  The pursuit of improved accuracy in recommender systems has led to the
incorporation of user context. Context-aware recommender systems typically
handle large amounts of data which must be uploaded and stored on the cloud,
putting the user's personal information at risk. While there have been previous
studies on privacy-sensitive and context-aware recommender systems, there has
not been a full-fledged system deployed in an isolated mobile environment. We
propose a secure and efficient on-device mechanism to predict a user's next
intention. The knowledge of the user's real-time intention can help recommender
systems to provide more relevant recommendations at the right moment. Our
proposed algorithm is both context and sequence aware. We embed user intentions
as weighted nodes in an n-dimensional vector space where each dimension
represents a specific user context factor. Through a neighborhood searching
method followed by a sequence matching algorithm, we search for the most
relevant node to make the prediction. An evaluation of our methodology was done
on a diverse real-world dataset where it was able to address practical
scenarios like behavior drifts and sequential patterns efficiently and
robustly. Our system also outperformed most of the state-of-the-art methods
when evaluated for a similar problem domain on standard datasets.
