OpenRaiser/Intern-Atlas huggingface dataset:
Point-of-interest demand modeling with human mobility patterns
Authors: Yanchi Liu, Chuanren Liu, Xinjiang Lu, Mingfei Teng, Hengshu Zhu, Hui Xiong
Venue: Conference KDD 2017
Year: 2017

Point-of-Interest (POI) demand modeling in urban regions is critical for many applications such as business site selection and real estate investment. While some efforts have been made for the demand analysis of some specific POI categories, such as restaurants, it lacks systematic means to support POI demand modeling. To this end, in this paper, we develop a systematic POI demand modeling framework, named Region POI Demand Identification (RPDI), to model POI demands by exploiting the daily needs of people identified from their large-scale mobility data. Specifically, we first partition the urban space into spatially differentiated neighborhood regions formed by many small local communities. Then, the daily activity patterns of people traveling in the city will be extracted from human mobility data. Since the trip activities, even aggregated, are sparse and insufficient to directly identify the POI demands, especially for underdeveloped regions, we develop a latent factor model that integrates human mobility data, POI profiles, and demographic data to robustly model the POI demand of urban regions in a holistic way. In this model, POI preferences and supplies are used together with demographic features to estimate the POI demands simultaneously for all the urban regions interconnected in the city. Moreover, we also design efficient algorithms to optimize the latent model for large-scale data. Finally, experimental results on real-world data in New York City (NYC) show that our method is effective for identifying POI demands for different regions.
Inferring Demographics and Social Networks of Mobile Device Users on Campus From AP-Trajectories
Authors: Pinghui Wang, Feiyang Sun, Di Wang, Jing Tao, Xiaohong Guan, Albert Bifet
Venue: Conference WWW 2017
Year: 2017

Exploring demographics and social networks of Internet users are widely used for many applications such as recommendation systems. The popularity of mobile devices (e.g., smartphones) and location-based Internet services (e.g., Google Maps) facilitates the collection of users' locations over time. Despite recent efforts to predict users' attributes (e.g., age and gender) and social networks based on utilizing the rich location context knowledge (e.g., name, type, and description) of places of interest (e.g., restaurants and hotels) they checked-in on location-based online social networks such as Foursqure and Gowalla, little attention has been given to inferring attributes and social networks of mobile device users based on their spatiotemporal trajectories with less/no location context knowledge. In this paper we collect logs of thousands of mobile devices' network connections to wireless access points (APs) of two campuses, and investigate whether one can infer mobile device users' demographic attributes and social networks solely from their spatiotemporal AP-trajectories. We develop a tensor factorization based method Dinfer to infer mobile device users' demographic attributes from their AP-trajectories by leveraging prior knowledge, such as users' social networks. We also propose a novel method Sinfer to learn social networks between mobile device users by exploring patterns of their AP-trajectories, such as fine-grained co-occurrence events (e.g., co-coming, co-leaving, and co-presenting duration). Experimental results on real-word datasets demonstrate the effectiveness of our methods.
Next point-of-interest recommendation on resource-constrained mobile devices
Authors: Qinyong Wang, Hongzhi Yin, Tong Chen, Zi Huang, Hao Wang, Yanchang Zhao, Nguyen Quoc Viet Hung
Venue: Conference WWW 2020
Year: 2020

In the modern tourism industry, next point-of-interest (POI) recommendation is an important mobile service as it effectively aids hesitating travelers to decide the next POI to visit. Currently, most next POI recommender systems are built upon a cloud-based paradigm, where the recommendation models are trained and deployed on the powerful cloud servers. When a recommendation request is made by a user via mobile devices, the current contextual information will be uploaded to the cloud servers to help the well-trained models generate personalized recommendation results. However, in reality, this paradigm heavily relies on high-quality network connectivity, and is subject to high energy footprint in the operation and increasing privacy concerns among the public. To bypass these defects, we propose a novel Light Location Recommender System (LLRec) to perform next POI recommendation locally on resource-constrained mobile devices. To make LLRec fully compatible with the limited computing resources and memory space, we leverage FastGRNN, a lightweight but effective gated Recurrent Neural Network (RNN) as its main building block, and significantly compress the model size by adopting the tensor-train composition in the embedding layer. As a compact model, LLRec maintains its robustness via an innovative teacher-student training framework, where a powerful teacher model is trained on the cloud to learn essential knowledge from available contextual data, and the simplified student model LLRec is trained under the guidance of the teacher model. The final LLRec is downloaded and deployed on users’ mobile devices to generate accurate recommendations solely utilizing users’ local data. As a result, LLRec significantly reduces the dependency on cloud servers, thus allowing for next POI recommendation in a stable, cost-effective and secure way. Extensive experiments on two large-scale recommendation datasets further demonstrate the superiority of our proposed solution.
You are where you go: Inferring demographic attributes from location check-ins
Authors: Yuan Zhong, Nicholas Jing Yuan, Wen Zhong, Fuzheng Zhang, Xing Xie
Venue: Conference WSDM 2015
Year: 2015

User profiling is crucial to many online services. Several recent studies suggest that demographic attributes are predictable from different online behavioral data, such as users' "Likes" on Facebook, friendship relations, and the linguistic characteristics of tweets. But location check-ins, as a bridge of users' offline and online lives, have by and large been overlooked in inferring user profiles. In this paper, we investigate the predictive power of location check-ins for inferring users' demographics and propose a simple yet general location to profile (L2P) framework. More specifically, we extract rich semantics of users' check-ins in terms of spatiality, temporality, and location knowledge, where the location knowledge is enriched with semantics mined from heterogeneous domains including both online customer review sites and social networks. Additionally, tensor factorization is employed to draw out low dimensional representations of users' intrinsic check-in preferences considering the above factors. Meanwhile, the extracted features are used to train predictive models for inferring various demographic attributes.
St-norm: Spatial and temporal normalization for multi-variate time series forecasting
Authors: Jinliang Deng, Xiusi Chen, Renhe Jiang, Xuan Song, Ivor W. Tsang
Venue: Conference KDD 2021
Year: 2021

Multi-variate time series (MTS) data is a ubiquitous class of data abstraction in the real world. Any instance of MTS is generated from a hybrid dynamical system with their specific dynamics normally unknown. The hybrid nature of such a dynamical system is a result of complex external impacts, which can be summarized as high-frequency and low-frequency from the temporal view, or global and local if we take the spatial view. These impacts also determine the forthcoming development of MTS making them paramount to capture in a time series forecasting task. However, conventional methods face intrinsic difficulties in disentangling the components yielded by each kind of impact from the raw data. To this end, we propose two kinds of normalization modules -- temporal and spatial normalization -- which separately refine the high-frequency component and the local component underlying the raw data. Moreover, both modules can be readily integrated into canonical deep learning architectures such as Wavenet and Transformer. Extensive experiments on three datasets are conducted to illustrate that, with additional normalization modules, the performance of the canonical architectures can be enhanced by a large margin in the application of MTS and achieves state-of-the-art results compared with existing MTS models.
Mist: A multiview and multimodal spatial-temporal learning framework for citywide abnormal event forecasting
Authors: Chao Huang, Chuxu Zhang, Jiashu Zhao, Xian Wu, Nitesh V. Chawla, Dawei Yin
Venue: Conference WWW 2019
Year: 2019

Citywide abnormal events, such as crimes and accidents, may result in loss of lives or properties if not handled efficiently. It is important for a wide spectrum of applications, ranging from public order maintaining, disaster control and people's activity modeling, if abnormal events can be automatically predicted before they occur. However, forecasting different categories of citywide abnormal events is very challenging as it is affected by many complex factors from different views: (i) dynamic intra-region temporal correlation; (ii) complex inter-region spatial correlations; (iii) latent cross-categorical correlations. In this paper, we develop a Multi-View and Multi-Modal Spatial-Temporal learning (MiST) framework to address the above challenges by promoting the collaboration of different views (spatial, temporal and semantic) and map the multi-modal units into the same latent space. Specifically, MiST can preserve the underlying structural information of multi-view abnormal event data and automatically learn the importance of view-specific representations, with the integration of a multi-modal pattern fusion module and a hierarchical recurrent framework. Extensive experiments on three real-world datasets, i.e., crime data and urban anomaly data, demonstrate the superior performance of our MiST method over the state-of-the-art baselines across various settings.
Automatic Expert Selection for Multi-Scenario and Multi-Task Search
Authors: Xinyu Zou, Zhi Hu, Yiming Zhao, Xuchu Ding, Zhongyi Liu, Chenliang Li, Aixin Sun
Venue: Conference SIGIR 2022
Year: 2022

Multi-scenario learning (MSL) enables a service provider to cater for users' fine-grained demands by separating services for different user sectors, e.g., by user's geographical region. Under each scenario there is a need to optimize multiple task-specific targets e.g., click through rate and conversion rate, known as multi-task learning (MTL). Recent solutions for MSL and MTL are mostly based on the multi-gate mixture-of-experts (MMoE) architecture. MMoE structure is typically static and its design requires domain-specific knowledge, making it less effective in handling both MSL and MTL. In this paper, we propose a novel Automatic Expert Selection framework for Multi-scenario and Multi-task search, named AESM^{2}. AESM^{2} integrates both MSL and MTL into a unified framework with an automatic structure learning. Specifically, AESM^{2} stacks multi-task layers over multi-scenario layers. This hierarchical design enables us to flexibly establish intrinsic connections between different scenarios, and at the same time also supports high-level feature extraction for different tasks. At each multi-scenario/multi-task layer, a novel expert selection algorithm is proposed to automatically identify scenario-/task-specific and shared experts for each input. Experiments over two real-world large-scale datasets demonstrate the effectiveness of AESM^{2} over a battery of strong baselines. Online A/B test also shows substantial performance gain on multiple metrics. Currently, AESM^{2} has been deployed online for serving major traffic.
Making the most of your triple store: query answering in OWL 2 using an RL reasoner
Authors: Yujiao Zhou, Bernardo Cuenca Grau, Ian Horrocks, Zhe Wu, Jay Banerjee
Venue: Conference WWW 2013
Year: 2013

Triple stores implementing the RL profile of OWL 2 are becoming increasingly popular. In contrast to unrestricted OWL 2, the RL profile is known to enjoy favourable computational properties for query answering, and state-of-the-art RL reasoners such as OWLim and Oracle's native inference engine of Oracle Spatial and Graph have proved extremely successful in industry-scale applications. The expressive restrictions imposed by OWL 2 RL may, however, be problematical for some applications. In this paper, we propose novel techniques that allow us (in many cases) to compute exact query answers using an off-the-shelf RL reasoner, even when the ontology is outside the RL profile. Furthermore, in the cases where exact query answers cannot be computed, we can still compute both lower and upper bounds on the exact answers. These bounds allow us to estimate the degree of incompleteness of the RL reasoner on the given query, and to optimise the computation of exact answers using a fully-fledged OWL 2 reasoner. A preliminary evaluation using the RDF Semantic Graph feature in Oracle Database has shown very promising results with respect to both scalability and tightness of the bounds.
Attack under Disguise: An Intelligent Data Poisoning Attack Mechanism in Crowdsourcing
Authors: Chenglin Miao, Qi Li, Lu Su, Mengdi Huai, Wenjun Jiang, Jing Gao
Venue: Conference WWW 2018
Year: 2018

As an effective way to solicit useful information from the crowd, crowdsourcing has emerged as a popular paradigm to solve challenging tasks. However, the data provided by the participating workers are not always trustworthy. In real world, there may exist malicious workers in crowdsourcing systems who conduct the data poisoning attacks for the purpose of sabotage or financial rewards. Although data aggregation methods such as majority voting are conducted on workers» labels in order to improve data quality, they are vulnerable to such attacks as they treat all the workers equally. In order to capture the variety in the reliability of workers, the Dawid-Skene model, a sophisticated data aggregation method, has been widely adopted in practice. By conducting maximum likelihood estimation (MLE) using the expectation maximization (EM) algorithm, the Dawid-Skene model can jointly estimate each worker»s reliability and conduct weighted aggregation, and thus can tolerate the data poisoning attacks to some degree. However, the Dawid-Skene model still has weakness. In this paper, we study the data poisoning attacks against such crowdsourcing systems with the Dawid-Skene model empowered. We design an intelligent attack mechanism, based on which the attacker can not only achieve maximum attack utility but also disguise the attacking behaviors. Extensive experiments based on real-world crowdsourcing datasets are conducted to verify the desirable properties of the proposed mechanism.
Iolaus: Securing online content rating systems
Authors: Arash Molavi Kakhki, Chloe Kliman-Silver, Alan Mislove
Venue: Conference WWW 2013
Year: 2013

Online content ratings services allow users to find and share content ranging from news articles (Digg) to videos (YouTube) to businesses (Yelp). Generally, these sites allow users to create accounts, declare friendships, upload and rate content, and locate new content by leveraging the aggregated ratings of others. These services are becoming increasingly popular; Yelp alone has over 33 million reviews. Unfortunately, this popularity is leading to increasing levels of malicious activity, including multiple identity (Sybil) attacks and the "buying" of ratings from users.
Sybil defense in crowdsourcing platforms
Authors: Dong Yuan, Guoliang Li, Qi Li, Yudian Zheng
Venue: Conference CIKM 2017
Year: 2017

Crowdsourcing platforms have been widely deployed to solve many computer-hard problems, e.g., image recognition and entity resolution. Quality control is an important issue in crowdsourcing, which has been extensively addressed by existing quality-control algorithms, e.g., voting-based algorithms and probabilistic graphical models. However, these algorithms cannot ensure quality under sybil attacks, which leverages a large number of sybil accounts to generate results for dominating answers of normal workers. To address this problem, we propose a sybil defense framework for crowdsourcing, which can help crowdsourcing platforms to identify sybil workers and defense the sybil attack. We develop a similarity function to quantify worker similarity. Based on worker similarity, we cluster workers into different groups such that we can utilize a small number of golden questions to accurately identify the sybil groups. We also devise online algorithms to instantly detect sybil workers to throttle the attacks. Our method also has ability to detect multi-attackers in one task. To the best of our knowledge, this is the first framework for sybil defense in crowdsourcing. Experimental results on real-world datasets demonstrate that our method can effectively identify and throttle sybil workers.
Near-optimal algorithms for autonomous exploration and multi-goal stochastic shortest path
Authors: Haoyuan Cai, Tengyu Ma 0001, Simon S. Du
Venue: Conference ICML 2022
Year: 2022

We revisit the incremental autonomous exploration problem proposed by Lim & Auer (2012). In this setting, the agent aims to learn a set of near-optimal goal-conditioned policies to reach the $L$-controllable states: states that are incrementally reachable from an initial state $s_0$ within $L$ steps in expectation. We introduce a new algorithm with stronger sample complexity bounds than existing ones. Furthermore, we also prove the first lower bound for the autonomous exploration problem. In particular, the lower bound implies that our proposed algorithm, Value-Aware Autonomous Exploration, is nearly minimax-optimal when the number of $L$-controllable states grows polynomially with respect to $L$. Key in our algorithm design is a connection between autonomous exploration and multi-goal stochastic shortest path, a new problem that naturally generalizes the classical stochastic shortest path problem. This new problem and its connection to autonomous exploration can be of independent interest.
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic
Authors: Mikael Henaff, Alfredo Canziani, Yann LeCun
Venue: Conference ICLR 2019
Year: 2019

Learning a policy using only observational data is challenging because the distribution of states it induces at execution time may differ from the distribution observed during training. We propose to train a policy by unrolling a learned model of the environment dynamics over multiple time steps while explicitly penalizing two costs: the original cost the policy seeks to optimize, and an uncertainty cost which represents its divergence from the states it is trained on. We measure this second cost by using the uncertainty of the dynamics model about its own predictions, using recent ideas from uncertainty estimation for deep networks. We evaluate our approach using a large-scale observational dataset of driving behavior recorded from traffic cameras, and show that we are able to learn effective driving policies from purely observational data, with no environment interaction.
Dependency-aware self-training for entity alignment
Authors: Bing Liu, Tiancheng Lan, Wen Hua, Guido Zuccon
Venue: Conference WSDM 2023
Year: 2023

Entity Alignment (EA), which aims to detect entity mappings (i.e. equivalent entity pairs) in different Knowledge Graphs (KGs), is critical for KG fusion. Neural EA methods dominate current EA research but still suffer from their reliance on labelled mappings. To solve this problem, a few works have explored boosting the training of EA models with self-training, which adds confidently predicted mappings into the training data iteratively. Though the effectiveness of self-training can be glimpsed in some specific settings, we still have very limited knowledge about it. One reason is the existing works concentrate on devising EA models and only treat self-training as an auxiliary tool. To fill this knowledge gap, we change the perspective to self-training to shed light on it. In addition, the existing self-training strategies have limited impact because they introduce either much False Positive noise or a low quantity of True Positive pseudo mappings. To improve self-training for EA, we propose exploiting the dependencies between entities, a particularity of EA, to suppress the noise without hurting the recall of True Positive mappings. Through extensive experiments, we show that the introduction of dependency makes the self-training strategy for EA reach a new level. The value of self-training in alleviating the reliance on annotation is actually much higher than what has been realised. Furthermore, we suggest future study on smart data annotation to break the ceiling of EA performance.
MoFlow: an invertible flow model for generating molecular graphs
Authors: Chengxi Zang, Fei Wang 0001
Venue: Conference KDD 2020
Year: 2020

Generating molecular graphs with desired chemical properties driven by deep graph generative models provides a very promising way to accelerate drug discovery process. Such graph generative models usually consist of two steps: learning latent representations and generation of molecular graphs. However, to generate novel and chemically-valid molecular graphs from latent representations is very challenging because of the chemical constraints and combinatorial complexity of molecular graphs. In this paper, we propose MoFlow, a flow-based graph generative model to learn invertible mappings between molecular graphs and their latent representations. To generate molecular graphs, our MoFlow first generates bonds (edges) through a Glow based model, then generates atoms (nodes) given bonds by a novel graph conditional flow, and finally assembles them into a chemically valid molecular graph with a posthoc validity correction. Our MoFlow has merits including exact and tractable likelihood training, efficient one-pass embedding and generation, chemical validity guarantees, 100% reconstruction of training data, and good generalization ability. We validate our model by four tasks: molecular graph generation and reconstruction, visualization of the continuous latent space, property optimization, and constrained property optimization. Our MoFlow achieves state-of-the-art performance, which implies its potential efficiency and effectiveness to explore large chemical space for drug discovery.