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Revisiting Online Personal Search Metrics with the User in Mind

Azin Ashkan
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '19) (2019)


Traditional online quality metrics are based on search and browsing signals, such as position and time of the click. Such metrics typically model all users' behavior in exactly the same manner. Modeling individuals' behavior in Web search may be challenging as the user's historical behavior may not always be available (e.g., if the user is not signed into a given service). However, in personal search, individual users issue queries over their personal corpus (e.g. emails, files, etc.) while they are logged into the service. This brings an opportunity to calibrate online quality metrics with respect to an individual's search habits. With this goal in mind, the current paper focuses on a user-centric evaluation framework for personal search by taking into account variability of search and browsing behavior across individuals. The main idea is to calibrate each interaction of a user with respect to their historical behavior and search habits. To formalize this, a characterization of online metrics is proposed according to the relevance signal of interest and how the signal contributes to the computation of the gain in a metric. The proposed framework introduces a variant of online metrics called pMetrics (short for personalized metrics) that are based on the average search habits of users for the relevance signal of interest. Through extensive online experiments on a large population of Gmail search users, we show that pMetrics are effective in terms of their sensitivity, robustness, and stability compared to their standard variants as well as baselines with different normalization factors.