This is where context-aware services come in. Recall that user context is the mechanism that ties community and services together and integrates them. When traveling around the community, each user carries his or her context around like a calling card that contains not only the user’s basic account details but also relevant details about the user’s purpose in accessing the site—the five Ws and one H. For example, suppose a user views a list of the most popular tags attached to video blogs in a specific community on the SNS—a popular feature that provides a snapshot of what is happening in the community, in this case the student body of a university.
In this view, the service lumps all video content together, including content created and offered by the SNS itself (first-party content), video blog entries produced by users (second-party content), and embedded video clips provided through a consumer-oriented service offered by a TV network (third-party content).
When the user clicks a tag, the SNS software records his choice along with elements of his user context. For first- and second-party content, the site presumably makes use of this contextual information to aggregate user likes and dislikes in order to improve the community. If the user requests third-party content, these user context elements are transmitted to the service provider along with the content request. In “five Ws and one H” format, the transmitted user context includes the following:
· Who: The user’s account lists him as a 30-year-old male.
· Where: The IP address indicates that the user is staying at a particular hotel in Tokyo, Japan.
· When: The request is made at 9:00 pm, while the user is engaged in an online conversation with friends.
· What: From the list of popular tags, the user chose “Michiko Osada” (the name of an actress).
· How: The user is running the SNS smart client application on a desktop or laptop PC.
· Why: By tracing the user’s actions, the SNS software determines that the user is interested in previewing different television programs.
From analyzing thousands of requests made by users of this SNS and others, the TV network and other providers can detect patterns of usage and determine that users with similar user contexts often have similar tastes and interests. When the TV network service transmits the clip the user has requested, therefore, it also transmits a list of other video clips the user may find interesting, based on choices made by similar users in the past.
(In this case, users in the student community have made thousands of past requests for this particular clip, enough to make it the top recommendation for this user in this context. For users in other communities with different user contexts, the recommendation could be different. For example, in a community devoted to fashion design, the top recommendation might involve a signature piece of jewelry the actress often wears.)
The SNS smart client application shows these recommendations in a list appearing beneath the preview area at the lower right of the screen, as seen above. By scrolling through the list of recommendations, the user can access these additional clips or other content offered by other service providers without leaving the SNS environment.
By clicking a preview, the user begins watching the program, which appears in the main area of the screen. The content includes metadata that allows members of the community to share opinions on it using tagging, recommendations, and commenting. In the illustration below, a comment window appears at the bottom of the screen so users can share their opinions on the clip while watching it.Although the notion of sharing comments among users is already implemented in some services, this service offers an additional feature which enables you to specify the boundary of your comment sharing. That said, you can share your opinion only with your friends if you wish . You might argue it's a subtle difference but it produces a tremendous advantage in the user experience.
This hypothetical SNS software allows users to watch video content either within the application, or on a television equipped with compatible media center software. By choosing to view the full program on TV, the user transmits his user context to the service provider again. This time, it’s slightly different:
· Who: 30-year-old male.
· Where: A hotel room.
· When: At 9:10 pm, about 10 minutes after requesting the preview clip.
· What: The same tag (“Michiko Osada”) that led the user to the preview in the first place.
· How: Via the media center application on a compatible TV device.
· Why: The user has viewed the preview clip and now wishes to watch the entire program.
While watching the program on the hotel’s TV system with broadcast-quality picture and sound, the user can use his computer to supplement the experience with the SNS. For example, the program the user is watching is about Kazutoyo, a famous samurai in Japanese history. The name “Kazutoyo” appears on the SNS as a tag associated with the program. When the user clicks the “Kazutoyo” tag, the application makes search requests to several different services, transmitting the following user context:
· Who: 30-year-old male.
· Where: A hotel room.
· When: At 9:30 pm, while watching television.
· What: The tag “Kazutoyo.”
· How: Using the SNS application on a PC.
· Why: The user has encountered the tag on the SNS and wishes to browse any information associated with it.
Different services would return different responses, such as Kazutoyo-related merchandise or information about Kazutoyo’s birthplace and options for traveling there. By observing and analyzing what the user does with this information, the SNS can trace the chain of the user’s curiosity, which it can use to refine its future estimations of user contexts.
User context is the key to bridging the gap between social communities and commercial services in order to create a true digital lifestyle. By offering services that can accept and understand user context metadata, providers can help denizens of online communities enrich their experience with content and features tailored to specific interests. In turn, users and communities can take advantage of these services to improve the communities themselves.
