Yongzhen Wang, Heng Huang, Yuliang Yan, Xiaozhong Liu
Social advertisement has emerged as a viable means to improve purchase sharing in the context of e-commerce. However, humanly generating lots of advertising scripts can be prohibitive to both e-platforms and online sellers, and moreover, developing the desired auto-generator will need substantial gold-standard training samples. In this paper, we put forward a novel seq2seq model to generate social advertisements automatically, in which a quality-sensitive loss function is proposed based on user click behavior to differentiate training samples of varied qualities. Our motivation is to leverage the clickthrough data as a kind of quality indicator to measure the textual fitness of each training sample quantitatively, and only those ground truths that satisfy social media users will be considered the eligible and able to optimize the social advertisement generation. Specifically, under the qualified case, the ground truth should be utilized to supervise the whole training phase as much as possible, whereas in the opposite situation, the generated result ought to preserve the semantics of original input to the greatest extent. Simulation experiments on a large-scale dataset demonstrate that our approach achieves a significant superiority over two existing methods of distant supervision and three state-of-the-art NLG solutions.
{"title":"Quality-Sensitive Training! Social Advertisement Generation by Leveraging User Click Behavior","authors":"Yongzhen Wang, Heng Huang, Yuliang Yan, Xiaozhong Liu","doi":"10.1145/3308558.3313536","DOIUrl":"https://doi.org/10.1145/3308558.3313536","url":null,"abstract":"Social advertisement has emerged as a viable means to improve purchase sharing in the context of e-commerce. However, humanly generating lots of advertising scripts can be prohibitive to both e-platforms and online sellers, and moreover, developing the desired auto-generator will need substantial gold-standard training samples. In this paper, we put forward a novel seq2seq model to generate social advertisements automatically, in which a quality-sensitive loss function is proposed based on user click behavior to differentiate training samples of varied qualities. Our motivation is to leverage the clickthrough data as a kind of quality indicator to measure the textual fitness of each training sample quantitatively, and only those ground truths that satisfy social media users will be considered the eligible and able to optimize the social advertisement generation. Specifically, under the qualified case, the ground truth should be utilized to supervise the whole training phase as much as possible, whereas in the opposite situation, the generated result ought to preserve the semantics of original input to the greatest extent. Simulation experiments on a large-scale dataset demonstrate that our approach achieves a significant superiority over two existing methods of distant supervision and three state-of-the-art NLG solutions.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83601326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Boge Liu, Long Yuan, Xuemin Lin, Lu Qin, W. Zhang, Jingren Zhou
The problem of computing (α, β)-core in a bipartite graph for given α and β is a fundamental problem in bipartite graph analysis and can be used in many applications such as online group recommendation, fraudsters detection, etc. Existing solution to computing (α, β)-core needs to traverse the entire bipartite graph once. Considering the real bipartite graph can be very large and the requests to compute (α, β)-core can be issued frequently in real applications, the existing solution is too expensive to compute the (α, β)-core. In this paper, we present an efficient algorithm based on a novel index such that the algorithm runs in linear time regarding the result size (thus, the algorithm is optimal since it needs at least linear time to output the result). We prove that the index only requires O(m) space where m is the number of edges in the bipartite graph. Moreover, we devise an efficient algorithm with time complexity O(δ·m) for index construction where δ is bounded by √m and is much smaller than √m in practice. We also discuss efficient algorithms to maintain the index when the bipartite graph is dynamically updated and parallel implementation of the index construction algorithm. The experimental results on real and synthetic graphs (more than 1 billion edges) demonstrate that our algorithms achieve up to 5 orders of magnitude speedup for computing (α, β)-core and up to 3 orders of magnitude speedup for index construction, respectively, compared with existing techniques.
{"title":"Efficient (α, β)-core Computation: an Index-based Approach","authors":"Boge Liu, Long Yuan, Xuemin Lin, Lu Qin, W. Zhang, Jingren Zhou","doi":"10.1145/3308558.3313522","DOIUrl":"https://doi.org/10.1145/3308558.3313522","url":null,"abstract":"The problem of computing (α, β)-core in a bipartite graph for given α and β is a fundamental problem in bipartite graph analysis and can be used in many applications such as online group recommendation, fraudsters detection, etc. Existing solution to computing (α, β)-core needs to traverse the entire bipartite graph once. Considering the real bipartite graph can be very large and the requests to compute (α, β)-core can be issued frequently in real applications, the existing solution is too expensive to compute the (α, β)-core. In this paper, we present an efficient algorithm based on a novel index such that the algorithm runs in linear time regarding the result size (thus, the algorithm is optimal since it needs at least linear time to output the result). We prove that the index only requires O(m) space where m is the number of edges in the bipartite graph. Moreover, we devise an efficient algorithm with time complexity O(δ·m) for index construction where δ is bounded by √m and is much smaller than √m in practice. We also discuss efficient algorithms to maintain the index when the bipartite graph is dynamically updated and parallel implementation of the index construction algorithm. The experimental results on real and synthetic graphs (more than 1 billion edges) demonstrate that our algorithms achieve up to 5 orders of magnitude speedup for computing (α, β)-core and up to 3 orders of magnitude speedup for index construction, respectively, compared with existing techniques.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85412842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As an alternative means of convenient and smart transportation, mobility-on-demand (MOD), typified by online ride-sharing and connected taxicabs, has been rapidly growing and spreading worldwide. The large volume of complex traffic and the uncertainty of market supplies/demands have made it essential for many MOD service providers to proactively dispatch vehicles towards ride-seekers. To meet this need effectively, we propose STRide, an MOD coordination-learning mechanism reinforced spatio-temporally with capsules. We formalize the adaptive coordination of vehicles into a reinforcement learning framework. STRide incorporates spatial and temporal distributions of supplies (vehicles) and demands (ride requests), customers' preferences and other external factors. A novel spatio-temporal capsule neural network is designed to predict the provider's rewards based on MOD network states, vehicles and their dispatch actions. This way, the MOD platform adapts itself to the supply-demand dynamics with the best potential rewards. We have conducted extensive data analytics and experimental evaluation with three large-scale datasets (~ 21 million rides from Uber, Yellow Taxis and Didi). STRide is shown to outperform state-of-the-arts, substantially reducing request-rejection rate and passenger waiting time, and also increasing the service provider's profits, often making 30% improvement over state-of-the-arts.
{"title":"Spatio-Temporal Capsule-based Reinforcement Learning for Mobility-on-Demand Network Coordination","authors":"Suining He, K. Shin","doi":"10.1145/3308558.3313401","DOIUrl":"https://doi.org/10.1145/3308558.3313401","url":null,"abstract":"As an alternative means of convenient and smart transportation, mobility-on-demand (MOD), typified by online ride-sharing and connected taxicabs, has been rapidly growing and spreading worldwide. The large volume of complex traffic and the uncertainty of market supplies/demands have made it essential for many MOD service providers to proactively dispatch vehicles towards ride-seekers. To meet this need effectively, we propose STRide, an MOD coordination-learning mechanism reinforced spatio-temporally with capsules. We formalize the adaptive coordination of vehicles into a reinforcement learning framework. STRide incorporates spatial and temporal distributions of supplies (vehicles) and demands (ride requests), customers' preferences and other external factors. A novel spatio-temporal capsule neural network is designed to predict the provider's rewards based on MOD network states, vehicles and their dispatch actions. This way, the MOD platform adapts itself to the supply-demand dynamics with the best potential rewards. We have conducted extensive data analytics and experimental evaluation with three large-scale datasets (~ 21 million rides from Uber, Yellow Taxis and Didi). STRide is shown to outperform state-of-the-arts, substantially reducing request-rejection rate and passenger waiting time, and also increasing the service provider's profits, often making 30% improvement over state-of-the-arts.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85425448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shahar Harel, S. Albo, Eugene Agichtein, Kira Radinsky
Result ranking diversification has become an important issue for web search, summarization, and question answering. For more complex questions with multiple aspects, such as those in community-based question answering (CQA) sites, a retrieval system should provide a diversified set of relevant results, addressing the different aspects of the query, while minimizing redundancy or repetition. We present a new method, DRN , which learns novelty-related features from unlabeled data with minimal social signals, to emphasize diversity in ranking. Specifically, DRN parameterizes question-answer interactions via an LSTM representation, coupled with an extension of neural tensor network, which in turn is combined with a novelty-driven sampling approach to automatically generate training data. DRN provides a novel and general approach to complex question answering diversification and suggests promising directions for search improvements.
{"title":"Learning Novelty-Aware Ranking of Answers to Complex Questions","authors":"Shahar Harel, S. Albo, Eugene Agichtein, Kira Radinsky","doi":"10.1145/3308558.3313457","DOIUrl":"https://doi.org/10.1145/3308558.3313457","url":null,"abstract":"Result ranking diversification has become an important issue for web search, summarization, and question answering. For more complex questions with multiple aspects, such as those in community-based question answering (CQA) sites, a retrieval system should provide a diversified set of relevant results, addressing the different aspects of the query, while minimizing redundancy or repetition. We present a new method, DRN , which learns novelty-related features from unlabeled data with minimal social signals, to emphasize diversity in ranking. Specifically, DRN parameterizes question-answer interactions via an LSTM representation, coupled with an extension of neural tensor network, which in turn is combined with a novelty-driven sampling approach to automatically generate training data. DRN provides a novel and general approach to complex question answering diversification and suggests promising directions for search improvements.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84143539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In many online platforms, people must choose how broadly to allocate their energy. Should one concentrate on a narrow area of focus, and become a specialist, or apply oneself more broadly, and become a generalist? In this work, we propose a principled measure of how generalist or specialist a user is, and study behavior in online platforms through this lens. To do this, we construct highly accurate community embeddings that represent communities in a high-dimensional space. We develop sets of community analogies and use them to optimize our embeddings so that they encode community relationships extremely well. Based on these embeddings, we introduce a natural measure of activity diversity, the GS-score. Applying our embedding-based measure to online platforms, we observe a broad spectrum of user activity styles, from extreme specialists to extreme generalists, in both community membership on Reddit and programming contributions on GitHub. We find that activity diversity is related to many important phenomena of user behavior. For example, specialists are much more likely to stay in communities they contribute to, but generalists are much more likely to remain on platforms as a whole. We also find that generalists engage with significantly more diverse sets of users than specialists do. Furthermore, our methodology leads to a simple algorithm for community recommendation, matching state-of-the-art methods like collaborative filtering. Our methods and results introduce an important new dimension of online user behavior and shed light on many aspects of online platform use.
{"title":"Generalists and Specialists: Using Community Embeddings to Quantify Activity Diversity in Online Platforms","authors":"Isaac Waller, Ashton Anderson","doi":"10.1145/3308558.3313729","DOIUrl":"https://doi.org/10.1145/3308558.3313729","url":null,"abstract":"In many online platforms, people must choose how broadly to allocate their energy. Should one concentrate on a narrow area of focus, and become a specialist, or apply oneself more broadly, and become a generalist? In this work, we propose a principled measure of how generalist or specialist a user is, and study behavior in online platforms through this lens. To do this, we construct highly accurate community embeddings that represent communities in a high-dimensional space. We develop sets of community analogies and use them to optimize our embeddings so that they encode community relationships extremely well. Based on these embeddings, we introduce a natural measure of activity diversity, the GS-score. Applying our embedding-based measure to online platforms, we observe a broad spectrum of user activity styles, from extreme specialists to extreme generalists, in both community membership on Reddit and programming contributions on GitHub. We find that activity diversity is related to many important phenomena of user behavior. For example, specialists are much more likely to stay in communities they contribute to, but generalists are much more likely to remain on platforms as a whole. We also find that generalists engage with significantly more diverse sets of users than specialists do. Furthermore, our methodology leads to a simple algorithm for community recommendation, matching state-of-the-art methods like collaborative filtering. Our methods and results introduce an important new dimension of online user behavior and shed light on many aspects of online platform use.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73372946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Social influence plays a vital role in shaping a user's behavior in online communities dealing with items of fine taste like movies, food, and beer. For online recommendation, this implies that users' preferences and ratings are influenced due to other individuals. Given only time-stamped reviews of users, can we find out who-influences-whom, and characteristics of the underlying influence network? Can we use this network to improve recommendation? While prior works in social-aware recommendation have leveraged social interaction by considering the observed social network of users, many communities like Amazon, Beeradvocate, and Ratebeer do not have explicit user-user links. Therefore, we propose GhostLink, an unsupervised probabilistic graphical model, to automatically learn the latent influence network underlying a review community - given only the temporal traces (timestamps) of users' posts and their content. Based on extensive experiments with four real-world datasets with 13 million reviews, we show that GhostLink improves item recommendation by around 23% over state-of-the-art methods that do not consider this influence. As additional use-cases, we show that GhostLink can be used to differentiate between users' latent preferences and influenced ones, as well as to detect influential users based on the learned influence graph.
{"title":"GhostLink: Latent Network Inference for Influence-aware Recommendation","authors":"Subhabrata Mukherjee, Stephan Günnemann","doi":"10.1145/3308558.3313449","DOIUrl":"https://doi.org/10.1145/3308558.3313449","url":null,"abstract":"Social influence plays a vital role in shaping a user's behavior in online communities dealing with items of fine taste like movies, food, and beer. For online recommendation, this implies that users' preferences and ratings are influenced due to other individuals. Given only time-stamped reviews of users, can we find out who-influences-whom, and characteristics of the underlying influence network? Can we use this network to improve recommendation? While prior works in social-aware recommendation have leveraged social interaction by considering the observed social network of users, many communities like Amazon, Beeradvocate, and Ratebeer do not have explicit user-user links. Therefore, we propose GhostLink, an unsupervised probabilistic graphical model, to automatically learn the latent influence network underlying a review community - given only the temporal traces (timestamps) of users' posts and their content. Based on extensive experiments with four real-world datasets with 13 million reviews, we show that GhostLink improves item recommendation by around 23% over state-of-the-art methods that do not consider this influence. As additional use-cases, we show that GhostLink can be used to differentiate between users' latent preferences and influenced ones, as well as to detect influential users based on the learned influence graph.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"496 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77072112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Microservice architectures and container technologies are broadly adopted by giant internet companies to support their web services, which typically have a strict service-level objective (SLO), tail latency, rather than average latency. However, diagnosing SLO violations, e.g., long tail latency problem, is non-trivial for large-scale web applications in shared microservice platforms due to million-level operational data and complex operational environments. We identify a new type of tail latency problem for web services, small-window long-tail latency (SWLT), which is typically aggregated during a small statistical window (e.g., 1-minute or 1-second). We observe SWLT usually occurs in a small number of containers in microservice clusters and sharply shifts among different containers at different time points. To diagnose root-causes of SWLT, we propose an unsupervised and low-cost diagnosis algorithm-?-Diagnosis, using two-sample test algorithm and ?-statistics for measuring similarity of time series to identify root-cause metrics from millions of metrics. We implement and deploy a real-time diagnosis system in our real-production microservice platforms. The evaluation using real web application datasets demonstrates that ?-Diagnosis can identify all the actual root-causes at runtime and significantly reduce the candidate problem space, outperforming other time-series distance based root-cause analysis algorithms.
{"title":"?-Diagnosis: Unsupervised and Real-time Diagnosis of Small- window Long-tail Latency in Large-scale Microservice Platforms","authors":"Huasong Shan, Yuan Chen, Haifeng Liu, Yunpeng Zhang, Xiao Xiao, Xiaofeng He, Min Li, Wei Ding","doi":"10.1145/3308558.3313653","DOIUrl":"https://doi.org/10.1145/3308558.3313653","url":null,"abstract":"Microservice architectures and container technologies are broadly adopted by giant internet companies to support their web services, which typically have a strict service-level objective (SLO), tail latency, rather than average latency. However, diagnosing SLO violations, e.g., long tail latency problem, is non-trivial for large-scale web applications in shared microservice platforms due to million-level operational data and complex operational environments. We identify a new type of tail latency problem for web services, small-window long-tail latency (SWLT), which is typically aggregated during a small statistical window (e.g., 1-minute or 1-second). We observe SWLT usually occurs in a small number of containers in microservice clusters and sharply shifts among different containers at different time points. To diagnose root-causes of SWLT, we propose an unsupervised and low-cost diagnosis algorithm-?-Diagnosis, using two-sample test algorithm and ?-statistics for measuring similarity of time series to identify root-cause metrics from millions of metrics. We implement and deploy a real-time diagnosis system in our real-production microservice platforms. The evaluation using real web application datasets demonstrates that ?-Diagnosis can identify all the actual root-causes at runtime and significantly reduce the candidate problem space, outperforming other time-series distance based root-cause analysis algorithms.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82581232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Feng, Mingyang Zhang, Huandong Wang, Zeyu Yang, Chao Zhang, Yong Li, Depeng Jin
Online services are playing critical roles in almost all aspects of users' life. Users usually have multiple online identities (IDs) in different online services. In order to fuse the separated user data in multiple services for better business intelligence, it is critical for service providers to link online IDs belonging to the same user. On the other hand, the popularity of mobile networks and GPS-equipped smart devices have provided a generic way to link IDs, i.e., utilizing the mobility traces of IDs. However, linking IDs based on their mobility traces has been a challenging problem due to the highly heterogeneous, incomplete and noisy mobility data across services. In this paper, we propose DPLink, an end-to-end deep learning based framework, to complete the user identity linkage task for heterogeneous mobility data collected from different services with different properties. DPLink is made up by a feature extractor including a location encoder and a trajectory encoder to extract representative features from trajectory and a comparator to compare and decide whether to link two trajectories as the same user. Particularly, we propose a pre-training strategy with a simple task to train the DPLink model to overcome the training difficulties introduced by the highly heterogeneous nature of different source mobility data. Besides, we introduce a multi-modal embedding network and a co-attention mechanism in DPLink to deal with the low-quality problem of mobility data. By conducting extensive experiments on two real-life ground-truth mobility datasets with eight baselines, we demonstrate that DPLink outperforms the state-of-the-art solutions by more than 15% in terms of hit-precision. Moreover, it is expandable to add external geographical context data and works stably with heterogeneous noisy mobility traces. Our code is publicly available1.
{"title":"DPLink: User Identity Linkage via Deep Neural Network From Heterogeneous Mobility Data","authors":"J. Feng, Mingyang Zhang, Huandong Wang, Zeyu Yang, Chao Zhang, Yong Li, Depeng Jin","doi":"10.1145/3308558.3313424","DOIUrl":"https://doi.org/10.1145/3308558.3313424","url":null,"abstract":"Online services are playing critical roles in almost all aspects of users' life. Users usually have multiple online identities (IDs) in different online services. In order to fuse the separated user data in multiple services for better business intelligence, it is critical for service providers to link online IDs belonging to the same user. On the other hand, the popularity of mobile networks and GPS-equipped smart devices have provided a generic way to link IDs, i.e., utilizing the mobility traces of IDs. However, linking IDs based on their mobility traces has been a challenging problem due to the highly heterogeneous, incomplete and noisy mobility data across services. In this paper, we propose DPLink, an end-to-end deep learning based framework, to complete the user identity linkage task for heterogeneous mobility data collected from different services with different properties. DPLink is made up by a feature extractor including a location encoder and a trajectory encoder to extract representative features from trajectory and a comparator to compare and decide whether to link two trajectories as the same user. Particularly, we propose a pre-training strategy with a simple task to train the DPLink model to overcome the training difficulties introduced by the highly heterogeneous nature of different source mobility data. Besides, we introduce a multi-modal embedding network and a co-attention mechanism in DPLink to deal with the low-quality problem of mobility data. By conducting extensive experiments on two real-life ground-truth mobility datasets with eight baselines, we demonstrate that DPLink outperforms the state-of-the-art solutions by more than 15% in terms of hit-precision. Moreover, it is expandable to add external geographical context data and works stably with heterogeneous noisy mobility traces. Our code is publicly available1.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78667909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modeling people's activities in the urban space is a crucial socio-economic task but extremely challenging due to the deficiency of suitable methods. To model the temporal dynamics of human activities concisely and specifically, we present State-sharing Hidden Markov Model (SSHMM). First, it extracts the urban states from the whole city, which captures the volume of population flows as well as the frequency of each type of Point of Interests (PoIs) visited. Second, it characterizes the urban dynamics of each urban region as the state transition on the shared-states, which reveals distinct daily rhythms of urban activities. We evaluate our method via a large-scale real-life mobility dataset and results demonstrate that SSHMM learns semantics-rich urban dynamics, which are highly correlated with the functions of the region. Besides, it recovers the urban dynamics in different time slots with an error of 0.0793, which outperforms the general HMM by 54.2%.
{"title":"Understanding Urban Dynamics via State-sharing Hidden Markov Model","authors":"Tong Xia, Yue Yu, Fengli Xu, Funing Sun, Diansheng Guo, Depeng Jin, Yong Li","doi":"10.1145/3308558.3313453","DOIUrl":"https://doi.org/10.1145/3308558.3313453","url":null,"abstract":"Modeling people's activities in the urban space is a crucial socio-economic task but extremely challenging due to the deficiency of suitable methods. To model the temporal dynamics of human activities concisely and specifically, we present State-sharing Hidden Markov Model (SSHMM). First, it extracts the urban states from the whole city, which captures the volume of population flows as well as the frequency of each type of Point of Interests (PoIs) visited. Second, it characterizes the urban dynamics of each urban region as the state transition on the shared-states, which reveals distinct daily rhythms of urban activities. We evaluate our method via a large-scale real-life mobility dataset and results demonstrate that SSHMM learns semantics-rich urban dynamics, which are highly correlated with the functions of the region. Besides, it recovers the urban dynamics in different time slots with an error of 0.0793, which outperforms the general HMM by 54.2%.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75999305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Saini, Sunny Dhamnani, Aakash Srinivasan, A. A. Ibrahim, Prithviraj Chavan
Causal inference using observational data on multiple treatments is an important problem in a wide variety of fields. However, the existing literature tends to focus only on causal inference in case of binary or multinoulli treatments. These models are either incompatible with multiple treatments, or extending them to multiple treatments is computationally expensive. We use a previous formulation of causal inference using variational autoencoder (VAE) and propose a novel architecture to estimate the causal effect of any subset of the treatments. The higher order effects of multiple treatments are captured through a task embedding. The task embedding allows the model to scale to multiple treatments. The model is applied on real digital marketing dataset to evaluate the next best set of marketing actions. For evaluation, the model is compared against competitive baseline models on two semi-synthetic datasets created using the covariates from the real dataset. The performance is measured along four evaluation metrics considered in the causal inference literature and one proposed by us. The proposed evaluation metric measures the loss in the expected outcome when a particular model is used for decision making as compared to the ground truth. The proposed model outperforms the baselines along all five evaluation metrics. It outperforms the best baseline by over 30% along these evaluation metrics. The proposed approach is also shown to be robust when a subset of the confounders is not observed. The results on real data show the importance of the flexible modeling approach provided by the proposed model.
{"title":"Multiple Treatment Effect Estimation using Deep Generative Model with Task Embedding","authors":"S. Saini, Sunny Dhamnani, Aakash Srinivasan, A. A. Ibrahim, Prithviraj Chavan","doi":"10.1145/3308558.3313744","DOIUrl":"https://doi.org/10.1145/3308558.3313744","url":null,"abstract":"Causal inference using observational data on multiple treatments is an important problem in a wide variety of fields. However, the existing literature tends to focus only on causal inference in case of binary or multinoulli treatments. These models are either incompatible with multiple treatments, or extending them to multiple treatments is computationally expensive. We use a previous formulation of causal inference using variational autoencoder (VAE) and propose a novel architecture to estimate the causal effect of any subset of the treatments. The higher order effects of multiple treatments are captured through a task embedding. The task embedding allows the model to scale to multiple treatments. The model is applied on real digital marketing dataset to evaluate the next best set of marketing actions. For evaluation, the model is compared against competitive baseline models on two semi-synthetic datasets created using the covariates from the real dataset. The performance is measured along four evaluation metrics considered in the causal inference literature and one proposed by us. The proposed evaluation metric measures the loss in the expected outcome when a particular model is used for decision making as compared to the ground truth. The proposed model outperforms the baselines along all five evaluation metrics. It outperforms the best baseline by over 30% along these evaluation metrics. The proposed approach is also shown to be robust when a subset of the confounders is not observed. The results on real data show the importance of the flexible modeling approach provided by the proposed model.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87935074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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