  Personal digital traces are constantly produced by connected devices,
internet services and interactions. These digital traces are typically small,
heterogeneous and stored in various locations in the cloud or on local devices,
making it a challenge for users to interact with and search their own data. By
adopting a multidimensional data model based on the six natural questions --
what, when, where, who, why and how -- to represent and unify heterogeneous
personal digital traces, we can propose a learning-to-rank approach using the
state of the art LambdaMART algorithm and frequency-based features that
leverage the correlation between content (what), users (who), time (when),
location (where) and data source (how) to improve the accuracy of search
results. Due to the lack of publicly available personal training data, a
combination of known-item query generation techniques and an unsupervised
ranking model (field-based BM25) is used to build our own training sets.
Experiments performed over a publicly available email collection and a personal
digital data trace collection from a real user show that the frequency-based
learning approach improves search accuracy when compared with traditional
search tools.
