Personalizing Search on Shared Devices

Abstract

Search personalization tailors the search experience to individual searchers. To do this, search engines construct interest models comprising signals from observed behavior associated with ma-chines, often via Web browser cookies or other user identifiers. However, shared device usage is common, meaning that the activities of multiple searchers may be interwoven in the interest models generated. Recent research on activity attribution has led to methods to automatically disentangle the histories of multiple searchers and correctly ascribe newly-observed search activity to the correct per-son. Building on this, we introduce attribution-based personalization (ABP), a procedure that extends traditional personalization to target individual searchers on shared devices. Activity attribution may improve personalization, but its benefits are not yet fully understood. We present an oracle study (with perfect knowledge of which searchers perform each action on each machine) to under-stand the effectiveness of ABP in predicting searchers' future interests. We utilize a large Web search log dataset containing both per-son identifiers and machine identifiers to quantify the gain in personalization performance from ABP, identify the circumstances under which ABP is most effective, and develop a classifier to determine when to apply it that yields sizable gains in personalization performance. ABP allows search providers to personalize experiences for individuals rather than targeting all users of a device collectively.