ganging up of info overload

8/3/2019 Ganging Up of Info Overload

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106 Computer

Instea d of being starvedfor informat ion, we find

ourselves overloaded.

Humans have always addressed

the high cost of finding infor-

mation by sharing itinventing

oral traditions, written lan-

guage, and the Web as informa-

tion-sharing tools. The printing press,

broadcast media, and most recently the

Internet have all changed the nature of

the information problem. Information is

no longer scarce. Indeed, there is far toomuch of it for any one person to review,

let alone organize. Instead of being

starved for information, we find our-

selves overloaded.

When information is abundant, the

knowledge of which information is use-

ful and valuable matt ers most. We all use

our network of family, friends, and col-

leagues to recommend movies, books,

cars, and news articles. Collaborative fil-

tering technology automates the process

of sharing opinions on the relevance and

quality of information.

Collaborative filtering is one techniqueamong many information filtering tech-

niques that range from unfiltered to per-

sonalized and from effortless to laborious,

as illustrated by the chart shown as

Figure 1.

Libraries or the Web are good exam-

ples of unfiltered information sources.

E-mail directed to one recipient is a

good example of a filtered informat ion

source. A best-seller list requires little

effort for the user, but provides the same

recommendations t o all users, so it is in

the upper left of the chart. Filters basedon demographics, such as age, sex, or

marital statu s, require some effort from

the user in providing the demographics,

and provide some level of persona l fil-

tering, so they are near the middle of the

chart.

Collaborative filtering requires rela-

tively little effort from the user, and pro-

vides individually targeted recommen-

dations, so it is in the upper right of the

chart.

Effort, of course, can be reduced via

automation. While collaborative filtering

is not necessarily effortless, it requires a

relatively small amount of effort on the

part of the user and provides very indi-

vidualized recommendations. The col-

laborative filtering systems that we

discuss here each offer a high degree of

personalization, but each system takes

a different approach to automation,

attempting to find the best trade-off

between the amount of work the users

must put into the system and the per-

ceived value and benefits they receive in

return.

TAPESTRY

Collaborative filtering research beganin the early 1990s at Xerox PARC in

response to the overwhelming number of

e-mail messages within PARC, which

numbered far more than could be easily

managed by mailing lists and keyword

filtering.

The Tapestry system enabled users to

add annotations to messages. Two data-

bases stored the incoming stream of doc-

uments and the linked annotation

records. A sophisticated query system

allowed users to browse for messages

based on both their content and annota-

tions.Users could set up standing filter

queries that would watch the document

stream and annotation records, finding

documents that matched the query at any

time, present or future. For instance, a

user could ask for a ll messages about col-

laborat ive filtering rat ed excellent by a

superior. Only when the message was

rated excellent would it be selected and

forwarded to the user.

Tapestry was the first step in automat-

ing recommendation-sharing among

friends and colleagues. It capitalized on

the idea that humans working with com-puters could be more effective informa-

tion filters than computers or hum ans

working alone. People understand and

judge information in ways that current

computer systems cannot, largely be-

cause people can more readily determine

quality as well as content. Because users

needed to know whose recommenda-

tions to follow, Tapestry worked best in

a small community of people who al-

ready knew each o ther.

Ganging up onInfor mat ionOver load

Al Borchers, Jon Herlocker, Joseph Konstan, and John RiedlUniversity of Minnesota

Inter

netWatch

Editor: Ron Vetter, University of North

Carolina at Wilmington, Mathematical

Sciences Dept., 601 South College Rd.,

Wilmington, NC 28403; voice (910) 962-

3671, fax (910) 962-7107; vetter@cms.

uncwil.edu


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April 1998 107

USENET AND GROUPLENSUsenet, one of the earliest and largest

bulletin board systems, was originally a

valuable source of information. But as

the number of users grew, the system

became increasingly overloaded. It

reached the point where most users

found only a few useful articles in a

group filled with dozens or even hun-

dreds of art icles a day.

The GroupLens system, started at the

University of Minnesota in 1992, at-

tempts to make Usenet useful again by

providing personalized predictions on

the qua lity of the messages. (GroupLens

has undergone dramatic changes, includ-

ing being commercialized by Net Percep-

tions. This column discusses the Group-Lens Research system.) Because there are

differences among user tastes, the

GroupLens system asks users to ra te arti-

cles on a one to five scale. GroupLens

then collects and compares these rat ings

to find users sharing similar t astes. If, for

example, you need a prediction for an

unread article, GroupLens would see

how the other users sharing your ta stes

had rated the article. If they liked it,

chances are you will too, so GroupLens

gives that article a high ranking.

GroupLens extends the Tapestry model

in several ways. The small Tapestry com-munities were limited to reading and eval-

uating only a relatively small set of

messages. A large community was needed

to generate recommendations across a

large stream of informat ion, like Usenet

news. GroupLens created a large virtual

community where users could share rec-

ommendations without actually knowing

each other. Surprisingly, the virtual com-

munity of GroupLens allowed personal-

ization at the same time it assured privacy

and anonymity. You did not need to know

the identity of those you correlated with

to gain the benefit of their recommenda-tions, unlike Tapestry where the benefits

came directly from your personal rela-

tionships with recommenders.

Conceptually, GroupLens works by

computing a correlation distance be-

tween each pair of u sers. For example,

users who are close to user Jane, accord-

ing to the distance function, form a

neighborhood for Jane. GroupLens uses

the op inions of the users in Janes neigh-

borhood to form predictions about her

interests. The opinions are weighed

according to how close each member of

the neighborhood is to Jane.

GroupLens shows that predictions

from an automated recommender system

can be meaningful to users. Predictions

generated by the GroupLens engine cor-

relate well with user ratings and are more

accurate than average ratings. Highly

rated articles are more likely to be read

and ra ted, which means that users are

more likely to rate articles so that the sys-

tem can better understand their interests.

RINGO AND VIDEO RECOMMENDERIn the mid-1990s other systems experi-

mented with variations on the GroupLens

model and algorithm. Upendra Shard-

anand and Pattie Maes developed Ringo,

an e-mail and Web system that recom-

mends music. They compared the Group-

Lens algorithm with others based on

different statistical measures of similarity

and another based on similarities among

the music CDs rather than among users.

Their research verified that predictions

improve as more ratings are collected.

Video Recommender, which makes

recommendations on movies, found a

middle ground on the trade-off between

lots of work and lots of value (the

Tapestry model) and no work and little

value (ratings by movie critics). In

exchange for submitting ratings on a

selected set of movies, the system gener-

ates personalized predictions that are

more accurate than critic recommenda-

tions. Video Recommenders predictions

have a 0 .62 correlation coefficient, whilemovie critics achieve only a 0.22 corre-

lation coefficient.

Both R ingo and Video Recommender

show that collaborative filtering can

apply to all media, even domains like

music and movies where computer-based

content analysis is not yet possible. These

systems showed collaborat ive filtering

allows serendipity where content-based

systems might not. If youve shown inter-

est only in country-western music, for

Automatic

Manual

Impersonal

Personal

Best -sellerlist

New YorkTimescritic

ConsumerReports

Implicitcollaborative

filtering

Explicitcollaborative

filtering

Web

surfing

Tapestry

Library research

Word of mouth

Demographics

Figure 1. Information retrieval techniques. The vertical dimension indicates how difficult it i s for

the end user to access the filtered information, while the horizontal dimension indicates the level

of personalization. Filters based on demographics require some effort from the end user and pro-

vide some level of personal filtering, so would be placed near the middle of t he chart. Automated

collaborative filt ering requires relati vely little effort f rom the end user and provides individually

targeted recommendations, so it would be placed in t he upper right corner of the chart.


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108 Computer

example, a content filter would only rec-

ommend more country western. In a col-

laborative recommender system, how-

ever, users whose interests correlate with

yours on country western might lead you

to discover blues albums of interest.

Ringo and Video Recommender also

extend the virtual community to a real

connected community by allowing users

to post comments for others to read and

by revealing e-mail addresses of users

who have volunteered to reveal their

identities. Users wanted to get to know

others who shared their tastes and even

requested a Video Recommender singles

club. The knowledge derived from such

clubs made users more confident in therecommenda tions they received.

LOTUSLotus developed an active collabora-

tive filtering system that revived the

Tapestry model. Lotus researchers

believed people could always give more

relevant recommendations than any

computed function, so they chose to ask

for more work from the users in

exchange for better predictions about

user interests.

Built in Lotus Notes, the system made

it easy to send pointers to Web pages.Pointers could include hypertext links

and annotations explaining the content,

context, and relevance of the document.

One pointer, for example, might read:

Sally, you should definitely see this page

on collabora tive filtering.Jane. In the

Lotus system, pointers could be sent to

groups or individuals or published for all

to see.

Lotus found a striking division be-

tween those who would provide infor-

mation and those who would use it. In

the system Lotus implemented, one user

was responsible for 80 percent of thepointers. These information mediators

can help ensure the quality of informa-

tion, helping other users grow to trust

their recommendations.

As in Tapestry, in small social work-

groups information mediators may gen-

erate enough value to spend much of

their time mediating information. In

large anonymous groups, information

mediation may require shared work from

a larger community.

GroupLens continues to evolve at theUniversity of Minnesota and we are

experimenting with new t echniques

to help people find information that is of

value to them. Weve found that time

spent reading is a fairly accurate measure

of a users rating for an article. Future

GroupLens systems, then, will use time

measurements to gather implicit ratings

and to build predictions from those rat-

ings. Users can of course immediately see

the benefits of such a system, which

requires little extra work to personalize

their information needs.

Collaborative filtering can also incor-porat e agent technology through filter-

ing robots. Filterbots can automatically

rate new articles as they appear by using

different content analysis algorithms.

The first human raters to see these arti-

cles will already see predictions, person-

alized by their correlations with the

various filterbots. New prediction algo-

rithms will likely help counter the spar-

sity problem, where users have not rated

enough items in common to correlate,

and the scalability problem, where huge

numbers of users and items require over-

whelming computing resources. y

Acknowledgements

The authors gratefully acknowledge

the contributions of GroupLens co-

founder Paul Resnick, Hack Week par-

ticipants Dave Maltz and Brad Miller, all

the members of the GroupLens Research

team, and the support of the National

Science Foundation under grant IRI-

9613960.

Al Borchers is a visiting faculty memberand postdoctoral researcher at the Uni-

versity of Minnesota. He is developing

collaborative filtering algorithms to

power the next GroupLens system.

Jon Herlocker is a PhD student at the

University of Minnesota, researching

algorithmic issues in collaborative filter-

ing and w ays to measure the effectiveness

of recomm ender systems.

Joseph Konstan is an assistant professor

of computer science and engineering at

the University of Minnesota. He alsoserves as consulting scientist for N et Per-

ceptions, a company that he cofounded

to com mercialize collaborative filtering.

John Riedl is an associate professor of

computer science and engineering at the

University of M innesota. He is also chief

technical officer of N et Perceptions and

the cocreator of GroupLens.

Contact the authors at {borchers,her-

locke,konstan,riedl}@cs.umn.edu.

Web Recommender Systems

To experiment with recommender

systems, you don t have to wait. There

are already some online. Here are a

few good examples:

http://www.wisewire.com

http://www.amazon.com

http://www.moviefinder.com

http://www.movielens.umn.edu

http://www.cdnow.com

http://www.bignote.com

To Read More about Collaborative Filtering

D. Goldberg et al., Using Collaborative Filtering to Weave an Information

Tapestry, Comm. ACM, Dec. 1992, pp. 61-70.

P. Resnick et al., GroupLens: An O pen Architecture for Collaborative Filtering of

Netnews, Proc. CSCW 94, ACM Press, New York, 1994, pp. 175-186.

J. Konstan et al., GroupLens: Collabora tive Filtering for Usenet News, Comm.

ACM, Mar. 1997, pp. 77-87.

D.A. Maltz and K. Erlich, Pointing The Way: Active Collaborative Filtering,

Proc. CHI 95, ACM Press, New York, 199 5, pp. 202-209.

W. H ill et a l., Recommending and Evaluating Choices in a Virtual Community

of Use, Proc. CHI 95, ACM Press, New York, 1995 , pp. 194-201.

U. Shardanand and P. Maes, Social Information Filtering: Algorithms for Automating

Word of Mouth, Proc. CHI 95, ACM Press, New York, 1995, pp. 210-217.

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