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[1]Principia Cybernetica Web

Collaborative Filtering

Collaborative filtering systems can produce personal recommendations by computing
the similarity between your preference and the one of other people
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Recently a number of methods have been developed for the "collaborative
filtering" or "social filtering" of information (Resnick et al. 1994; Shardanand
& Maes 1995; Breeze et al. 1998). The main idea is to automate the process of
"word-of-mouth" by which people recommend products or services to one another. If
you need to choose between a variety of options with which you do not have any
experience, you will often rely on the opinions of others who do have such
experience. However, when there are thousands or millions of options, like in the
Web, it becomes practically impossible for an individual to locate reliable
experts that can give advice about each of the options. By shifting from an
individual to a collective method of recommendation, the problem becomes more
manageable.

Instead of asking opinions to each individual, you might try to determine an
"average opinion" for the group. This, however, ignores your particular
interests, which may be different from those of the "average person". You would
rather like to hear the opinions of those people who have interests similar to
your own, that is to say, you would prefer a "division-of-labor" type of
organization, where people only contribute to the domain they are specialized in.

The basic mechanism behind collaborative filtering systems is the following:
* a large group of people's preferences are registered;
* using a similarity metric, a subgroup of people is selected whose preferences
are similar to the preferences of the person who seeks advice;
* a (possibly weighted) average of the preferences for that subgroup is
calculated;
* the resulting preference function is used to recommend options on which the
advice-seeker has expressed no personal opinion as yet.

Typical similarity metrics are Pearson correlation coefficients between the
users' preference functions and (less frequently) vector distances or dot
products.

If the similarity metric has indeed selected people with similar tastes, the
chances are great that the options that are highly evaluated by that group will
also be appreciated by the advice-seeker. The typical application is the
recommendation of books, music CDs, or movies. More generally, the method can be
used for the selection of documents, services or products of any kind.

The main bottleneck with existing collaborative filtering systems is the
collection of preferences (cf. Shardanand & Maes 1995). To be reliable, the
system needs a very large number of people (typically thousands) to express their
preferences about a relatively large number of options (typically dozens). This
requires quite a lot of effort from a lot of people. Since the system only
becomes useful after a "critical mass" of opinions has been collected, people
will not be very motivated to express detailed preferences in the beginning
stages (e.g. by scoring dozens of music records on a 10 point scale), when the
system cannot yet help them.

One way to avoid this start-up problem is to collect preferences that are
implicit in people's actions (Nichols 1998). For example, people who order books
from an Internet bookshop implicitly express their preference for the books they
buy over the books they do not buy. Customers who have bought the same book are
likely to have similar preferences for other books as well. This principle is
applied by the [2]Amazon web bookshop, which for each book offers a list of
related books that were bought by the same people.

There are even more straightforward ways to collect implicit preferences on the
web. One method is to register all the documents on a website that have been
consulted by a given user (cf. Breeze et al. 1998). The list of all available
documents, with preference 1 for those that have been consulted and preference 0
for the others, then determines a preference function for that user (cf. Breeze
et al. 1998). Using a similarity metric on these preference vectors makes it
possible to determine neighborhoods of users with similar interests.

More info:

* [externallink.GIF] [3]Collaborative Filtering Research Links: a list of
papers about collaborative filtering, with abstracts and links to the full
papers
* [externallink.GIF] [4]Collaborative Filtering Resources at Berkeley and at
[externallink.GIF] [5]the SIGGroup
* Breese J.S., Heckerman D. and Kadie C. (1998), [externallink.GIF]
[6]Empirical Analysis of Predictive Algorithms for Collaborative Filtering,
Proceedings 14th Conference on Uncertainty in Artificial Intelligence,
Madison WI: Morgan Kauffman.
* Nichols D.M. (1998) " [externallink.GIF] [7]Implicit Rating and Filtering",
Proc. Fifth DELOS Workshop on Filtering and Collaborative Filtering,
Budapest, Hungary, 10-12 November 1997, ERCIM, 31-36.
* Resnick P, Iacovou N., Suchak M., Bergstrom, and Riedl J. (1994), "
[externallink.GIF] [8]GroupLens: An open architecture for collaborative
filtering of netnews", Proceedings of ACM 1994 Conference on Computer
Supported Cooperative Work, Chapel Hill, NC: ACM, 175-186.
* Shardanand U. and Maes (1995), [externallink.GIF] [9]Social information
filtering: Algorithms for automating "word of mouth", Proceedings of CHI'95
-- Human Factors in Computing Systems, 210-217
* A. Chislenko's essay on [externallink.GIF] [10]Automated Collaborative
Filtering and Semantic Transports
* [externallink.GIF] [11]Fifth DELOS Workshop on Filtering and Collaborative
Filtering, Budapest

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Author
F. [14]Heylighen,

Date
Jan 31, 2001 (modified)
Mar 24, 1999 (created)

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References

1. LYNXIMGMAP:http://pespmc1.vub.ac.be/COLLFILT.html#PCP-header
2. http://www.amazon.com/
3. http://www.jamesthornton.com/hotlist/collabfilters.html
4. http://www.sims.berkeley.edu/resources/collab/
5. http://www.acm.org/siggroup/collab.html
6. http://research.microsoft.com/users/breese/cfalgs.html
7. http://www.comp.lancs.ac.uk/computing/research/cseg/projects/ariadne/docs/delos5.html
8. http://www.acm.org/pubs/citations/proceedings/cscw/192844/p175-resnick/
9. http://info.sigchi.acm.org/sigchi/chi95/Electronic/documnts/papers/us_bdy.htm
10. http://www.lucifer.com/~sasha/articles/ACF.html
11. http://www.ercim.org/publication/ws-proceedings/DELOS5/
12. http://pespmc1.vub.ac.be/COPYR.html
13. http://pespmc1.vub.ac.be/REFERPCP.html
14. http://pespmc1.vub.ac.be/HEYL.html
15. http://pespmc1.vub.ac.be/DEFAULT.html
16. http://pespmc1.vub.ac.be/ORG.html
17. http://pespmc1.vub.ac.be/^COLDEV.html
18. http://pespmc1.vub.ac.be/WEBRESEA.html
19. http://pespmc1.vub.ac.be/WEBCONAN.html
20. http://pespmc1.vub.ac.be/KNOWSTRUC.html
21. http://pespmc1.vub.ac.be/MAKANNOT.html
22. http://pespmc1.vub.ac.be/hypercard.acgi$annotform?

[USEMAP]
http://pespmc1.vub.ac.be/COLLFILT.html#PCP-header
1. http://pespmc1.vub.ac.be/DEFAULT.html
2. http://pespmc1.vub.ac.be/HOWWEB.html
3. http://pcp.lanl.gov/COLLFILT.html
4. http://pespmc1.vub.ac.be/COLLFILT.html
5. http://pespmc1.vub.ac.be/SERVER.html
6. http://pespmc1.vub.ac.be/hypercard.acgi$randomlink?searchstring=.html
7. http://pespmc1.vub.ac.be/RECENT.html
8. http://pespmc1.vub.ac.be/TOC.html#COLLFILT
9. http://pespmc1.vub.ac.be/SEARCH.html

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