An Analysis of Cross-Device Search *

Background. Ownership and use of multiple devices such as desktop computers, smartphones, and tablets is increasing rapidly. Search is popular and people often perform search tasks that span device boundaries. Understanding how these devices are used and how people transition between them during information seeking is essential in developing search support for a multi-device world. Aim. The aim of this study is to characterize multi-device search behavior in a search log dataset and to demonstrate that sufficiently non-random behavior patterns emerge for search across multiple devices. We further show that such patterns can be reliably exploited to predict device-to-device transitions by machine learning methods trained on historical search log data. Data. We analyze a dataset of user search log records drawn from a popular commercial search engine, gathered by the authors during 2013 from the internal database of that search engine. While the dataset is proprietary and agreements prevent us from identifying either the particular search engine or the time period spanned by the data, we can give specifics concerning the form and use of the data. The data consists of roughly two billion English language queries submitted by approximately thirty-three million users in the United States. The data includes query terms, user identifiers, device information, location data, and time of query. In addition, rows have been annotated with topical and historical user information, generated using trained topic classifiers and constructed from simple historical statistics. Three sub-datasets were constructed from the main dataset, one for each of the three learning tasks completed in this study. Methods. We characterize multi-device search across four device types, including aspects of search behavior on each device (e.g., topics of interest) and characteristics of device transitions. Building on the characterization, we learn models to predict various aspects of cross-device search using existing machine learning methods. Three tasks were completed, which were: (1) predicting the next device used for search, (2) predicting if a device switch occurs, and (3) predicting the next device given a device switch occurs. Results. Our analysis shows that people use different devices to search for different content, and time of day interacts with device to affect content sought. We found that exploitable patterns emerge for device transitions, with pre∗An abridged version of this paper was presented at the 23rd International Conference on Information and Knowledge Management (CIKM 2014) under the title “CrossDevice Search. ” vious device signaling the next device, even when they differ. For two of the prediction tasks (i.e., predict next device and predict next device given switch), predictive accuracy, recall and precision exceeded 90% with a fairly compact feature set. The remaining task (predict device switch) also saw significant gains in accuracy over the baselines, approaching 80% given all features. Conclusions. Our results give evidence that learnable patterns exist in user device transition behavior. Being able to accurately predict device transition behavior enables many applications. For example, accurately forecasting the device used for the next query lets search engines proactively retrieve device-appropriate content (e.g., short documents for smartphones) for likely topics of interest (which differ by device), while knowledge of the current device combined with device-specific topical interest models may assist in better query-sense disambiguation. The former becomes especially important when combined with search-task continuation prediction systems, which can predict whether a user is likely to continue searching for a specific topic within the near future.