Retroactive Answering of Search Queries
Major search engines currently use the history of a user's actions (e.g., queries, clicks) to personalize search results. In this paper, we present a new personalized service, query-specific web recommendations (QSRs), that retroactively answers queries from a user's history as new results arise. The QSR system addresses two important subproblems with applications beyond the system itself: (1) Automatic identification of queries in a user's history that represent standing interests and unfulfilled needs. (2) Effective detection of interesting new results to these queries. We develop a variety of heuristics and algorithms to address these problems, and evaluate them through a study of Google history users. Our results strongly motivate the need for automatic detection of standing interests from a user's history, and identifies the algorithms that are most useful in doing so. Our results also identify the algorithms, some which are counter-intuitive, that are most useful in identifying interesting new results for past queries, allowing us to achieve very high precision over our data set.
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