OpenRaiser/Intern-Atlas huggingface dataset:
SATAR: A Self-supervised Approach to Twitter Account Representation Learning and its Application in Bot Detection
Authors: Shangbin Feng, Herun Wan, Ningnan Wang, Jundong Li, Minnan Luo
Venue: CIKM
Year: 2021

Twitter has become a major social media platform since its launching in 2006, while complaints about bot accounts have increased recently. Although extensive research efforts have been made, the state-of-the-art bot detection methods fall short of generalizability and adaptability. Specifically, previous bot detectors leverage only a small fraction of user information and are often trained on datasets that only cover few types of bots. As a result, they fail to generalize to real-world scenarios on the Twittersphere where different types of bots co-exist. Additionally, bots in Twitter are constantly evolving to evade detection. Previous efforts, although effective once in their context, fail to adapt to new generations of Twitter bots. To address the two challenges of Twitter bot detection, we propose SATAR, a self-supervised representation learning framework of Twitter users, and apply it to the task of bot detection. In particular, SATAR generalizes by jointly leveraging the semantics, property and neighborhood information of a specific user. Meanwhile, SATAR adapts by pre-training on a massive number of self-supervised users and fine-tuning on detailed bot detection scenarios. Extensive experiments demonstrate that SATAR outperforms competitive baselines on different bot detection datasets of varying information completeness and collection time. SATAR is also proved to generalize in real-world scenarios and adapt to evolving generations of social media bots.
CANN: Coupled Approximation Neural Network for Partial Domain Adaptation
Authors: Cheng Feng, Chaoliang Zhong, Jie Wang, Jun Sun, Yasuto Yokota
Venue: CIKM
Year: 2021

Unsupervised domain adaptation (UDA) methods aim to transfer knowledge from a labeled source domain to an unlabeled target domain. Most existing UDA methods try to learn domain-invariant features so that the classifier trained by the source labels can automatically be adapted to the target domain. However, recent works have shown the limitations of these methods when label distributions differ between the source and target domains. Especially, in partial domain adaptation (PDA) where the source domain holds plenty of individual labels (private labels) not appeared in the target domain, the domain-invariant features can cause catastrophic performance degradation. In this paper, based on the originally favorable underlying structures of the two domains, we learn two kinds of target features, i.e., the source-approximate features and target-approximate features instead of the domain-invariant features. The source-approximate features utilize the consistency of the two domains to estimate the distribution of the source private labels. The target-approximate features enhance the feature discrimination in the target domain while detecting the hard (outlier) target samples. A novel Coupled Approximation Neural Network (CANN) has been proposed to co-train the source-approximate and target-approximate features by two parallel sub-networks without sharing the parameters. We apply CANN to three prevalent transfer learning benchmark datasets, Office-Home, Office-31, and Visda2017 with both UDA and PDA settings. The results show that CANN outperforms all baselines by a large margin in PDA and also performs best in UDA.
Popcorn: Human-in-the-loop Popularity Debiasing in Conversational Recommender Systems
Authors: Zuohui Fu, Yikun Xian, Shijie Geng, Gerard de Melo, Yongfeng Zhang
Venue: CIKM
Year: 2021

Recent conversational recommender systems (CRS) provide a promising solution to accurately capture a user's preferences by communicating with users in natural language to interactively guide them while pro-actively eliciting their current interests. Previous research on this mainly focused on either learning a supervised model with semantic features extracted from the user's responses, or training a policy network to control the dialogue state. However, none of them has considered the issue of popularity bias in a CRS. This paper proposes a human-in-the-loop popularity debiasing framework that integrates real-time semantic understanding of open-ended user utterances as well as historical records, while also effectively managing the dialogue with the user. This allows the CRS to balance the recommendation performance as well as the item popularity so as to avoid the well-known "long-tail'' effect. We demonstrate the effectiveness of our approach via experiments on two conversational recommendation datasets, and the results confirm that our proposed approach achieves high-accuracy recommendation while mitigating popularity bias.
Fast and Accurate Anchor Graph-based Label Prediction
Authors: Yasuhiro Fujiwara, Yasutoshi Ida, Atsutoshi Kumagai, Sekitoshi Kanai, Naonori Ueda
Venue: CIKM
Year: 2021

Anchor graphs are a popular tool used in label prediction of sparsely labeled data. In anchor graphs, labels of labeled data are propagated to unlabeled data via anchor points; anchor points are the centers of k-means clusters. Anchor graph-based label prediction determines local weights between data points and anchor points by exploiting Nesterov's method to obtain the graph's adjacency matrix, and it inverts a matrix obtained from the adjacency matrix to predict labels., however, incurs high computation cost since (1) Nesterov's method is applied to all closest anchor points to compute local weights, and (2) the computation cost of the inversion matrix is cubic in the number of anchor points. We propose an approach that can efficiently perform anchor graph-based label prediction because of its two key advances: (1) it prunes unnecessary anchor points so they are not passed to Nesterov's method, and (2) it applies the conjugate gradient method in computing labels of data points to avoid matrix inversion. In addition, we propose to exploit basis vectors computed by SVD as anchor points to improve label prediction accuracy. Experiments show that our approach outperforms the previous approaches in terms of efficiency and accuracy.
Learning An End-to-End Structure for Retrieval in Large-Scale Recommendations
Authors: Weihao Gao, Xiangjun Fan, Chong Wang, Jiankai Sun, Kai Jia, Wenzhi Xiao, Ruofan Ding, Xingyan Bin, Hui Yang, Xiaobing Liu
Venue: CIKM
Year: 2021

One of the core problems in large-scale recommendations is to retrieve top relevant candidates accurately and efficiently, preferably in sub-linear time. Previous approaches are mostly based on a two-step procedure: first learn an inner-product model, and then use some approximate nearest neighbor (ANN) search algorithm to find top candidates. In this paper, we present Deep Retrieval (DR), to learn a retrievable structure directly with user-item interaction data (e.g. clicks) without resorting to the Euclidean space assumption in ANN algorithms. DR's structure encodes all candidate items into a discrete latent space. Those latent codes for the candidates are model parameters and learnt together with other neural network parameters to maximize the same objective function. With the model learnt, a beam search over the structure is performed to retrieve the top candidates for reranking. Empirically, we first demonstrate that DR, with sub-linear computational complexity, can achieve almost the same accuracy as the brute-force baseline on two public datasets. Moreover, we show that, in a live production recommendation system, a deployed DR approach significantly outperforms a well-tuned ANN baseline in terms of engagement metrics. To the best of our knowledge, DR is among the first non-ANN algorithms successfully deployed at the scale of hundreds of millions of items for industrial recommendation systems.
Pruning Meta-Trained Networks for On-Device Adaptation
Authors: Dawei Gao, Xiaoxi He, Zimu Zhou, Yongxin Tong, Lothar Thiele
Venue: CIKM
Year: 2021

Adapting neural networks to unseen tasks with few training samples on resource-constrained devices benefits various Internet-of-Things applications. Such neural networks should learn the new tasks in few shots and be compact in size. Meta-learning enables few-shot learning, yet the meta-trained networks can be over-parameterised. However, naive combination of standard compression techniques like network pruning with meta-learning jeopardises the ability for fast adaptation. In this work, we propose adaptation-aware network pruning (ANP), a novel pruning scheme that works with existing meta-learning methods for a compact network capable of fast adaptation. ANP uses weight importance metric that is based on the sensitivity of the meta-objective rather than the conventional loss function, and adopts approximation of derivatives and layer-wise pruning techniques to reduce the overhead of computing the new importance metric. Evaluations on few-shot classification benchmarks show that ANP can prune meta-trained convolutional and residual networks by 85% without affecting their fast adaptation.
Evaluating Human-AI Hybrid Conversational Systems with Chatbot Message Suggestions
Authors: Zihan Gao, Jiepu Jiang
Venue: CIKM
Year: 2021

AI chatbots can offer suggestions to help humans answer questions by reducing text entry effort and providing relevant knowledge for unfamiliar questions. We study whether chatbot suggestions can help people answer knowledge-demanding questions in a conversation and influence response quality and efficiency. We conducted a large-scale crowdsourcing user study and evaluated 20 hybrid system variants and a human-only baseline. The hybrid systems used four chatbots of varied response quality and differed in the number of suggestions and whether to preset the message box with top suggestions. Experimental results show that chatbot suggestions---even using poor-performing chatbots---have consistently improved response efficiency. Compared with the human-only setting, hybrid systems have reduced response time by 12%--35% and keystrokes by 33%--60%, and users have adopted a suggestion for the final response without any changes in 44%--68% of the cases. In contrast, crowd workers in the human-only setting typed most of the response texts and copied 5% of the answers from other sites. However, we also found that chatbot suggestions did not always help response quality. Specifically, in hybrid systems equipped with poor-performing chatbots, users responded with lower-quality answers than others in the human-only setting. It seems that users would not simply ignore poor suggestions and compose responses as they could without seeing the suggestions. Besides, presetting the message box has improved reply efficiency without hurting response quality. We did not find that showing more suggestions helps or hurts response quality or efficiency consistently. Our study reveals how and when AI chatbot suggestions can help people answer questions in hybrid conversational systems.
To Be or not to Be, Tail Labels in Extreme Multi-label Learning
Authors: Zhiqi Ge, Ximing Li
Venue: CIKM
Year: 2021

EXtreme Multi-label Learning (XML) aims to predict each instance its most relevant subset of labels from an extremely huge label space, often exceeding one million or even larger in many real applications. In XML scenarios, the labels exhibit a long tail distribution, where a significant number of labels appear in very few instances, referred to as tail labels. Unfortunately, due to the lack of positive instances, the tail labels are intractable to learn as well as predict. Several previous studies even suggested that the tail labels can be directly removed by referring to their label frequencies. We consider that such violent principle may miss many significant tail labels, because the predictive accuracy is not strictly consistent with the label frequency especially for tail labels. In this paper, we are interested in finding a reasonable principle to determine whether a tail label should be removed, not only depending on their label frequencies. To this end, we investigate a method named Nearest Neighbor Positive Proportion Score (N2P2S) to score the tail labels by annotations of the instance neighbors. Extensive empirical results indicate that the proposed N2P2S can effectively screen the tail labels, where many preserved tail labels can be learned and accurately predicted even with very few positive instances.
EasyFlinkCEP: Big Event Data Analytics for Everyone
Authors: Nikos Giatrakos, Eleni Kougioumtzi, Antonios Kontaxakis, Antonios Deligiannakis, Yannis Kotidis
Venue: CIKM
Year: 2021

FlinkCEP is the Complex Event Processing (CEP) API of the Flink Big Data platform. The high expressive power of the language of FlinkCEP comes at the cost of cumbersome parameterization of the queried patterns, acting as a barrier for FlinkCEP's adoption. Moreover, properly configuring a FlinkCEP program to run over a computer cluster requires advanced skills on modern hardware administration which non-expert programmers do not possess. In this work (i) we build a novel, logical CEP operator that receives CEP pattern queries in the form of extended regular expressions and seamlessly re-writes them to FlinkCEP programs, (ii) we build a CEP Optimizer that automatically decides good job configurations for these FlinkCEP programs. We also present an experimental evaluation which demonstrates the significant benefits of our approach.
Hierarchical Semantics Matching For Heterogeneous Spatio-temporal Sources
Authors: Daniel Glake, Norbert Ritter, Florian Ocker, Nima Ahmady-Moghaddam, Daniel Osterholz, Ulfia Lenfers, Thomas Clemen
Venue: CIKM
Year: 2021

Spatio-temporal data are semantically valuable information used for various analytical tasks to identify spatially relevant and temporally limited correlations within a domain. The increasing availability and data acquisition from multiple sources with their typically high heterogeneity are getting more and more attention. However, these sources often lack interconnecting shared keys, making their integration a challenging problem. For example, publicly available parking data that consist of point data on parking facilities with fluctuating occupancy and static location data on parking spaces cannot be directly correlated. Both data sets describe two different aspects from distinct sources in which parking spaces and fluctuating occupancy are part of the same semantic model object. Especially for ad hoc analytical tasks on integrated models, these missing relationships cannot be handled using join operations as usual in relational databases. The reason lies in the lack of equijoin relationships, comparing for equality of strings and additional overhead in loading data up before processing. This paper addresses the optimization problem of finding suitable partners in the absence of equijoin relations for heterogeneous spatio-temporal data, applicable to ad hoc analytics. We propose a graph-based approach that achieves good recall and performance scaling via hierarchically separating the semantics along spatial, temporal, and domain-specific dimensions. We evaluate our approach using public data, showing that it is suitable for many standard join scenarios and highlighting its limitations.
Mining Bursty Groups from Interaction Data
Authors: Alexander Gorovits, Lin Zhang, Ekta Gujral, Evangelos E. Papalexakis, Petko Bogdanov
Venue: CIKM
Year: 2021

Empirical studies and theoretical models both highlight burstinessas a common temporal pattern in online behavior. A key driver for burstiness is the self-exciting nature of online interactions. For example, posts in online groups often incite posts in response. Such temporal dependencies are easily lost when interaction data is aggregated in snapshots which are subsequently analyzed independently. An alternative is to model individual interactions as a multi-dimensional self-exciting process, thus, enforcing both temporal and network dependencies. Point processes, however, are challenging to employ for large real-world datasets as fitting them incurs super-linear cost in the number of events. How can we efficiently detect online groups exhibiting bursty self-exciting temporal behavior in large real-world datasets? We propose a bursty group detection framework, called MYRON, which explicitly models self-exciting behavior within groups while also accounting for network-wide baseline activity. MYRON imposes bursty temporal structure within a scalable tensor factorization framework to decouple within-group interactions as interpretable factors. Our framework can incorporate different "shapes"of temporal burstiness via wavelet decomposition or kernels forself-exciting behavior. Our evaluation on both synthetic and real-world data demonstrates MYRON's utility in community detection.It is up to 40% more effective in detecting ground truth groups compared to state-of-the-art baselines. In addition, MYRON is able to uncover interpretable bursty patterns of behavior from user-photo interactions in Flickr.
CDCEO'21 - First Workshop on Complex Data Challenges in Earth Observation
Authors: Aleksandra Gruca, Pedro Herruzo, Pilar Rípodas, Andrzej Kucik, Christian Briese, Michael K. Kopp, Sepp Hochreiter, Pedram Ghamisi, David P. Kreil
Venue: CIKM
Year: 2021

High-resolution remote sensing technology for Earth Observation (EO) has radically changed how we monitor the state of our planet around the clock. An effective interpretation of the resulting complex large-scale time series adopts the best machine learning techniques from signal processing, computer vision, pattern recognition, and artificial intelligence. The First Workshop on Complex Data Challenges in Earth Observation was open to both method development and advanced applications in a wide range of related topics, including image and signal processing, gap-filling, data fusion, feature extraction, prediction of spatio-temporal features, and the detection of rules underlying the observed state transitions and causal relationships. The full agenda, featuring keynotes and a selection of high quality contributed talks is available online at www.iarai.ac.at/cdceo21
Self-Supervised Learning on Users' Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce
Authors: Yulong Gu, Wentian Bao, Dan Ou, Xiang Li, Baoliang Cui, Biyu Ma, Haikuan Huang, Qingwen Liu, Xiaoyi Zeng
Venue: CIKM
Year: 2021

Multi-scenario Learning to Rank is essential for Recommender Systems, Search Engines and Online Advertising in e-commerce portals where the ranking models are usually applied in many scenarios. However, existing works mainly focus on learning the ranking model for a single scenario, and pay less attention to learning ranking models for multiple scenarios. We identify two practical challenges in industrial multi-scenario ranking systems: (1) The Feedback Loop problem that the model is always trained on the items chosen by the ranker itself. (2) Insufficient training data for small and new scenarios. To address the above issues, we present ZEUS, a novel framework that learns a Zoo of ranking modEls for mUltiple Scenarios based on pre-training on users' spontaneous behaviors (e.g. queries which are directly searched in the search box and not recommended by the ranking system). ZEUS decomposes the training process into two stages: self-supervised learning based pre-training and fine-tuning. Firstly, ZEUS performs self-supervised learning on users' spontaneous behaviors and generates a pre-trained model. Secondly, ZEUS fine-tunes the pre-trained model on users' implicit feedback in multiple scenarios. Extensive experiments on Alibaba's production dataset demonstrate the effectiveness of ZEUS, which significantly outperforms state-of-the-art methods. ZEUS averagely achieves 6.0%, 9.7%, 11.7% improvement in CTR, CVR and GMV respectively than state-of-the-art method.
GraphEvolveDroid: Mitigate Model Degradation in the Scenario of Android Ecosystem Evolution
Authors: Yonghao Gu, Liangxun Li
Venue: CIKM
Year: 2021

Machine learning-based Android malware detection models suffer from model degradation over time due to ecosystem evolution, which means models trained on history data perform poorly on newly arrived data. Existing solutions to handle the above problem focus on sophisticated feature engineering to find stable features, which is labor-intensive. In this paper, we try to mitigate model degradation by substituting the representation paradigm from Euclidean (vector) to non-Euclidean (graph) without changing features and propose a graph-based Android malware detection model called GraphEvolveDroid. Specifically, we first construct a directed evolutionary network with the KNN model, where each node represents an APP and the starting APP node of each edge is the ancestor of the ending APP node. Then we use stacked GCN layers to transmit the information of ancestor nodes to child nodes so that the shift of the distribution of child nodes can be suppressed. Experimental results on a large real dataset spanning three years demonstrate that GraphEvolveDroid could significantly mitigate model degradation because of slowing down the shift of data distribution.
NED: Niche Detection in User Content Consumption Data
Authors: Ekta Gujral, Leonardo Neves, Evangelos E. Papalexakis, Neil Shah
Venue: CIKM
Year: 2021

Explainable machine learning methods have attracted increased interest in recent years. In this work, we pose and study the niche detection problem, which imposes an explainable lens on the classical problem of co-clustering interactions across two modes. In the niche detection problem, our goal is to identify niches, or co-clusters with node-attribute oriented explanations. Niche detection is applicable to many social content consumption scenarios, where an end goal is to describe and distill high-level insights about user-content associations: not only that certain users like certain types of content, but rather the types of users and content, explained via node attributes. Some examples are an e-commerce platform with who-buys-what interactions and user and product attributes, or a mobile call platform with who-calls-whom interactions and user attributes. Discovering and characterizing niches has powerful implications for user behavior understanding, as well as marketing and targeted content production. Unlike prior works, ours focuses on the intersection of explainable methods and co-clustering. First, we formalize the niche detection problem and discuss preliminaries. Next, we design an end-to-end framework, NED, which operates in two steps: discovering co-clusters of user behaviors based on interaction densities, and explaining them using attributes of involved nodes. Finally, we show experimental results on several public datasets, as well as a large-scale industrial dataset from Snapchat, demonstrating that NED improves in both co-clustering (20% accuracy) and explanation-related objectives (12% average precision) compared to state-of-the-art methods.
Integrating Transductive and Inductive Embeddings Improves Link Prediction Accuracy
Authors: Chitrank Gupta, Yash Jain, Abir De, Soumen Chakrabarti
Venue: CIKM
Year: 2021

In recent years, inductive graph embedding models, viz., graph neural networks (GNNs) have become increasingly accurate at link prediction (LP) in online social networks. The performance of such networks depends strongly on the input node features, which vary across networks and applications. Selecting appropriate node features remains application-dependent and generally an open question. Moreover, owing to privacy and ethical issues, use of personalized node features is often restricted. In fact, many publicly available data from online social network do not contain any node features (e.g., demography). In this work, we provide a comprehensive experimental analysis which shows that harnessing a transductive technique (e.g., Node2Vec) for obtaining initial node representations, after which an inductive node embedding technique takes over, leads to substantial improvements in link prediction accuracy. We demonstrate that, for a wide variety of GNN variants, node representation vectors obtained from Node2Vec serve as high quality input features to GNNs, thereby improving LP performance.