BA-GNN: Behavior-aware graph neural network for session-based recommendation
Session-based recommendation is a popular research topic that aims to predict users' next
possible interactive item by exploiting anonymous sessions. The existing studies mainly …
possible interactive item by exploiting anonymous sessions. The existing studies mainly …
Personalized context-aware collaborative online activity prediction
With the rapid development of Internet services and mobile devices, nowadays, users can
connect to online services anytime and anywhere. Naturally, user's online activity behavior …
connect to online services anytime and anywhere. Naturally, user's online activity behavior …
Graph neural network and multi-view learning based mobile application recommendation in heterogeneous graphs
F Xie, Z Cao, Y Xu, L Chen… - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
With the popularity of smartphones, mobile applications (mobile apps) have become a
necessity in people's lives and work. Massive apps provide users with a variety of choices …
necessity in people's lives and work. Massive apps provide users with a variety of choices …
Calendar graph neural networks for modeling time structures in spatiotemporal user behaviors
User behavior modeling is important for industrial applications such as demographic
attribute prediction, content recommendation, and target advertising. Existing methods …
attribute prediction, content recommendation, and target advertising. Existing methods …
General-purpose user embeddings based on mobile app usage
In this paper, we report our recent practice at Tencent for user modeling based on mobile
app usage. User behaviors on mobile app usage, including retention, installation, and …
app usage. User behaviors on mobile app usage, including retention, installation, and …
Spatiotemporal-enhanced network for click-through rate prediction in location-based services
In Location-Based Services (LBS), user behavior naturally has a strong dependence on the
spatiotemporal information, ie, in different geographical locations and at different times, user …
spatiotemporal information, ie, in different geographical locations and at different times, user …
Contextual counters and multimodal Deep Learning for activity-level traffic classification of mobile communication apps during COVID-19 pandemic
The COVID-19 pandemic has reshaped Internet traffic due to the huge modifications
imposed to lifestyle of people resorting more and more to collaboration and communication …
imposed to lifestyle of people resorting more and more to collaboration and communication …
MAPLE: Mobile App Prediction Leveraging Large Language Model Embeddings
In recent years, predicting mobile app usage has become increasingly important for areas
like app recommendation, user behaviour analysis, and mobile resource management …
like app recommendation, user behaviour analysis, and mobile resource management …
CaSe4SR: Using category sequence graph to augment session-based recommendation
L Liu, L Wang, T Lian - Knowledge-Based Systems, 2021 - Elsevier
Session-based recommendation aims to predict next item based on users' anonymous
behavior sequence within a short time. Recent studies focus on modeling sequential …
behavior sequence within a short time. Recent studies focus on modeling sequential …
Characterizing and predicting individual traffic usage of mobile application in cellular network
The proliferation of smart devices prompts the explosive usage of mobile applications, which
increases network traffic load. Characterizing the application level traffic patterns from an …
increases network traffic load. Characterizing the application level traffic patterns from an …