergraphs (i.e., sets of sets) than by ordinary graphs.
Do Not Read the Same News! Enhancing Diversity and Personalization of News Recommendation
Authors: Seonghwan Choi, Hyeondey Kim, Manjun Gim
Venue: WWW
Year: 2022

Personalized news recommendation by machine is one of the widely studied areas. As the production of news articles increases and topics are diversified, it is impractical to read all the articles available to users. Therefore, the purpose of the news recommendation system should be to provide relevant news based on the user’s interest. Unlike other recommendation systems, explicit feedback from users on each item such as ratings is rarely provided in news recommendation systems. Most news recommendation systems use implicit feedback such as click histories to profile user interest, which leads to biased recommendation results towards generally popular articles. In this paper, we suggest a novel news recommendation model for more personalized recommendations. If a user reads news not widely clicked by others, the news reflects the user’s personal interest rather than other popular news clicked. We implement two user encoders, one to encode the general interest of the set of users and another one to encode the user’s individual interest. We also propose regularization methods that induce two encoders to encode different types of user interest. The experiment on real-world data shows that our proposed method improves the diversity and the quality of recommendations for different click histories without any significant performance drops.
Human Centric Design in Smartcity Technologies: Implications for the Governance, Control and Performance Evaluation of Mobility Ecosystems
Authors: Rob Christiaanse
Venue: WWW
Year: 2022

Governance can be understood as the system by which actors in society are directed and controlled. Given the trinity ”Institution, Market and Organization” a pressing question is: which governance structure minimizes the transaction costs in governing and controlling in the build and service design for organizations similar to a city? We investigate the notion of the governance of common goods and the problem of organizations - how or when to balance control mechanisms. As Tirole observes there is a need to explicate incentives for all stakeholders on the basis of some measure of aggregate welfare of all stakeholders. In this paper we develop a mathematical model to explicate information needs in a bilateral contract and use these insights in the case study, initialized and inspired by the procurement process of transport services at the care institution in the Netherlands in 2020 and 2021. Our results show that the information problem emerges when the object of what is exchanged between two parties is not considered as the unit of analysis. Once we understand the nature of the bilateral exchange relationship then we are able to consider the consequences of the control loss causing transaction costs due to conflicting objectives, moral hazard, adverse selection, opportunism and so on.
A Guided Topic-Noise Model for Short Texts
Authors: Robert Churchill, Lisa Singh, Rebecca Ryan, Pamela Davis-Kean
Venue: WWW
Year: 2022

Researchers using social media data want to understand the discussions occurring in and about their respective fields. These domain experts often turn to topic models to help them see the entire landscape of the conversation, but unsupervised topic models often produce topic sets that miss topics experts expect or want to see. To solve this problem, we propose Guided Topic-Noise Model (GTM), a semi-supervised topic model designed with large domain-specific social media data sets in mind. The input to GTM is a set of topics that are of interest to the user and a small number of words or phrases that belong to those topics. These seed topics are used to guide the topic generation process, and can be augmented interactively, expanding the seed word list as the model provides new relevant words for different topics. GTM uses a novel initialization and a new sampling algorithm called Generalized Polya Urn (GPU) seed word sampling to produce a topic set that includes expanded seed topics, as well as new unsupervised topics. We demonstrate the robustness of GTM on open-ended responses from a public opinion survey and four domain-specific Twitter data sets.