OpenRaiser/Intern-Atlas huggingface dataset:
Workshop on Human-in-the-loop Data Curation
Authors: Gianluca Demartini, Jie Yang, Shazia Sadiq
Venue: CIKM
Year: 2022

Although data quality is a long-standing and enduring problem, it has recently received a resurgence of attention due to the fast proliferation of data analytics, machine learning, and decision-support applications built upon the wide-scale availability and accessibility of (big) data. The success of such applications heavily relies on not only the quantity, but also the quality of data. Data curation, which may include annotation, cleaning, transformation, integration, etc., is a critical step to provide adequate assurances on the quality of analytics and machine learning results. Such data preparation activities are recognised as time and resource intensive for data scientists as data often comes with a number of challenges that need to be tackled before it can be used in practice. Data re-purposing and the resulting distance between design and use intentions of the data, is a fundamental issue behind many of these challenges. These challenges include a variety of data issues such as noise and outliers, incompleteness, representativeness or biases, heterogeneity of format or semantics, etc. Mishandling these challenges can lead to negative and sometimes damaging effects, especially in critical domains like healthcare, transport, and finance. An observable distinct feature of data quality in these contexts is the increasingly important role played by humans, being often the source of data generation and the active players in data curation. This workshop will provide an opportunity to explore the interdisciplinary overlap between manual, automated, and hybrid human-machine methods of data curation.
Efficient Trajectory Similarity Computation with Contrastive Learning
Authors: Liwei Deng, Yan Zhao, Zidan Fu, Hao Sun, Shuncheng Liu, Kai Zheng
Venue: CIKM
Year: 2022

The ubiquity of mobile devices and the accompanying deployment of sensing technologies have resulted in a massive amount of trajectory data. One important fundamental task is trajectory similarity computation, which is to determine how similar two trajectories are. To enable effective and efficient trajectory similarity computation, we propose a novel robust model, namely Contrastive Learning based Trajectory Similarity Computation (CL-TSim). Specifically, we employ a contrastive learning mechanism to learn the latent representations of trajectories and then calculate the dissimilarity between trajectories based on these representations. Compared with sequential auto-encoders that are the mainstream deep learning architectures for trajectory similarity computation, CL-TSim does not require a decoder and step-by-step reconstruction, thus improving the training efficiency significantly. Moreover, considering the non-uniform sampling rate and noisy points in trajectories, we adopt two type of augmentations, i.e., point dowm-sampling and point distorting, to enhance the robustness of the proposed model. Extensive experiments are conducted on two widely-used real-world datasets, i.e., Porto and ChengDu, which demonstrate the superior effectiveness and efficiency of the proposed model.
Weakly-Supervised Online Hashing with Refined Pseudo Tags
Authors: Chen-Lu Ding, Xin Luo, Xiao-Ming Wu, Yu-Wei Zhan, Rui Li, Hui Zhang, Xin-Shun Xu
Venue: CIKM
Year: 2022

With the rapid development of social media, various types of tags uploaded by social users are attached to the images. Compared to clean labels marked by experts, although user-provided tags are imperfect, e.g., wrong tags, reduplicative tags, or missing tags, they are more diverse, fine-grained, and informative. Currently, there exist several weakly-supervised hashing methods attempting to learn hash codes using tags as supervision. Although they could benefiting from the rich information contained in tags, most of them may defy the nature of social media data. In real scenarios, social media data appears in streaming fashion, but most weakly-supervised hashing methods are just batch-based which cannot effectively handle streaming data. To this end, only one weakly-supervised online hashing method has been proposed, but it is still far from enough to alleviate the negative effects of tags. In this paper, to address the above problems, we propose a new method, termed Weakly-Supervised Online Hashing with Refined Pseudo Tags (RPT-WOH). To improve the quality of weakly-supervised tags, we design the real-valued pseudo tag matrix and learn it by exploiting the correlation between the previous and new tags. Furthermore, we propose a memory-based similarity learning which could effectively maintain the semantic correlation between old and new data. In addition, we propose an effective and efficient discrete online optimization algorithm making RPT-WOH easily scalable to large-scale data. Extensive experiments conducted on two benchmark datasets demonstrate that RPT-WOH offers satisfactory performance.
GFlow-FT: Pick a Child Network via Gradient Flow for Efficient Fine-Tuning in Recommendation Systems
Authors: Ke Ding, Yong He, Xin Dong, Jieyu Yang, Liang Zhang, Ang Li, Xiaolu Zhang, Linjian Mo
Venue: CIKM
Year: 2022

Conversion Rate (CVR) prediction is a crucial task in online advertising systems. Existing single-domain CVR prediction models suffer from the data sparsity problem since few users purchase items after clicking. In recent years, a robust and effective technique called fine-tuning can transfer knowledge from a data-rich source domain to enhance the CVR prediction performance in a data-sparse target domain. However, since most CVR prediction models have a large number of parameters, fine-tuning all the parameters on a data-sparse domain may lead to over-fitting. In this paper, we propose a general and efficient transfer learning method called Gradient-Flow based Fine-Tuning (GFlow-FT), which only needs to update a subset of parameters (called child network) via pruning the gradients to restrain gradient norm against over-fitting. In addition, our method employs the gradient-flow based measure via calculating the Hessian-gradient product as the criteria for picking the child network, which is superior to the magnitude-based and loss-based measure from empirical results. Extensive experimental results on three real-world datasets from recommendation systems show that GFlow-FT can significantly improve the performance of CVR prediction compared with state-of-the-art fine-tuning approaches.
MASR: A Model-Agnostic Sparse Routing Architecture for Arbitrary Order Feature Sharing in Multi-Task Learning
Authors: Xin Dong, Ruize Wu, Chao Xiong, Hai Li, Lei Cheng, Yong He, Shiyou Qian, Jian Cao, Linjian Mo
Venue: CIKM
Year: 2022

Multi-task learning (MTL) has experienced rapid growth in recent years. A typical way of conducting MTL with deep neural networks (DNNs) is either establishing a sort of global feature sharing mechanism across all tasks or assigning each task an individual set of parameters with cross-connections. However, these existing approaches leverage DNNs only to share features of a certain order. Several modelsdemonstrated that explicitly modeling feature sharing with both low-order and high-order features can boost performance. To this end, we propose a model-agnostic sparse routing architecture called MASR, which emphasizes arbitrary order feature sharing for multi-task learning. It is able to choose specific orders of features to route for a given task through learnable latent variables. Moreover, MASR is model-agnostic and can be combined with existing MTL models to share features of both low-order and high-order. Extensive experimental results on several real-world datasets not only confirm the significant improvement of MASR performed to existing MTL models but also outperform existing hybrid architectures in terms of AUC metric.
THECOG 2022 - Transforms In Behavioral And Affective Computing (Revisited)
Authors: Georgios Drakopoulos, Eleanna Kafeza
Venue: CIKM
Year: 2022

Human decision making is central in many functions across a broad spectrum of fields such as marketing, investment, smart contract formulations, political campaigns, and organizational strategic management. Behavioral economics seeks to study the psychological, cultural, and social factors contributing to decision making along reasoning. It should be highlighted here that behavioral economics do not negate classical economic theory but rather extend it in two distinct directions. First, a finer granularity can be obtained by studying the decision making process not of massive populations but instead of individuals and groups with signal estimation or deep learning techniques based on a wide array of attributes ranging from social media posts to physiological signs. Second, time becomes a critical parameter and changes to the disposition towards alternative decisions can be tracked with input-output or state space models. The primary findings so far are concepts like bounded rationality and perceived risk, while results include optimal strategies for various levels of information awareness and action strategies based on perceived loss aversion principles. From the above it follows that behavioral economics relies on deep learning, signal processing, control theory, social media analysis, affective computing, natural language processing, and gamification to name only a few fields. Therefore, it is directly tied to computer science in many ways. THECOG will be a central meeting point for researchers of various backgrounds in order to generate new interdisciplinary and groundbreaking results.
Contrastive Learning with Bidirectional Transformers for Sequential Recommendation
Authors: Hanwen Du, Hui Shi, Pengpeng Zhao, Deqing Wang, Victor S. Sheng, Yanchi Liu, Guanfeng Liu, Lei Zhao
Venue: CIKM
Year: 2022

Contrastive learning with Transformer-based sequence encoder has gained predominance for sequential recommendation. It maximizes the agreements between paired sequence augmentations that share similar semantics. However, existing contrastive learning approaches in sequential recommendation mainly center upon left-to-right unidirectional Transformers as base encoders, which are suboptimal for sequential recommendation because user behaviors may not be a rigid left-to-right sequence. To tackle that, we propose a novel framework named Contrastive learning with Bidirectional Transformers for sequential recommendation (CBiT). Specifically, we first apply the slide window technique for long user sequences in bidirectional Transformers, which allows for a more fine-grained division of user sequences. Then we combine the cloze task mask and the dropout mask to generate high-quality positive samples and perform multi-pair contrastive learning, which demonstrates better performance and adaptability compared with the normal one-pair contrastive learning. Moreover, we introduce a novel dynamic loss reweighting strategy to balance between the cloze task loss and the contrastive loss. Experiment results on three public benchmark datasets show that our model outperforms state-of-the-art models for sequential recommendation. Our code is available at this link: https://github.com/hw-du/CBiT/tree/master.
Optimal Action Space Search: An Effective Deep Reinforcement Learning Method for Algorithmic Trading
Authors: Zhongjie Duan, Cen Chen, Dawei Cheng, Yuqi Liang, Weining Qian
Venue: CIKM
Year: 2022

Algorithmic trading is a crucial yet challenging task in the financial domain, where trading decisions are made sequentially from milliseconds to days based on the historical price movements and trading frequency. To model such a sequential decision making process in the dynamic financial markets, Deep Reinforcement Learning (DRL) based methods have been applied and demonstrated their success in finding trading strategies that achieve profitable returns. However, the financial markets are complex imperfect information games with high-level of noise and uncertainties which usually make the exploration policy of DRL less effective. In this paper, we propose an end-to-end DRL method that explores solutions on the whole graph via a probabilistic dynamic programming algorithm. Specifically, we separate the state into environment state and position state, and model the position state transition as a directed acyclic graph. To obtain reliable gradients for model training, we adopt a probabilistic dynamic programming algorithm to explore solutions over the whole graph instead of sampling a path. By avoiding the sampling procedure, we propose an efficient training algorithm and overcome the efficiency problem in most existing DRL methods. Furthermore, our method is compatible with most recurrent neural network architecture, which makes our method easy to implement and very effective in practice. Extensive experiments have been conducted on two real-world stock datasets. Experimental results demonstrate that our method can generate stable trading strategies for both high-frequency and low-frequency trading, significantly outperforming the baseline DRL methods on annualized return and Sharpe ratio.
A Platform for Argumentative Zoning Annotation and Scientific Summarization
Authors: Alaa El-Ebshihy, Annisa Maulida Ningtyas, Linda Andersson, Florina Piroi, Andreas Rauber
Venue: CIKM
Year: 2022

Argumentative Zoning (AZ) is a tool to obtain informative summaries of scientific articles. Using AZ assumes the definition of the main rhetorical structure in scientific articles, which are, then, used for the summary creation. The unavailability of large AZ annotated benchmark datasets is a bottleneck to training AZ-based summarization algorithms. In this work, we present an annotation platform for an AZ that defines four categories (zones), Claim, Method, Result and Conclusion, that are used to label sentences selected from scientific articles. The proposed tool can be used both for collecting benchmark datasets, and to help the researchers to create their own sub-corpora.
Federated K-Private Set Intersection
Authors: Ahmed Roushdy Elkordy, Yahya H. Ezzeldin, Salman Avestimehr
Venue: CIKM
Year: 2022

Private set intersection (PSI) is a popular protocol that allows multiple parties to evaluate the intersection of their sets without revealing them to each other. PSI has numerous practical applications, including privacy preserving data mining and location-based services. In this work, we develop a new approach for the PSI problem within the federated analytics framework. In particular, we consider a setting where a server wants to determine (query) which among its local set of data identifiers appears coupled with the same value in at least K of the N parties. Applications for this framework include but are not limited to: double-filing insurance verification, credit scoring and password checkup on an institutional level. To address the proposed setting, we propose a new protocol Fed-K-PSI that allows the server to answer this query while being oblivious to the data of identifiers that do not satisfy the distributed query at the parties. In addition, Fed-K-PSI also maintains the anonymity of the parties by hiding which K parties satisfied the query, or which value associated with the identifier which caused the query to be successful. Our proposed setting does not lend itself directly to state-of-the-art approaches in PSI based on Oblivious Transfer, since the server does not have a complete representation of a datapoint (only the identifier, but no value). Our proposed approach tackles this problem by constructing a distributed function at the parties, which encodes the datapoints and returns a deterministic known property if and only if the value for a given identifier is the same in at least K of the N parties. We show that Fed-K-PSI achieves a strong information-theoretic privacy guarantee and is resilient to collusion scenarios among honest-but-curious parties. We also evaluate Fed-K-PSI via extensive experiments to study the effect of the different system parameters.
From Product Searches to Conversational Agents for E-Commerce
Authors: Giuseppe Di Fabbrizio
Venue: CIKM
Year: 2022

As consumers' demand for online shopping substantially increased in the last few years, e-commerce companies are still far from providing a high-quality user experience that may compete with in-store experiences. On the one hand, matching search queries with highly relevant products for discovery and browsing is still a challenge within existing search technologies. Available e-commerce solutions hardly provide tools to optimize product search relevance and fail to integrate user behavior signals into the search optimization pipeline. On the other hand, accessing the rich and complex information concealed in an e-commerce catalog through a search bar has not evolved far since its initial adoption. In this talk, we illustrate how the VUI conversational AI platform has been successfully adopted to both improve the user's experience quality with highly relevant search and discovery results and expand the traditional search bar with conversational agents' technology, enriching the user's experience at each stage of the e-commerce product life cycle. We review in depth some of the key deep learning models as part of the query understanding component and discuss the overall conversation architecture as it integrates with an existing e-commerce catalog. We include real-life demonstrations derived from use cases extracted from deployed systems.
Detecting Significant Differences Between Information Retrieval Systems via Generalized Linear Models
Authors: Guglielmo Faggioli, Nicola Ferro, Norbert Fuhr
Venue: CIKM
Year: 2022

Being able to compare Information Retrieval(IR) systems correctly is pivotal to improving their quality. Among the most popular tools for statistical significance testing, we list t-test and ANOVA that belong to the linear models family. Therefore, given the relevance of linear models for IR evaluation, a great effort has been devoted to studying how to improve them to better compare IR systems. Linear models rely on assumptions that IR experimental observations rarely meet, e.g. about the normality of the data or the linearity itself. Even though linear models are, in general, resilient to violations of their assumptions, departing from them might reduce the effectiveness of the tests. Hence, we investigate the use of the Generalized Linear Models (GLM) framework, a generalization of the traditional linear modelling that relaxes assumptions about the distribution and the shape of the models. To the best of our knowledge, there has been little or no investigation on the use of GLMs for comparing IR system performance. We discuss how GLM work and how they can be applied in the context of IR evaluation. In particular, we focus on the link function used to build GLMs, which allows for the model to have non-linear shapes. We conduct thorough experimentation using two TREC collections and several evaluation measures. Overall, we show how the log and logit links are able to identify more and more consistent significant differences (up to 25% more with 50 topics) than the identity link used today and with a comparable, or slightly better, risk of publication bias.
Risk-Aware Bid Optimization for Online Display Advertisement
Authors: Rui Fan, Erick Delage
Venue: CIKM
Year: 2022

This research focuses on the bid optimization problem in the real-time bidding setting for online display advertisements, where an advertiser, or the advertiser's agent, has access to the features of the website visitor and the type of ad slots, to decide the optimal bid prices given a predetermined total advertisement budget. We propose a risk-aware data-driven bid optimization model that maximizes the expected profit for the advertiser by exploiting historical data to design upfront a bidding policy, mapping the type of advertisement opportunity to a bid price, and accounting for the risk of violating the budget constraint during a given period of time. After employing a Lagrangian relaxation, we derive a parametrized closed-form expression for the optimal bidding strategy. Using a real-world dataset, we demonstrate that our risk-averse method can effectively control the risk of overspending the budget while achieving a competitive level of profit compared with the risk-neutral model and a state-of-the-art data-driven risk-aware bidding approach.
Smart Contract Scams Detection with Topological Data Analysis on Account Interaction
Authors: Shuhui Fan, Shaojing Fu, Yuchuan Luo, Haoran Xu, Xuyun Zhang, Ming Xu
Venue: CIKM
Year: 2022

The skyrocketing market value of cryptocurrencies has prompted more investors to pour funds into cryptocurrencies to seek asset hedging. However, the anonymity of blockchain makes cryptocurrency naturally a tool of choice for criminals to commit smart contract scams. Consequently, smart contract scam detection is particularly critical for investors to avoid economic loss. Previous methods mainly leverage specific code logic of smart contracts and/or design rules based on abnormal transaction behaviors for scam detection. Although these methods gain success at detecting particular scams, they perform worse when applied to scams with highly similar codes. Besides, well-designed decision rules rely on expert knowledge and tedious data collection steps, which causes poor flexibility. To combat these challenges, we consider the problem of smart contract scam detection via mining topological features of account interaction information that dynamically evolves. We adopt interactive features extracted from dynamic interaction information of accounts and propose a framework named TTG-SCSD to utilize the features and Topological Data Analysis for smart contract scams detection. The TTG-SCSD constructs discrete dynamic interaction graphs for each contract and designs interactive features that characterize account behaviors. The features are modeled combined with a topology quantification mechanism to capture contract intentions in transactions. Experimental results on real-world transaction datasets from Ethereum show that TTG-SCSD obtains better generalizability and improves the performance of the bare versions of the comparison methods.