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Mobile search is becoming increasingly important for mobile users as mobile devices are more widely used. Mobile search is quite different from standard PC-based web search in a number of ways: (a) the user interfaces and I/O are limited by screen real estate, (b) key pads are tiny and inconvenient for use, (c) limited bandwidth and (d) costly connection fees. These limitations result in more navigational queries in the mobile search. Furthermore, user location, activities, preferences, and interaction history can also improve accuracy in determining relevance for mobile search. In the past, most personalized search algorithms are studied in the context of PC-based web search. Personalized mobile search should however play a bigger role at improving the user experiences. This paper focuses on the personalization strategies which explicitly and implicitly infer user search context at individual user level. We propose an architecture which collects user information (at mobile device and carrier network) and derives user intention in given situations. We show that personalized mobile search perform well for ambiguous queries and localized searches.