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Media LaboratoryMassachusetts Institute of Technology

20 Ames Street #E15-305Cambridge, MA, 02139 USA

{ lieber, nwv, adriana} @media.mit.edu

ABSTRACTWeb browsing, like most of today’s desktop applications, isusually a solitary activity. Other forms of media, such aswatching television, are often done by groups of people,such as families or friends. What would it be like to docollaborative Web browsing? Could the computer provideassistance to group browsing by trying to help find mutualinterests among the participants? -/.�021 3547698;:<39. is anexperiment in building an agent to assist a group of peoplein browsing, by suggesting new material likely to be ofcommon interest. It is built as an extension to the single-user Web browsing agent Letizia. Let’s Browse featuresautomatic detection of the presence of users, automated“channel surfing” browsing, and dynamic display of theuser profiles and explanation of recommendations.

KeywordsBrowsing, collaboration, agents, user profiles

COLLABORATIVE BROWSINGIncreasingly, Web browsing will be performed incollaborative settings, such as a family at home or in abusiness meeting. For example, WebTV estimates that theaverage number of people who are watching during asession with its service is two, indicating that multi-userbrowsing is the norm rather than the exception. In mostsuch situations, one person has control of the remote or thekeyboard and mouse, and the others present are relativelypassive. Yet the browsing session can't be consideredsuccessful if the interests of others present are not takeninto account.

Collaborative browsing can take many forms. A group ofpeople may be searching for specific information, exploringpreviously unexplored territory to see what's interesting, orsome combination of the two. What links a person choosesto view and how a person reacts to what appears can alsoserve as a meaningful form of communication between theparticipants. Participants can learn about each other as wellas learn about the content of the Web pages.

Fig. 1: Let’s Browse screen display

Increasingly, also, we believe that Web browsing will beassisted by intelligent agent software, which can keep trackof user’s interests, inferring interest from observing useractions, and autonomously looking for items that satisfyinterests. We have had extensive experience with such asingle-user agent, =/>@?BA C9ABD , [8, 9] which performsreconnaissance on Web pages. Letizia proactively fetcheslinks from the page currently viewed, and chooses thosepages that best match a user profile learned by watching theuser’s choices. Letizia presents its recommendations in aseparate, “channel surfing” window that continuouslydisplays recommendations.

We were interested in extending the channel surfingmetaphor to situations where, as in TV, more than oneperson may be watching. Even if only person “has theremote control,” the agent can be cast in the role ofrepresenting the interests of the other participants, withoutrequiring negotiation at every step, which may bedisruptive. The job of the agent is to choose, from the linksreachable from the current page, those that are likely to bestsatisfy the interests of all the participants. We call theresulting system E/F@GIH J/K7LNMPO<JNF .EXPERIENCE WITH THE PROTOTYPEThe first experiment with the Let's Browse prototypesystem was set up on the occasion of the Media Lab'sDigital Life Consortium meeting in October 1997. The idea

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http://www.media.mit.edu/~lieber/Lieberary/Lets-Browse/Lets-Browse-Intro.html

was to experiment with an automated browsing system thatsurfed on behalf of the common interests of a set of userswho were physically present at a single screen. Theenvironment was originally intended for use with the userspresent before a wall-sized display, but was actuallydeployed with a single 19” CRT monitor (Fig. 1).

The goal is that users can walk up to the screen, and theirpresence is automatically sensed by Let's Browse, withoutany explicit action on their part. The screen wouldcontinually show a selection of Web pages that the agentdetermined might be of common interest to the participants,together with explanation of its choices.

Sensing the Presence of UsersPresence of the users was detected by use of “Meme Tags,”active badges worn by the participants (Fig. 2), prepared bythe Media Lab's Learning and Epistemology group. Thesebadges were also being used at the same event, in anotherexperiment to track the flow of QSRTQSRIU , short phrases thatwere communicated from person to person in the group [1].These electronic badges communicated the user's identity,transmitted through an infrared link. The range oftransmission is good to a few meters in line of sight of thereceiver, mounted on the Let's Browse display screen.

Interest Profiles of the UsersThe identities of the users currently in front of the screenare used to index into a set of user profiles, in order toconstruct an interest profile for the group. We used severalheuristics to try to determine user interests (semi-)automatically, rather than present the users with an explicitinterest questionnaire.

In advance of the event, we determined interest profiles foreach of the 475 attendees, by running an off-line Webcrawler that scanned a breadth-first search around eachattendee's page. The crawl started from either the homepage of the attendee or the home page of the attendee'sorganization if their personal page was not known.

For each page, we performed a TFIDF (term frequencytimes inverse document frequency) keyword frequencyanalysis to extract terms approximating the “subjectmatter” of each page, a common technique in Web searchengines. The crawler operated with a time limit thattypically allowed scanning between 10 and 50 pages per

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Fig 2: A Meme Tag and the kiosk configuration

user. A user profile is essentially a set of keyword-weightpairs, similar to the user profiling techniques used in thesingle-user agent Letizia [8].

The purpose of the off-line compilation of user interestswas to "prime the pump", since when the user walks up tothe screen, the agent typically does not have enough time tobegin the process of determining the user's interests beforethe user expects to see some meaningful results. This is incontrast to Letizia, which does not start out with anyprecomputed profiles. We also found that, in contrast to asingle user, who often has the patience to wait a while untilthe system learns his or her preferences, groups are far lesspatient, and it is important that the system be able todemonstrate its capability to make good choices quickly.However, if the Web access were fast enough, all theinterest profiles for Let's Browse could also be computedon the fly.

With the current setup, indexing into precomputed userprofiles gives the agent some immediate fodder to work on.Extra compute time during operation can be used tocontinue the search, deepening the breadth-first search foreach user, interleaving this with the search for pages torecommend to the user. If a user appears whose profile isnot already stored in the database, the system can startcrawling around his or her web page to dynamicallycompute a profile, getting as good an approximation as ispossible in the time allotted.

An interesting result from the Let's Browse experimentconcerned the number of terms stored in the user profile. InLetizia, only a small number of terms were retained fromthe TFIDF computation on each page, typically about 10.This was justified by results from information retrieval thatshow that retaining a large number of terms per documentdoes not improve the indication of the content of thedocument by very much.

However, for Let's Browse, this was not sufficient, and weneeded to jack the number of terms up to about 50 to getacceptable results. The reason is that low-frequency terms,while by themselves not very good indicators of content,become very significant if they are shared by your othercollaborators, even if they are also low-frequency terms forthem. Their commonality increases the value of that termfor both of you. Keeping a larger number of terms thereforeincreases the chance for a larger intersection of interestsbetween collaborators.

Selection of Recommended PagesThe left half of the Let's Browse display is a browsercontaining the pages currently recommended by Let'sBrowse (Fig. 3). Whenever the list of current userschanges, because a users walks up to or walks away fromthe workstation, the browsing restarts from a commonpage, in our case the Media Lab's home. The browsingproceeds by a breadth-first scan from the initial page,filtering through the user profiles.

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Fig. 3: Breadth-first search of graph of linked Web pages

The kind of browsing performed by Let’s Browse might betermed ~2���@�P~��N�@���P�\���\� �T�N�P����� , as opposed to a �\�I���\�;��9��� �;�\����� �e�9�;� �@� that we might get if instead we called up asearch engine such as AltaVista on the terms of the userprofiles and presented the results. Two pages are linked onthe Web because some human being thought that someonewho looked at one page might want also to look at theother. The “neighborhood” of pages a short distance fromthe current page constitutes a good approximation to thesemantic neighborhood of the current page. This is in starkcontrast to the set of pages retrieved by a search engine ona given query, which have no connectivity informationwhatsoever. Let's Browse shows just those pages in thesemantic neighborhood of the current page that show goodmatches to the profiles of the current set of users.

If the page currently being searched matches the profiles ofthe users above a certain threshold, it is chosen for displayto the user. We also compare the page being considered tothe page currently being recommended, if any, so that wedon't show the user a page that is significantly worse thanthe page he or she is seeing already. Recommendationstypically came at the rate of between a few a minute andone every few minutes. We buffer up the recommendationsand release them at fixed intervals, to create a smootherflow of recommendations to the user.

We used a simple linear combination of the profiles of eachuser, so that the recommendation was the page that scoredthe best in the combined profile. There are otherpossibilities for a recommendation policy. Instead, onecould compute the score for each participant independently,then look for the recommendation that scored the best forany of the participants, so that a strong interest on the partof one participant is not discouraged by low interest on thepart of the others. Alternatively, participant profiles could"take turns" influencing the recommendations, so as not toallow one participant's interest, or even an average, todominate the group over a period of time.

Participant Browsing InputIn our experimental setup, we did not have any suitablemeans for interactive input from the participants. Thus weoperated Let's Browse in an entirely passive “automaticchannel surfing” mode. The next stage would be toconsider interactive browsing input from the participants.

The simplest form of input would be a selection of a linkfrom a currently displayed page. In Letizia, every linkselection results in adding the page selected to the userprofile. In Let's Browse, the closest analogy would be toadd the page to the profile of each user, counting thatselection as a joint decision between the participants, to becredited equally among them. If we had multiple pointingdevices, we could credit the decision to the person wieldingthe pointing device, with perhaps a weaker "passive assent"credit on the part of the other participants.

Another possible configuration for a Let's Browse systemwould be to run independent Let's Browse browsers foreach participant, and thus each participant would havecontrol over their own input and profile. The browserscould then get out of sync with each other, butrecommendations could still be displayed based on thecommon profiles. Finally, the most independent possibleconfiguration would be to run completely independentcopies of Let's Browse for each participant.

This last possibility would result in another interestingmode of use. By running someone else's profile on yourown browsing activity, you are essentially “browsing withanother person's eyes.” Thus the agent is giving you an ideaof what another person might have found interesting. Incontrast to another person giving you specific Web sites tolook at, this kind of browsing allows you to examine Websites as the other person might, even if they have never seenthe page before. For example, if you are not an expert oncars and are in the market to purchase one, it might behelpful to browse car manufacturer's sites with a profile ofa friend who is an expert in the kind of cars you are lookingfor. This is a new kind of expertise sharing that deservesfurther exploration.

Visualization of the Recommendation ProcessEarlier Web agents such as Letizia merely showed therecommendations without detailed explanation of whyrecommendations were made. In Let's Browse, we providea visualization that explains why the agent made thechoices it did. This consists of a section that presents thecommonalities between the users, followed by a section foreach individual user (Fig. 4).

The names of the users are highlighted, and the top fewcommon terms that led to the selection of the paper arenoted. For each user, their name, address, e-mail, and apicture gleaned from their home page are displayed. Thetop few terms from their profiles are displayed, with theterms that are in common with the other participantshighlighted.

Fig. 4: Exposing recommendation and profiles

RESULTSWhile we did not conduct controlled experiments, theparticipants reacted favorably to our initial experiment. Inthis conference-like setting, the participants couldreasonably be expected to have some commonality ofinterest, though the exact interest match in any small groupwas usually not known. The group consisted of a fewrepresentatives each from a large number of companies, solarge cliques that already knew each other well were rare.Since they were browsing the pages of the Media Lab,which they all had come to visit, some commonality wasassured, but which aspects served to attract attentiondiffered considerably from group to group. Thus, the agentserved mainly in the function of icebreaker. How it wouldwork for enhancing long-term collaboration betweenpeople who already knew each other well remains to beseen.

RELATED WORKSeveral projects have been done that allow multiple usersto collaborate in browsing manually, either by coupling theuser interface of two or more browsers synchronously, orasynchronously by recording and playback of browsinghistories. This may be augmented by audio- or video-conferencing, or by textual chat among the participants.ARIADNE [12], GroupWeb [3], and recent features in LotusNotes™ are all examples of this approach. Webhound [7]used collaborative filtering techniques similar to those usedin [4], [13], and Firefly to recommend Web pages that werechosen by other users whose overall tastes in Web pagesmatched yours, though explicit rating of pages. None ofthese agents has the kind of incremental, real-time,autonomous operation found in Let's Browse.

Other kind of agents do perform autonomous explorationand incremental learning, but in service of a single useronly. A good example is WebWatcher [6], and our ownLetizia [8, 9]. Other examples of agents that assistbrowsing for a single user can be found in [2] and [5].

Silhouettell [10] shares goals with Let’s Browse, but differsin approach. Silhouettell uses a Web search engine querybased on user-provided keywords to recommend singlepages of common interest, whereas Let’s Browse usesprofiles extracted from users’ Web pages to navigatethrough the Web. An important component of Silhouettellis to use machine vision for identifying users.

ACKNOWLEDGEMENTSSupport for this research was provided in part by grantsthrough the Media Laboratory's Digital Life Consortium,News in the Future Consortium, British Telecom, IBM, andother sponsors of the MIT Media Lab.

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