Predicting fine-grained interests of users with temporal behavior is important to personalization and information filtering applications. However, existing interest prediction methods are incapable of capturing the subtle degreed user interests towards particular items, and the internal time-varying drifting attention of individuals is not studied yet. Moreover, the prediction process can also ...

With the popularity of location-based social networks such as Weibo and Twitter, there are many records points interest (POIs) showing when where people have visited certain locations. From these records, next POI recommendation suggests that a target user might want to visit based on their check-in history current spatio-temporal context. Current methods mainly apply different deep learning mo...

Abstract Understanding human mobility in urban areas is important for transportation, from planning to operations and online control. This paper proposes the concept of user-station attention, which describes user’s (or user group’s) interest or dependency on specific stations. The contributes a better understanding (e.g., travel purposes) facilitates downstream applications, such as individual...

User involvement in information system development has long received research attention due to its significant effects on information system success. Prior studies have tended to focus on the consequences of user involvement, and in contrast, this research focuses on the factors that influence user involvement. The factors deserving more attention are organizational identification (OI) and orga...

We present a novel approach for proactive support of user in knowledge intensive organisations. Whilst once information was a scarce resource, nowadays all kinds and qualities of information are available. However human attention has become a scarce resource which is difficult to manage and support. Our attention management system proactively supports the user in dealing with processes, activit...

Time series forecasting uses data from the past periods of time to predict future information, which is great significance in many applications. Existing methods still have problems such as low accuracy when dealing with some non-stationary multivariate forecasting. Aiming at shortcomings existing methods, this paper we propose a new model LSTM-attention-LSTM. The two LSTM models encoder and de...

In this paper, we describe research on using eye-tracking data for on-line assessment of user meta-cognitive behavior during interaction with an environment for exploration-based learning. This work contributes to user modeling and intelligent interfaces research by extending existing research on eyetracking in HCI to on-line capturing of high-level user mental states for real-time interaction ...