Targeted advertising benefits consumers by delivering them only the messages that match their interests, and also helps advertisers by identifying only the consumers interested in their messages. Although targeting mechanisms for online advertising are well established, pervasive computing environments lack analogous approaches. This paper explores the application of activity inferencing to targeted advertising. We present two mechanisms that link activity descriptions with ad content: direct keyword matching using an online advertising service, and “human computation” matching, which enhances keyword matching with help from online workers. The direct keyword approach is easier to engineer and responds more quickly, whereas the human computation approach has the potential to target more effectively.