Chapter 10: User Interfaces and Visualization
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Modern Information Retrieval Chapter 10: User Interfaces and Visualization |
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relevance feedback interfaces!monitoring user behavior relevance feedback interfaces!agents monitoring user behavior agents assistants personal assistants
Standard relevance feedback is predicated on the goal of improving an ad hoc query or building a profile for a routing query. More recently researchers have begun developing systems that monitor users' progress and behavior over long interaction periods in an attempt to predict which documents or actions the user is likely to want in future. These systems are called semi-automated assistants or recommender `agents,' and often make use of machine learning techniques [#!mitchell97!#]. Some of these systems require explicit user input in the form of a goal statement [#!joachims97!#] or relevance judgements [#!pazzani96!#], while others quietly record users' actions and try to make inferences based on these actions.
A system developed by Kozierok and Maes [#!kozierok93!#,#!maes93!#] makes predictions about how users will handle email messages (what order to read them in, where to file them) and how users will schedule meetings in a calendar manager application. The system `looks over the shoulder' of the users, recording every relevant action into a database. After enough data has been accumulated, the system uses a nearest-neighbors method [#!stanfill86b!#] to predict a user's action based on the similarity of the current situation to situations already encountered. For example, if the user almost always saves email messages from a particular person into a particular file, the system can offer to automate this action the next time a message from that person arrives [#!maes93!#]. This system integrates learning from both implicit and explicit user feedback. If a user ignores the system's suggestion, the system treats this as negative feedback, and accordingly adds the overriding action to the action database. After certain types of incorrect predictions, the system asks the user questions that allow it to adjust the weight of the featurethat caused the error. Finally, the user can explicitly train the system by presenting it with hypothetical examples of input-action pairs.
relevance feedback interfaces!Syskill and Webert Syskill and Webert
Another system, Syskill and Webert [#!pazzani96!#], attempts to learn a user profile based on explicit relevance judgements of pages explored while browsing the Web. In a sense this is akin to standard relevance feedback, except the user judgements are retained across sessions and the interaction model differs: as the user browses a new Web page, the links on the page are automatically annotated as to whether or not they should be relevant to the user's interest.
relevance feedback interfaces!Letizia Letizia
A related system is Letizia [#!lieberman95!#], whose goal is to bring to the user's attention a percentage of the available next moves that are most likely to be of interest, given the user's earlier actions. Upon request, Letizia provides recommendations for further action on the user's part, usually in the form of suggestions of links to follow when the user is unsure what to do next. The system monitors the user's behavior while navigating and reading Web pages, and concurrently evaluates the links reachable from the current page. The system uses only implicit feedback. Thus, saving a page as a bookmark is taken as strong positive evidence for the terms in the corresponding Web page. Links skipped are taken as negative support for the information reachable from the link. Selected links can indicate positive or negative evidence, depending on how much time the user spends on the resulting page and whether or not the decision to leave a page quickly is later reversed. Additionally, the evidence for user interest remains persistent across browsing sessions. Thus, a user who often reads kayaking pages is at another time reading the home page of a professional contact and may be alerted to the fact that the colleague's personal interests page contains a link to a shared hobby. The system uses a best-first search strategy and heuristics to determine which pages to recommend most strongly.
relevance feedback interfaces!Butterfly Butterfly system
A more user-directed approach to prefetching potentially relevant information is seen in the Butterfly system [#!mackinlay95!#]. This interface helps the user follow a series of citation links from a given reference, an important information seeking strategy [#!bates90b!#]. The system automatically examines the document the user is currently reading and prefetches the bibliographic citations it refers to. It also retrieves lists of articles that cite the focus document. The underlying assumption is that the services from which the citations are requested do not respond immediately. Rather than making the user wait during the delay associated with each request, the system handles many requests in parallel and the interface uses graphics and animations to show the incrementally growing list of available citations. The system does not try to be clever about which cites to bring first; rather the user can watch the `organically' growing visualization of the document and its citations, and based on what looks relevant, direct the system as to which parts of the citation space to spend more time on.