A Frequency-Based Learning-To-Rank Approach for Personal Digital Traces

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

A Tool for Personal Data Extraction

Digital storage now acts as an archive of the memories of users worldwide, keeping record of data as well as the context in which the data was acquired. The massive amount of data available and the fact that it is fragmented across many services (e.g., Facebook) and devices (e.g., laptop) make it very difficult for users to find specific pieces of information that they remember having stored or accessed. Unifying this fragmented data into a single data set that includes contextual information would allow for much better indexing and searching of personal information. Thus, we have developed a personal data extraction tool as a first step toward this vision. In this paper, we present this extraction tool, along with some preliminary statistics about personal data gathered by the tool for several users. The goal of the data analysis is to give a glimpse of what the digital life of a person may look like, and how it is currently partitioned across many different services; moreover, it reinforces the fact that it is not possible for users to manually retrieve, store and access their extensive digital data without the support of a personalized information management tool. 1