Search engines, portals and topic-centered web sites are all 

attempts to create more or less personalized web-services. 

However, no single service can in general fulfill all needs 

of a particular user, so users have to search and maintain 

personal profiles at several locations. We propose an architecture where each person has his own information 

management environment where all personalization is 

made locally. Information is exchanged with other’s if it’s 

of mutual interest that the information is published or received. We assume that users are self-interested, but that 

there is some overlap in their interests. 

Our recent work has focused on decentralized dissemination of information, specifically what we call decentralized recommender systems. We are investigating the behavior of such systems and have also done some preliminary work on the users’ information environment

Janson, Sverker

Olsson, Tomas

Rasmusson, Andreas

English

Swedish Institute of Computer Science Publications Database

Personalized Decentralized Communication
Tomas Olsson, Andreas Rasmusson and Sverker Janson
Swedish Institute of Computer Science
Box 1263
SE-164 29 Kista, Sweden
{tol, ara, sverker}@sics.se
Introduction
Search engines, portals and topic-centered web sites are all
attempts to create more or less personalized web-services.
However, no single service can in general fulfill all needs
of a particular user, so users have to search and maintain
personal profiles at several locations. We propose an ar-
chitecture where each person has his own information
management environment where all personalization is
made locally. Information is exchanged with other’s if it’s
of mutual interest that the information is published or re-
ceived. We assume that users are self-interested, but that
there is some overlap in their interests.
Our recent work has focused on decentralized dissemi-
nation of information, specifically what we call decentral-
ized recommender systems. We are investigating the be-
havior of such systems and have also done some prelimi-
nary work on the users’ information environment.
The Virtual Community Library
People working with information read, write and sort
“documents” in a way that reflects their specific tasks and
needs (c.f. the folder hierarchy on a personal computer).
From the work put down by the users in organizing their
own documents, a user’s personal agent can construct a
user profile. This profile can be used to personalize inter-
actions with other information sources, e.g. personalized
ranking of search-engine results, personalized document
recommendations, “intelligent” help in maintaining the
document collection, person-
alized views of external infor-
mation sources, subscriptions to
and change notifications for
external resources.
The restriction that we only
make use of the information the
user has put in her personal
library is central to the archi-
tecture. A problem in most sys-
tems relying on sharing informa-
tion is that they depend on users
doing actions that are not di-
rectly beneficent to themselves
(e.g. rating a read news-article
only saves other people’s time).
Accessing or maintaining the
personal library is assumed to
have to be done by the user
anyway. Since most of the in-
formation representing a user-
stays with the user (except in-
formation that can be inferred
from the agent’s communicative
acts), all profile information is
kept and updated at one place
and less private information is
disclosed.
We call an individually con-
trolled information collection a
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Figure 1: A Virtual Community Library where the light grey nodes are the users’ personal library
agents or organisations’ library agents (thick lines). Dark grey nodes are web-sites and web-services
while the unfilled circles are personal recommendation agents. The arrows indicate information
exchange.
100 200 300 400 500
5
10
15
20
25
30
35
Figure 2: The mean recall for the users (upper line) and the received mean share of all documents
for the recommender agents (lower line) (both in percent per time step) with ±1.0 standard deviation.
“Personal Library”. It is the fundamental building block in
our architecture and every idiosyncratic categorization of
documents (e.g. the documents on a web-server) is a Per-
sonal Library.
A Virtual Community Library (VCL) is modeled as a
multiagent system consisting of Personal Library agents
representing users and organizations (Rasmusson, Olsson
and Hansen 1998), see figure 1.  Other agents can be asso-
ciated to the personal library, e.g. recommender agents that
help the user finding relevant information. The virtual
community is not static; users will enter, leave and change
interests over time. The VCL requires means to publish
and get access to information in the personal libraries. How
useful the VCL will be to the users depends crucially on
the degree to which the users can benefit from the work put
down by the other users in maintaining their personal lib-
raries.
In this architecture, no agent is necessarily considered
more complete or authoritative. Still, large (institutional
and organizational) libraries will most likely be quite influ-
ential (by virtue of them being used a lot) in how docu-
ments are categorized and described.
Prototype Personal Library
The Personal Library should be integrated into the user’s
everyday document management environment.  Although
not completely unavoidable, it is preferable to minimize
the constraints on what tools the users can use to manage
their information. We have tested the applicability of inter-
cepting an application level protocol (IMAP,
www.imap.org) to transparently extract information about
user behavior. We implemented a prototype personal libra-
ry agent monitoring the user’s
email management (Rasmusson
1999). Email is often sorted
according the user’s personal
view of what is relevant. Also,
actions such as replying to and
forwarding mails indicate social
relationships with other people.
Using this information it
should be possible to construct
services based on the work-
context (e.g. sort the results of a
query based on the contents of
the currently active folder) and
also to create views of external
services, e.g. by showing them
as virtual mailboxes in the user’s
folder hierarchy. Yet, only some
smaller proof of concept serv-
ices have been implemented,
e.g. an application where users
vote on the relevance of a docu-
ment to a set of known people.
Remembrance Agent (Rhodes
1996) and MailCat (Segal and
Kephart 1999) are two related systems that unobtrusively
monitor the user’s actions to suggest potentially relevant
information sources (Remembrance Agent) and to suggest
on mailboxes to sort emails into (MailCat).  Remembrance
Agent is currently implemented as an emacs-mode and
hence depends on emacs. In a similar way the personal
library agent implemented here depends on the use of
IMAP. MailCat is built into Lotus Notes’ mail reader and
can hence access the GUI whereas the personal library
agent is not tied to any particular mail-reader, but is more
restricted in how the user interaction can be implemented.
The Decentralized Recommender System
Each personal library agent may be associated with a re-
commender agent whose task is to automate the dissemi-
nation of information among the agents. A recommender
agent proposes potentially relevant documents to its user
based on a profile of the user’s interests. The recommender
system we study mimics the word-of-mouth method for
spreading information and is based on Collaborative Fil-
tering (Shardanand and Maes 1995).  The recommender
agents send information to each other and, in the process,
learn which others to trust for relevant information (Olsson
1998). When the user publishes a document, the agent will
forward the message to those of the agents that it believes
are most interested in the document. This, in turn will give
the recipient agents information about the sender’s inter-
ests, allowing them to send relevant documents to that
agent in the future. Since the agents prefer communicating
with likeminded agents, clusters of likeminded agents will
form, hence shortening the path a document must travel to
reach the interested recipients.
Yenta (Foner 1997) and ACORN (Marsh and Masrour
1997) are two related agent based systems.  In Yenta the
agents compare their user profiles with each other in order
to find other users with similar interests. Yenta assumes
that it is non-problematic to directly compare user profiles.
In ACORN, the documents or search queries are the
“smart” entities that try to locate interested users or
matching documents, respectively. In our system, the
agents can have non-comparable user profiles, because
they share their profiles by sending document references
and thereby get documents as well.
Preliminary simulation results
To analyze the behavior of the recommendation network
we have implemented a simulation of a population of users
with different interests. We ran 100 simulations with 100
users, each having 3 out of 30 interests.
We are currently analyzing the results. The initial results
indicate that we get the expected clustering effect. In figure
2, it can be see that the mean recall1 for the users is higher
than the received mean share2 for the recommender agents.
If the documents were randomly sent to the agents the
mean recall should be similar to the received mean share,
but the clustering seems to make it non-random.
Research Issues
The aim with this research is do find guidelines for how to
design this kind of decentralized systems and to increase
the understanding of what trade-offs and assumptions that
must be made.
We have not yet addressed the issues of how changing
interests and differing quality of information may affect the
resulting communication patterns.  Under what conditions
will subgroups dissolve or form as response to changing
interests in the community? Will a decrease in the quality
of information disperse previously functioning communi-
ties, similarly to the way newsgroups tend to be abandoned
when the traffic increases too much?
Insights from these simulations may be used to estimate
the potential gains and understand the impact of changes in
the recommender system algorithms, e.g. in the group-
formation strategy or in different reward mechanisms.
Acknowledgements
This work was funded by FIOL (www.fiol.siti.se).
                                               
1
 The recall is the number of received relevant documents divided by the
total number of relevant documents in the system for a user.
2
 The received mean share is the total number of received documents for a
recommender agent divided by the total number of documents in the
system.
References
Foner, L. N. 1997. Yenta: A Multi-Agent, Referral-Based
Matchmaking System. In Proceedings of The First Inter-
national Conference on Autonomous Agents, 301-307.
ACM Press.
Marsh, S. and Masrour, Y. 1997. Agent Augmented Com-
munity Information — The ACORN Architecture. In Pro-
ceedings CASCON’97, Meeting of Minds.
Olsson, T. 1998. Decentralized Social Filtering based on
Trust. In Recommender Systems Workshop Papers. Tech-
nical Report WS-98-08, 84-88. AAAI Press.
Rasmusson, A. and Olsson, T and Hansen P. 1998. A Vir-
tual Community Library: SICS Digital Library Infrastruc-
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Digital Libraries. Second European Conference, ECDL’98,
Heraklion, Crete, 19-23 September, Proceedings. Lecture
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Rasmusson, A. 1999. Components of a Personal Digital
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Rhodes, B. J. 1996. Remembrance Agent - A continuously
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bition on the Practical Application of Intelligent Agents
and Multi-Agents, 487-495. The Practical Application
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Shardanand, U. and Maes, P. 1995. Social Information Fil-
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A Virtual Community Library:

Agent Augmented Community Information — The ACORN Architecture.

Decentralized Social Filtering based on Trust. In Recommender Systems Workshop Papers.

Yenta: A Multi-Agent, Referral-Based Matchmaking System. In

Personalized Decentralized Communication

Search engines, portals and topic-centered web sites are all attempts to create more or less personalized web-services. However, no single service can in general fulfill all needs of a particular user, so users have to search and maintain personal profiles at several locations. We propose an architecture where each person has his own information management environment where all personalization is made locally. Information is exchanged with other’s if it’s of mutual interest that the information is published or received. We assume that users are self-interested, but that there is some overlap in their interests. Our recent work has focused on decentralized dissemination of information, specifically what we call decentralized recommender systems. We are investigating the behavior of such systems and have also done some preliminary work on the users’ information environment

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Personalized Decentralized Communication

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