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| 文献发表年限 | 2019 | 创建时间 | 2019-12-02 | ||||||||
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| 摘要描述 | Recent technological advances, especially in the field of machine learning, provide astonishing progress on the road towards artificial general intelligence. However, tasks in current real-world business applications cannot yet be solved by machines alone. We, therefore, identify the need for developing socio-technological ensembles of humans and machines. Such systems possess the ability to accomplish complex goals by combining human and artificial intelligence to collectively achieve superior results and continuously improve by learning from each other. Thus, the need for structured design knowledge for those systems arises. Following a taxonomy development method, this article provides three main contributions: First, we present a structured overview of interdisciplinary research on the role of humans in the machine learning pipeline. Second, we envision hybrid intelligence systems and conceptualize the relevant dimensions for system design for the first time. Finally, we offer useful guidance for system developers during the implementation of such applications. | ||||||||||
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| 文献发表年限 | 2019 | 创建时间 | 2019-12-02 | ||||||||
| 文献关键字 | CSCW; Healthcare | ||||||||||
| 摘要描述 | The proposed workshop will identify research questions that will enable the field to uncover the types of work, labor relations, and social impacts that should be considered when designing AIbased healthcare technology. The workshop aims to outline key challenges, guidelines, and future agendas for the field, and provide collaboration opportunities for CSCW researchers, social scientists, AI researchers, clinicians, and relevant stakeholders in healthcare, to share their perspectives and co-create sociotechnical approaches to tackle timely issues related to AI and automation in healthcare work. | ||||||||||
| 文献作者 | Hao Liu; Hui Xiong | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-11-29 | ||||||||
| 文献关键字 | Transportation recommendation; context-aware; personalized; feature engineering; deployment; POI 特征工程 | ||||||||||
| 摘要描述 | Transportation recommendation is one important map service in navigation applications. Previous transportation recommendation solutions fail to deliver satisfactory user experience because their recommendations only consider routes in one transportation mode (uni-modal, e.g., taxi, bus, cycle) and largely overlook situational context. In this work, we propose Hydra, a recommendation system that oers multi-modal transportation planning and is adaptive to various situational contexts (e.g., nearby point-of-interest (POI) distribution and weather). We leverage the availability of existing routing engines and big urban data, and design a novel two-level framework that integrates uni-modal and multi-modal (e.g., taxibus, bus-cycle) routes as well as heterogeneous urban data for intelligent multi-modal transportation recommendation. In addition to urban context features constructed from multi-source urban data, we learn the latent representations of users, origin-destination (OD) pairs and transportation modes based on user implicit feedbacks, which captures the collaborative transportation mode preferences of users and OD pairs. A gradient boosting tree based model is then introduced to recommend the proper route among various uni-modal and multi-modal transportation routes. We also optimize the framework to support real-time, large-scale route query and recommendation. We deploy Hydra on Baidu Maps, one of the world’s largest map services. Real-world urban-scale experiments demonstrate the eectiveness and eciency of our proposed system. Since its deployment in August 2018, Hydra has answered over a hundred million route recommendation queries made by over ten million distinct users with 82.8% relative improvement of user click ratio. | ||||||||||
| 文献作者 | Junchen Ye; Hui Xiong | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-11-28 | ||||||||
| 文献关键字 | Demand Prediction, Spatio-Temporal Analysis, Sharing Economy, Deep Neural Network | ||||||||||
| 摘要描述 | Taxi and sharing bike bring great convenience to urban transportation. A lot of efforts have been made to improve the efficiency of taxi service or bike sharing system by predicting the next-period pick-up or drop-off demand. Different from the existing research, this paper is motivated by the following two facts: 1) From a micro view, an observed spatial demand at any time slot could be decomposed as a combination of many hidden spatial demand bases; 2) From a macro view, the multiple transportation demands are strongly correlated with each other, both spatially and temporally. Definitely, the above two views have great potential to revolutionize the existing taxi or bike demand prediction methods. Along this line, this paper provides a novel Co-prediction method based on Spatio-Temporal neural Network, namely, CoST-Net. In particular, a deep convolutional neural network is constructed to decompose a spatial demand into a combination of hidden spatial demand bases. The combination weight vector is used as a representation of the decomposed spatial demand. Then, a heterogeneous Long Short-Term Memory (LSTM) is proposed to integrate the states of multiple transportation demands, and also model the dynamics of them mixedly. Last, the environmental features such as humidity and temperature are incorporated with the achieved overall hidden states to predict the multiple demands simultaneously. Experiments have been conducted on real-world taxi and sharing bike demand data, results demonstrate the superiority of the proposed method over both classical and the state-of-the-art transportation demand prediction methods. | ||||||||||
| 文献作者 | Jingbo Zhou; Hui Xiong | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-11-22 | ||||||||
| 文献关键字 | Tag refinement | ||||||||||
| 摘要描述 | Tags of a Point of Interest (POI) can facilitate location-based services from many aspects like location search and place recommendation. However, many POI tags are often incomplete or imprecise, which may lead to performance degradation of tag-dependent applications. In this paper, we study the POI tag refinement problem which aims to automatically fill in the missing tags as well as correct noisy tags for POIs. We propose a tri-adaptive collaborative learning framework to search for an optimal POI-tag score matrix. The framework integrates three components to collaboratively (i) model the similarity matching between POI and tag, (ii) recover the POI-tag pattern via matrix factorization and (iii) learn to infer the most possible tags by maximum likelihood estimation. We devise an adaptively joint training process to optimize the model and regularize each component simultaneously. And the final refinement results are the consensus of multiple views from different components. We also discuss how to utilize various data sources to construct features for tag refinement, including user profile data, query data on Baidu Maps and basic properties of POIs. Finally, we conduct extensive experiments to demonstrate the effectiveness of our framework. And we further present a case study of the deployment of our framework on Baidu Maps. | ||||||||||
| 文献作者 | Yang Yang; Hui Xiong | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-11-22 | ||||||||
| 文献关键字 | 增量学习； 在线学习； fisher information matrix；对之前学好的模型参数建模，从而保存之前的信息 | ||||||||||
| 摘要描述 | Recent years have witnessed growing interests in developing deep models for incremental learning. However, existing approaches often utilize the fixed structure and online backpropagation for deep model optimization, which is difficult to be implemented for incremental data scenarios. Indeed, for streaming data, there are two main challenges for building deep incremental models. First, there is a requirement to develop deep incremental models with Capacity Scalability. In other words, the entire training data are not available before learning the task. It is a challenge to make the deep model structure scaling with streaming data for flexible model evolution and faster convergence. Second, since the stream data distribution usually changes in nature (concept drift), there is a constraint for Capacity Sustainability. That is, how to update the model while preserving previous knowledge for overcoming the catastrophic forgetting. To this end, in this paper, we develop an incremental adaptive deep model (IADM) for dealing with the above two capacity challenges in real-world incremental data scenarios. Specifically, IADM provides an extra attention model for the hidden layers, which aims to learn deep models with adaptive depth from streaming data and enables capacity scalability. Also, we address capacity sustainability by exploiting the attention based fisher information matrix, which can prevent the forgetting in consequence. Finally, we conduct extensive experiments on real-world data and show that IADM outperforms the state-of-the-art methods with a substantial margin. Moreover, we show that IADM has better capacity scalability and sustainability in incremental learning scenarios. | ||||||||||
| 文献作者 | Qingxin Meng; Hui Xiong | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-11-21 | ||||||||
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| 摘要描述 | The understanding of job mobility can benefit talent management operations in a number of ways, such as talent recruitment, talent development, and talent retention. While there is extensive literature showing the predictability of the organization-level job mobility patterns (e.g., in terms of the employee turnover rate), there are no effective solutions for supporting the understanding of job mobility at an individual level. To this end, in this paper, we propose a hierarchical career-path-aware neural network for learning individual-level job mobility. Specifically, we aim at answering two questions related to individuals in their career paths: 1) who will be the next employer? 2) how long will the individual work in the new position? Specifically, our model exploits a hierarchical neural network structure with embedded attention mechanism for characterizing the internal and external job mobility. Also, it takes personal profile information into consideration in the learning process. Finally, the extensive results on real-world data show that the proposed model can lead to significant improvements in prediction accuracy for the two aforementioned prediction problems. Moreover, we show that the above two questions are well addressed by our model with a certain level of interpretability. For the case studies, we provide data-driven evidence showing interesting patterns associated with various factors (e.g., job duration, firm type, etc.) in the job mobility prediction process. | ||||||||||
| 文献作者 | Jiarui Qin; Kan Ren | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-11-15 | ||||||||
| 文献关键字 | Sequential Recommendation, Collaborative Filtering, Co-Attention; GRU | ||||||||||
| 摘要描述 | Sequential recommendation task aims to predict user preference over items in the future given user historical behaviors. The order of user behaviors implies that there are resourceful sequential patterns embedded in the behavior history which reveal the underlying dynamics of user interests. Various sequential recommendation methods are proposed to model the dynamic user behaviors. However, most of the models only consider the user’s own behaviors and dynamics, while ignoring the collaborative relations among users and items, i.e., similar tastes of users or analogous properties of items. Without modeling collaborative relations, those methods suffer from the lack of recommendation diversity and thus may have worse performance. Worse still, most existing methods only consider the user-side sequence and ignore the temporal dynamics on the item side. To tackle the problems of the current sequential recommendation models, we propose Sequential Collaborative Recommender (SCoRe) which effectively mines high-order collaborative information using cross-neighbor relation modeling and, additionally utilizes both user-side and item-side historical sequences to better capture user and item dynamics. Experiments on three real-world yet large-scale datasets demonstrate the superiority of the proposed model over strong baselines. | ||||||||||
| 文献作者 | Renjun Hu; Xinjiang Lu | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-11-14 | ||||||||
| 文献关键字 | scalar projection； 一个利用scalar projection做的attention方法 | ||||||||||
| 摘要描述 | While Point-of-Interest (POI) recommendation has been a popular topic of study for some time, little progress has been made for understanding why and how people make their decisions for the selection of POIs. To this end, in this paper, we propose a user decision profiling framework, named PROUD, which can identify the key factors in people’s decisions on choosing POIs. Specifically, we treat each user decision as a set of factors and provide a method for learning factor embeddings. A unique perspective of our approach is to seamlessly identify key factors, while preserving decision structures, by maximizing the sum of scalar projection of all related factor embeddings on the aggregated embedding of key factors. In addition, we show that this objective involves nonconvex quadratically constrained quadratic programming (QCQP), which remains NP-hard in general. To address this, our PROUD adopts a self projection attention and an L2 regularized sparse activation to directly estimate the likelihood of each factor to be a key factor. Finally, extensive experiments on real-world data validate the advantage of PROUD in preserving user decision structures. Also, our case study indicates that the identified key decision factors can help us to provide more interpretable recommendations and analysis. | ||||||||||
| 文献作者 | Zhengxiao Du | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-11-07 | ||||||||
| 文献关键字 | few-shot learning; meta learning; LSTM; early-stop policy; learning rate; | ||||||||||
| 摘要描述 | Cold-start problems are long-standing challenges for practical recommendations. Most existing recommendation algorithms rely on extensive observed data and are brittle to recommendation scenarios with few interactions. This paper addresses such problems using few-shot learning and meta learning. Our approach is based on the insight that having a good generalization from a few examples relies on both a generic model initialization and an effective strategy for adapting this model to newly arising tasks. To accomplish this, we combine the scenario-specific learning with a model-agnostic sequential meta-learning and unify them into an integrated end-toend framework, namely Scenario-specific Sequential Meta learner (or s2Meta ). By doing so, our meta-learner produces a generic initial model through aggregating contextual information from a variety of prediction tasks while effectively adapting to specific tasks by leveraging learning-to-learn knowledge. Extensive experiments on various real-world datasets demonstrate that our proposed model can achieve significant gains over the state-of-the-arts for cold-start problems in online recommendation1. Deployment is at the Guess You Like session, the front page of the Mobile Taobao; and the illustration video can also be watched from the link2. | ||||||||||
