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 develo... ...
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 ... || CSCW; Healthcare...
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 l... || Transportation recommendation; context-aware; personalized; feature engineering; deployment; POI 特征工程; || Hao Liu; Hui Xiong...
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 follo... || Demand Prediction, Spatio-Temporal Analysis, Sharing Economy, Deep Neural Network; || Junchen Ye; Hui Xiong...
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 ; || Jingbo Zhou; Hui Xiong...
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 st... || 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; || Yang Yang; Hui Xiong...
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 ... ; || Qingxin Meng; Hui Xiong...
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, Collaborative Filtering, Co-Attention; GRU; || Jiarui Qin; Kan Ren...
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, wh... || scalar projection； 一个利用scalar projection做的attention方法; || Renjun Hu; Xinjiang Lu...
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... || few-shot learning; meta learning; LSTM; early-stop policy; learning rate;; || Zhengxiao Du...
