As smartphones have become indispensable personal devices, the number of smartphone
users has increased dramatically over the last decade. These personal devices, which are …

Recommender systems are one of the most successful applications of data mining and
machine-learning technology in practice. Academic research in the field is historically often …

Recommender systems help users find relevant items of interest, for example on e-
commerce or media streaming sites. Most academic research is concerned with approaches …

Being more powerful and intrusive into user-device interactions, LLMs are eager for on-
device execution to better preserve user privacy. In this work, we propose a new paradigm of …

Smartphone usage is a hot topic in pervasive computing due to their popularity and personal
aspect. We present our initial results from analyzing how individual differences, such as …

The increasing availability of high–dimensional, fine–grained data about human behaviour,
gathered from mobile sensing studies and in the form of digital footprints, is poised to …

To ensure user satisfaction and success of mobile applications, it is important to provide
highly performant applications. This is particularly important for resource-constrained …

Smartphones are considered as one of the most essential and highly personal devices of
individuals in our current world. Due to the popularity of context-aware technology and …

The capability of extracting sequential patterns from the user-item interaction data is now
becoming a key feature of recommender systems. Though it is important to capture the …

We describe the Customer LifeTime Value (CLTV) prediction system deployed at ASOS.
com, a global online fashion retailer. CLTV prediction is an important problem in e …