Digital Libraries:

Digital Libraries:
Information Broker Roles in Collaborative Filtering

Annika Waern, Mark Tierney, Åsa Rudström,
Jarmo Laaksolahti and Torben Mård

Swedish Institute of Computer Science,
Box 1263, SE-164 29 Kista, Sweden

{annika, mark, asa, jarmo, torben}@sics.se

The main goal of the EdInfo project [1] is to utilize human information brokers, or editors, as a resource in adaptive information systems. An information broker can be any of the following:

The dedicated expert that collects and potentially reviews literature within a restricted area of interest;
The journalist that produces articles with specific reader groups in mind;
The librarian that organizes incoming information and directs readers to various sources;
The professional information broker, that processes specific information requests, seeks for appropriate information sources, and produces summaries of the obtained information.

The common characteristic of these roles is that the information broker has some kind of understanding of what his or her customers want, and is willing to adapt to these needs. Information brokers collect information from various sources, evaluate its relative importance and then choose whether to include the information as it is, disregard it, summarize it, or perhaps rewrite or illustrate it differently than in the original source.

Many existing information services build upon user profiles, e.g. news services such as CNN Custom News. Users are allowed to explicitly set up their profiles by selecting a set of categories and subcategories that fit their interests. However useful, this approach gives rise to a number of problems. Firstly, the available categories might not fit the users' real interests and preferences. Secondly, the categorization may have to change if a new need occurs, or a new type of information is added, but then all users must change their profiles to adhere to the new categorization. Finally, it is likely that users seldom change their profiles once they are set up, so it is not certain that the profile really reflects the user's true interests.

Individual user adaptation provides a way to deal with these problems, since it can provide user-defined categorizations that are automatically or semi-automatically maintained by learning from the user's actions with the system. But in order to introduce individual user adaptation, we must impose at least two additional tasks on information brokers:

to maintain the rules for stereotypic adaptation, used in user profiles;
to structure the information in a way that allows for these rules to be applicable.

The essential source of information necessary for these tasks is feedback from users, both in terms of which profiles they set up, and how they use the information they obtain. Since this information in itself is of imminent value for information brokers, we believe that they will accept the addition of these tasks. Nevertheless, a definite requirement is that the tasks are made as simple as possible; we cannot assume that information brokers have any particular interest in the details of the algorithms for user modeling.

ConCall is an agent-based system that implements the EdInfo ideas. The system supports the collection, filtering and browsing of conference and workshop calls, but could just as well be used for calls for participation in seminars, courses, etc. Using ConCall, the user (an individual researcher) can review calls and set up reminders for deadlines. To avoid uninteresting calls, the user sets up a filter to retrieve a personal selection of calls and organize them in a personal manner. This filter is maintained by semi-automatic means. The service is accessed over the web. Reminders are received by email or over the GSM network. The first version of the ConCall service has been implemented and is currently under experimental evaluation.

In ConCall, human editors take part in the filtering and classification process of domain data. To achieve this, the system uses "buzzwords" for information filtering and structuring. "Buzzwords" are used to annotate individual pieces of information, similar to keywords except they are not bound to any formal or informal ontology. A "buzzword" is just as likely to surface into the system originating from a user as from the editor. The editor uses the "buzzwords" to annotate calls, which are then filtered according to the preferences of the users. Users may also set up their own set of filtering rules using their own "buzzwords" for a second level of filtering or ordering. These user-defined words are provided as feedback to the editor who can react to trends or new topics of interest by incorporating the new words. The purpose of using "buzzwords" instead of keywords is to allow for a more flexible and self-adjusting body of classification words.

Acknowledgement

The EdInfo project is funded by the Swedish research institute for information technology (SITI AB) and the Swedish board for technical development (NUTEK).

References

1. Höök, K, Rudström, Å., and Waern, A. (1997) Edited Adaptive Hypermedia: Combining Human and Machine Intelligence to Achieve Filtered Information. In Milosavljevic, Brusilovsky, Moore, Oberlander and Stock (Eds.), proceedings of the Flexible Hypertext Workshop. Macquarie Computing Report No. C/TR97-06, Macquarie University, Australia. Available at here.