Adaptive Query Auto Completion
Monday, August 31, 2015
3:30 pm - 4:30 pm
Gross Hall 330
Amit Goyal, Yahoo Labs Web Mining and Search group
12noon: Perspectives in Machine Learning/lunch for postdocs and grad students 3:30pm Seminar/reception Abstract: One of the most important features in a search engine is query auto-completion (QAC). QAC is the first service through which users interact with a search engine to input their search intent. In 2014, global users of Yahoo search saved more than 50% of keystrokes when submitting English queries by selecting QAC suggestions. QAC provides and updates a query suggestion list based on each new character typed by a user in the search box. The suggestion list is ranked by considering different factors, such as most popular completion (historical frequency counts from query logs), time (breaking news or recent popular queries), location, context (user¿s previous queries), personalization (user¿s profile), and click modeling (user¿s past click behaviors). The aforementioned approaches use only certain relevance features and do not fully take advantage of users¿ preferences such as user-QAC interactions. Suppose a user dwells on a suggestion list for a long time without selecting the top-ranked query, it indicates that the user intent might not be satisfied by the provided query suggestions. That wealth of implicit negative feedback has not yet been fully exploited for designing QAC models. Our findings suggest more accurate search results when redesigning QAC to include a more general ¿(static) relevance-(adaptive) implicit negative feedback¿ framework.