ABSTRACTBrowsing and searching for documents in large, onlineenterprise document repositories are common activities.While internet search produces satisfying results for mostuser queries, enterprise search has not been as successfulbecause of differences in document types and user require-ments. To support users in finding the information they needin their online enterprise repository, we created Docu-Browse, a faceted document browsing and search system.Search results are presented within the user-created docu-ment hierarchy, showing only directories and documentsmatching selected facets and containing text query terms. Inaddition to file properties such as date and file size, auto-matically detected document types, or genres, serve as oneof the search facets. Highlighting draws the user’s attentionto the most promising directories and documents whilethumbnail images and automatically identified keyphraseshelp select appropriate documents. DocuBrowse utilizesdocument similarities, browsing histories, and recom-mender system techniques to suggest additional promisingdocuments for the current facet and content filters.

Author KeywordsDocument retrieval, document management, faceted search, document visualization, document recommendation.

ACM Classification KeywordsH5.2. Information interfaces and presentation: User Inter-faces; H3.3. Information storage and retrieval: Information search and retrieval.

General TermsAlgorithms, Design, Human Factors.

INTRODUCTIONEnterprise search has been defined in different ways, includ-ing search of an organization’s intranet, search of an organi-zation’s external web site, and search of any text content inelectronic form, such as email and databases [6]. In this

paper we focus on search of unstructured information in acorporate document repository. In this type of enterprisesearch, an employee typically knows or remembers someattributes of the target results [12]. Locating documents inan enterprise often involves finding specific documents thateither the user created or the user knows or expects that theywere created. In these activities, the user’s knowledge of theorganization, its history, and its policies and practices can bevaluable in locating the desired documents. This is in con-trast to Internet users who are searching among a set ofunfamiliar documents. Additionally, Internet users are oftensearching for a fact, such as the phone number for the localrestaurant, or a general discussion of a topic, such as what ishappening with a particular politician.

In this paper, we present a method for providing search andnavigation options to the user through the use of metadataand the document collection file structure. Enterprise docu-ment collections are often organized in hierarchies similarto directory trees in file systems. These hierarchies are rep-resentations of the policies and practices of the organization,often partly mapping to the structure, roles, and activities inthe organization. While newer interfaces allow for access todocuments within multiple categories via tags or othermechanisms, the user experience is still relatively similar:users view the set of categories or directories and navigatethrough the options displayed to locate documents of inter-est.

To improve system support for navigation and selection inenterprise document collections, we combine data-orienteddocument analysis with novel interface design. A facet-based interface designed to run in a web browser provides arich user experience enabling a combination of search andnavigation-based location strategies. Because the documenttype, e.g., spreadsheet, is a useful facet for search, we auto-matically identify the genre based on features of the pageimages. Document analysis is also used to determine repre-sentative keyphrases of documents to provide a quick over-view of each document. These capabilities are part of thenew DocuBrowse environment.

In the next section, we elaborate on our vision for enterprisesearch. The following sections present an overview of themixed-initiative interface for browsing and searching docu-ment collections and discuss recommendation approachesappropriate to the enterprise context. This is followed by adescription of the document analysis component with an

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DocuBrowse: Faceted Searching, Browsing, and Recommendations in an Enterprise Context

Andreas Girgensohn1, Frank Shipman2, Francine Chen1, Lynn Wilcox1

1FX Palo Alto Laboratory, Inc. 2Department of Computer Science &3400 Hillview Avenue Center for the Study of Digital Libraries

Palo Alto, CA 94304, USA Texas A&M UniversityCollege Station, TX 77843-3112

{andreasg, chen, wilcox}@fxpal.com, shipman@cs.tamu.edu

emphasis on genre identification. We conclude with a visionof how such technologies will change the way people andbusinesses will store and retrieve documents.

ENTERPRISE SEARCHEnterprise search is often aided by the user’s memory ofsome properties of the document or the circumstances underwhich it was presented. We describe two typical examplesof this. An employee needs to find information from a pastproject review presentation. He does not know where thepresentation material is located, who gave the presentation,or even which organization in the company created thematerial. He does recall information in table format in aslide presentation last spring. Another employee is workingon solving a problem in product design and remembers asimilar problem that was solved in another product severalyears ago. She would like to find information on the solu-tion, including how it was solved and who solved it. Sincethe product manager has since left the company, she needsto search the document repository for the information. Sheknows the product name, the rough time frame, and thenature of the problem.

Unlike in enterprise searches, in typical internet searchesthe user does not have any prior knowledge of the docu-ments but looks for facts such as a restaurant’s phone num-ber or background about a politician. For these types ofrequests, many different documents meet their needs. Inaddition to the restaurant’s web site, the restaurant phonenumber could be found on a restaurant review site or a Yel-low Pages site. Similarly, articles about the politician can befound on many different news papers and blogs. Anotherdifference is that a Web search result that gets the user closeto the result often includes links for browsing to the desiredcontent. In contrast, documents in an enterprise context donot include links with which to browse between documentsand that can be used to compute relevance. As the above dif-ferences imply, the content-based search techniques used onthe Web, while helpful, do not fully address the problem ofenterprise search and document access. Thus, other tech-niques to enhance web-type text queries are needed.

Enterprise search also has to deal with scanned paper docu-ments. While most enterprise documents start in digitalform, they often have a period of their life-cycle as paperdocuments. They need to be signed, annotated, or handedfrom one person to another. As a result, a document thatstarts in an easily processable electronic document format(e.g., Microsoft Word) often becomes a scanned documentlater. The content of the scanned document may be mostlyreconstructed through OCR but the result is that all types ofdocuments, whether they start out in a word processor, aspreadsheet, a database, or presentation software, end up asthe same document format.

Recommender systems are useful in many contexts andhave become common tools for people finding movies,books, etc. Thus, it is natural to apply recommender tech-niques to the enterprise context. Unfortunately, most recom-mender techniques are not directly applicable due toassumptions about information availability and accesshomogeneity. Typically, recommender techniques rely onindividuals having access to the same set of resources. In a

corporate context, each employee is likely to have access toa different subset of resources. This may undermine some ofthe statistical analysis techniques commonly used. Thesesystems also rely on users being willing to share evaluationsof resources and interests. Such information is unlikely tobe available in the enterprise context. The enterprise socialsetting makes it inappropriate for employees to rate eachother’s (or their boss’s) documents. Likewise, issues of pri-vacy and compartmentalization makes it unlikely for infor-mation that can be used to determine who is working onwhat to be centralized. However, we can use certain types ofrecommendations such as documents matching the querythat other users with similar queries viewed or documentsthat were recently viewed by other users in the same organi-zation.

DOCUBROWSEOur DocuBrowse system supports faceted searching, brows-ing, and recommendations in an enterprise context. It com-bines well-known techniques for supporting documentaccess, including browsing the structure of the documentcollection, searching content, filtering based on metadata,and presenting recommendations based on past user orgroup activity (see Figure 1). We have deployed the systeminternally to provide access to 9,000 office documents and50,000 images created over the past ten years.

Browsing the Document CollectionEnterprise workers typically organize their document col-lections into sub-collections that group documents based oncharacteristics of their content, generation, or use. In filesystem-based document stores, the collection structure isthe directory hierarchy. For documents with metadata, docu-ments may be placed based on an ontology of the metadataconcepts (e.g., MeSH Terms for the National Library ofMedicine).

Acknowledging the centrality of browsing through collec-tions, we created DocuBrowse as a web-based interface thatmakes browsing such structures easy and intuitive. As such,the majority of DocuBrowse’s display is used to present thecontents of the user’s current location in the document col-lection. Subcollections (e.g. directories) are presented abovethe individual documents. DocuBrowse employs modernweb technologies to quickly respond to user requests in anasynchronous fashion, for example, by retrieving additionalinformation about a document to be displayed in a tool tip.

Unlike traditional file systems, DocuBrowse allows users toput a document into more than one “directory” and, unlikeWindows short cuts, each document entry provides directaccess to the original document. In the DocuBrowse proto-type, documents are identified by a content hash. Thisenables automatic duplicate detection so that only one copyof the document has to be stored. As a change to a documentchanges its identifier, all entries for that document have tobe updated. A history mechanism can point to previous ver-sions of a document. To explicitly point to an older versionof a document, an entry has to be marked to prevent updatesfor new document versions.

The document hierarchy is stored in a database. To speed upqueries that need to check all documents in a directory sub-

tree, e.g., for selecting thumbnails for directories, we alsostore the containment closure by directly storing all parentdirectories for each document and directory. That enables usto retrieve all documents or directories in a directory treewith a single query. While the initial document hierarchy ismodeled after the file system hierarchy provided by the user,multiple hierarchies can be supported. Each hierarchy isstored in database tables so that no changes to the flat docu-ment storage area are required when a hierarchy is added orchanged. Other hierarchies may be based on documentproperties or on external semantic hierarchies such as theLibrary of Congress classification for books.

Visualizing DocumentsDocuments are visualized by a box including the documentname, metadata, and automatically selected keyphrases onthe left and a thumbnail of the first page of the document onthe right (see Figure 2). Clicking anywhere in the documentopens the document in the document viewer. The documentthumbnails and the information text are offered in differentsizes that can be changed quickly by using a slider (seeFigure 1). This slider employs dynamic web technologies tozoom in or out without having to reload the whole page.Thumbnails are created in many different sizes when docu-ments are added to the collection so that thumbnails of arequested size can be shown quickly. Figure 4 shows a pagewith larger thumbnails.

Subcollections, or more simply collections, are visualizedby three thumbnails of selected documents in that collection(see Figure 2). Those thumbnails are cropped to squares tobetter fill the directory box. If fewer than three documents

Figure 1. Top-level of the DocuBrowse document hierarchy.

Search facet input Document directory Number of documents Slider for thumbnail sizeDate range Document thumbnails

Document Keyphrases KeyphrasesDocument tool tip Inline page viewer

Figure 2. Directory and document representations.

exist in that collection, then only that number of thumbnailsis presented. Thumbnails are selected such that a good sam-ple of the documents in the collection is provided. If a key-word or facet search is active, directory thumbnails are cho-sen among the documents that best match the search query.In addition to the thumbnails, the collection’s name and sta-tistics about its contents are shown. The statistics includethe number of documents and the date range. Clicking onthe collection visualization navigates to that subcollection,replacing the current list of collections and documents.

When the mouse lingers over a document or a thumbnail fora document in a subcollection, an interactive tool tip appearsthat provides easy access to more detailed information aboutthat document. The tool tip also includes a small documentviewer that allows the user to flip through images of thedocument pages. This lets the user quickly verify if this isthe desired document before opening the full-size documentviewer.

Search- and Filter-Based Navigation SupportUnlike traditional search systems that display a list ofmatching documents, DocuBrowse uses a combination offiltering, color-coding, and browsing to present searchresults. This approach keeps matching documents in contextand makes it easy to narrow or widen a search for a subcol-lection. Because the document organization tends to reflectthe structure and practices of organizations, we maintainthat structure in the visualization of results.

To find documents with certain characteristics or contents,DocuBrowse provides filters for different facets of the docu-ments. An important facet is the type, or genre, of a docu-ment. Our automatic genre detector is trained to identifytechnical papers, slides, tables, and photos that correspondroughly to the file formats MS Word, PowerPoint, Excel,and JPEG. By selecting a genre, only documents matchingthe genre and directories containing those documents areshown. For fuzzy genre matches, color coding is used toindicate the strength of the match.

In addition to genres, DocuBrowse can filter the resultsbased on document size and date. More options can be pro-vided for document collections where additional metadatafacets are available. When the user specifies values for afacet, only those documents that fit those values are shownin the browser. DocuBrowse also supports full-text search,so documents that partially or completely match the terms inthe query are displayed in the browser while non-matchingdocuments are filtered out. As with fuzzy genre matches,color coding indicates the quality of the match.

When a search or filter is active, the visualization of subcol-lection is colored to show where large numbers of matchingdocuments are located (see Figure 3). The score for a direc-tory tree combines the score of the best-matching documentwith the total number of matching documents and the den-sity of the match scores. The match score for a directory isgiven by:

Figure 3. Subcollection restricted to documents matching the “slide” genre.

where b is the best match score among documents in thedirectory tree, d is the normalized density, and c is the nor-malized count. The density is the average match score,including documents with a match score of zero. The countis the number of documents with a non-zero match score.Both d and c are normalized relative to the greatest valuefrom the subdirectories being compared. For combining dand c, we chose the quadratic mean because it comes closeto picking the maximum of the two values without com-pletely ignoring the other value. Document thumbnails foreach directory are chosen from those documents that bestmatch the query while balancing the selection from differentbranches of the directory tree. Documents with multiplepaths to them are only included once.

Multiple document facets can be combined to furtherrestrict the matching documents. For example, after havinglocated slides in a document collection, the user can furtherrestrict the matching documents to those that also containthe specified text string (see Figure 4). Note that each direc-tory in Figure 4 is only visualized by a single documentthumbnail even though multiple documents are contained inthose directories. This is due to the fact that only a singledocument matches the query in each of those directories.For combining the results of fuzzy facet searches, we multi-ply the individual document scores. That produces resultsconsistent with conjunctions in boolean contexts.

Document ViewerThe DocuBrowse viewer provides access to the documentpages without requiring other software such as Flash orAdobe Acrobat. It offers two fairly traditional views. Oneview provides thumbnails of all pages in the documents.Just like in the collection view, a slider allows the user toquickly change the size of the thumbnails. While such aview is available in applications such as Microsoft Power-

Point, it is less common in document viewers. The readingview includes thumbnails on the left with a large view of asingle page on the right. It is quite similar to a view pro-vided by Adobe Acrobat.

The snippet view displays more details for full text searchresults. It shows the thumbnails of pages with matchingterms and a larger view of the snippets of text in whichthose terms appeared (see Figure 5). This view provides aquick view of text matches that are too small to see in athumbnail view. By showing the outlines of snippets in thepage thumbnails, context for the snippets is provided.

Search ScenarioTo illustrate our approach to search, we describe a use sce-nario. The Director of Research at a research laboratorywants to access details about a presentation system that wascreated a few years ago. When looking at the top-level viewpresented by DocuBrowse (Figure 1), she notices the“Admin” directory and realizes that project reviews storedin that directory would be a good source for the desiredinformation. After navigating to that directory, she restrictsthe view to documents in the “slide” genre (Figure 3).Among the keyphrases displayed for one of the documentsis the word “ePIC” that sounds familiar. To make sure thatthis is indeed the desired system, she performs a text searchwith that term. This reduces the view to two subcollectionsand one document (Figure 4). Clicking on the document dis-plays search clippings that provide sufficient information toidentify the system (Figure 5).

ENTERPRISE-ORIENTED RECOMMENDATIONSDesigning mechanisms to generate suggestions requires anunderstanding of the structure of activity within an enter-prise. We look at different properties of corporate organiza-tions. Once appropriate recommender groups have beenidentified, we base our recommendations on recency, typeof access, and document similarity. We present recommen-

s b d2

c2

+2

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Figure 4. Slides containing the text “ePIC.”

Figure 5. Clippings of pages containing search text.

dations within DocuBrowse and indicate the basis for eachrecommendation.

Defining Recommender Groups in a Corporate SettingAs described by Simon, the hierarchic structures of organi-zations are meant to limit the need for information flowbetween parts of the organization [15]. As a result, onlyvery general documents such as phone lists, policy descrip-tions, and guidelines are likely to be widely available acrossan organization. This raises the problem of identifyingactivity in the organization’s information access that is pre-dictive of future needs of an individual.

Instead of using interaction history of the whole user com-munity to recognize people with similar information needs,as is generally true in recommender algorithms, we proposeto use subgroups of the organization chosen based on anunderstanding of information access in organizations. Twoattributes of individuals are being used to identify sub-groups:

• Organizational structure. As the basis of Simon’s argu-ment about the effects of bounded rationality on organiza-tional structure, the information needs of people in thesame part of the organization are likely to be indicative ofthe needs of others in that part of the organization. Thefirst subgroup considered are those individuals that arepart of the same organizational component. Determina-tion of the organizational levels used for this groupingrequires knowledge of the organization.

• Job classification. Simon notes that the organizationalstructure is not the only hierarchic decomposition of anenterprise. A classification indicative of the type of activ-ity one is involved in is one’s job title and different typesof activities (e.g. accounting, purchasing, administration,etc.). Thus, our second subgroup used to generate sugges-tions is the set of people with the same or similar job title.Again, some knowledge of the organization is required todetermine which job titles (e.g. assistant professor, associ-ate professor, professor) should be combined into a singlegroup.

To generate suggestions based on each of these groups, weaggregate individual access histories into relevance scoresas described below. Thus, all of the individuals in a speci-fied organizational layer are included in organizationalstructure recommendations. If a complete organizationalstructure is available, the individual assessments can beweighted by distance from the individual accessing the doc-ument store. Otherwise, all individuals within that structureare equally weighted. Similarly, all individuals within theset of job titles defined as equivalent for this purpose areincluded for generating job classification recommendations.

A final change with regard to traditional recommender algo-rithms is that suggestions can be for directories as well asindividual documents. Directories are important in organi-zations as the location where documents in a particularsequence are kept. Thus, while past interactions with theJanuary, February, and March accounting files for a officewould not point to the April accounting file in a traditionalrecommender approach, they will point to the directory thatincludes the April file.

Computing Document ValueOur recommendations are provided based on recency, typeof access, and document similarity. There are three methodsto generate suggestions. The first is based on the individ-ual’s past interactions with documents. The second and thirdare based on access and viewing by the subgroups of theorganization just described. To compute the likely interestvalue of a document for a particular individual, we currentlyuse a simple model to incorporate the type and history ofaccess by that individual. DocuBrowse distinguishesbetween documents that have been viewed in the interactivetool tip and those that were opened in the document viewer.We consider the former to be a quick glance and the latter amore detailed exploration. To differentiate between thosetypes of access, we record the most recent view event foreach and then apply an exponential decay to an initial score.In our current implementation, both types start with thesame score. Tool tip views have a half life of 30 minutes anddetailed views have a half life of 60 minutes. This frame-work can be expanded to include additional forms of accessas they become available in DocuBrowse. Also, instead ofonly considering the most recent view, more of the interac-tion history could be considered. For example, the decayingscores for all views could be added.

Computing the interest value of a directory is based on theinterest of the files within that directory. When a significantfractions of documents in a directory would be suggested,we instead suggest the whole directory. This approach isapplied recursively such that the parent directory is recom-mended if most subdirectories and documents in it aredeemed to be relevant.

Presenting Suggestions in DocuBrowseDocuBrowse presents suggestions in a floating pane belowthe main browsing interface (see Figure 6). If a directory issuggested instead of an individual document, the directoryis visualized in a fashion similar to the directory listing. Thethumbnails of the three most relevant documents are shown

Figure 6. Suggestions for relevant documents.

with the most relevant one in the front (see second sugges-tion in Figure 6).

Because there are multiple methods used to generate sug-gestions, DocuBrowse exposes the form of reasoning usedto generate each suggestion through color coding. We cur-rently indicate the following types of suggestions:

• Suggestions based on personal interaction history.

• Suggestions based on the interaction history of membersof one’s organizational branch.

• Suggestions based on the interaction history of employeeswith similar job titles.

• Suggestions based on multiple lines of reasoning.

Users of the system do not need to know the specifics of thereasoning approaches. It is natural that some forms of rea-soning are more valuable for some jobs than others. Experi-ences examining suggestions with different color codingswill lead users to learn which classes of suggestions workbest for them.

KEYPHRASE SELECTIONKeyphrases that give a sense of the content of a documentare used in the document summaries presented in Docu-Browse. A small number of keyphrases that can be com-pactly presented are needed. We decided that a larger num-ber of shorter keyphrases would be more informative, andso five keyphrases that are up to three words long areselected for each document.

Our method identifies sequences of words between stopwords and other textual cues, such as punctuation, includingPowerPoint bullets, and changes in font style and size, ascandidate keyphrases. For each document, the candidatekeyphrases are scored and the best N keyphrases selected,where N is prespecified and may be dependent on theamount of screen space available to the application.

To select the best keyphrases, a weighted combination offeatures is used. The features are text based and include: (1)number of times a term occurs in the document, (2) numberof documents in which a term occurs at least once in anEnglish corpus, (3) number of tokens in the keyphrase, (4)location of first mention of the term in the document, mea-sured as paragraph number.

The weighted combination of features is given by:

where λi is the weight given a feature and fi(kj,d) is the valueof feature i for keyphrase candidate kj in document d. Onceeach of the keyphrases is scored, they are then rankedagainst each other and the best keyphrases are selected foreach document.

Evaluation of the keyphrase selection has been limited tovisual inspection of the keyphrases displayed by Docu-Browse (Figures 1, 2, and 3). As with sentence-based sum-marization tasks, there are generally many more keyphrasesthat are suitable keyphrases for conveying the gist of a docu-ment than can be displayed, and evaluation is a tricky

endeavor. For technical papers and slides, the keyphrasesdisplayed by DocuBrowse have been found to be good.

Although we have described keyphrase selection by docu-ment, the approach can be applied to other units of text. Ourkeyphrase selection system has been used to select key-phrases for each page of a document. In these cases, thekeyphrases are selected within a document, and additionalfeatures and methods are used to reduce redundancy in theselected keyphrases. On the other hand, our keyphraseselection system could also be used to select keyphrases forlarger units, such as a subdirectory, although the usefulnesswould depend on the coherence of the documents in eachdirectory.

GENRE IDENTIFICATIONDocuments are often classified and searched for based onwords and topics. However, documents can also be classi-fied by another independent attribute: genre. Examplegenres in literature include poetry, fiction, and drama. Inenterprise search and browsing, a different set of documentgenres than those used to describe literature are needed.

Documents in the DocuBrowse repository are created usinga variety of tools that produce documents in different for-mats, including PDF, DOC, XLS, JPG, and PPT. In a sim-plistic approach to genre identification, the different docu-ment formats roughly correspond to different genres, and adocument filename extension could be a surrogate for docu-ment genre. However, a document creation tool is oftenused to create documents in more than one genre. In addi-tion, some extensions, such as PDF, are associated withmany genres, since files created in different formats areoften converted to PDF for its portable representation. Thus,using only file extensions would result in a user looking forslides to see PowerPoint files that are not slides. Further-more, the user will not see slides that are in other formats,such as PDF. Similarly, scanned document pages may beJPEG files, so that their genre is unknown.

In genre identification for DocuBrowse, the documents areautomatically categorized into a small number of genres,roughly corresponding to the genres usually associated withdocument format types: technical paper, slides, table, andphoto. Figure 7 shows examples of pages from each of thefour genres that our Genre Identification and Estimationsystem (GenIE) has been trained to identify. Note the varia-tion within a genre and that, while the text might not belarge enough to read, the genre of each page is readilyapparent.

The GenIE SystemIn the GenIE system, documents are classified based on fea-tures extracted from page images. Those images are eithergenerated by scanning paper documents or by renderingelectronic documents. Our approach tries to capture layoutfeatures without explicitly performing layout analysis. Inparticular, each page image is tiled and document-basedimage features are extracted to characterize each tile. Genreidentification is performed per page, and then documentgenre is estimated based on the genre estimates for thepages in the document.

Score kj( ) λii∑ fi kj d,( )=

We developed GenIE using a corpus of documents crawledfrom the web, some of them printed and scanned by us, andsome directly converted to JPEG. GenIE was trained toidentify the four genres of interest: technical papers, slides,photos, and tables.

We developed a set of features that capture local documentcharacteristics, such as lines of text or text size, within a tile.The tiles must be large enough to extract document charac-teristics within each tile and at the same time small enoughso that the different region types (e.g., heading, figure, bodytext) remain distinct. Empirically, we have found that divid-ing each page image into a grid of 5 tiles horizontally by 5tiles vertically, for a total of 25 tiles, meets our require-ments.

The following features are computed for each tile: (1) imagedensity [10], (2) horizontal pixel projection, (3) verticalpixel projection, and (4) color correlogram. Three page-based features are computed: (1) horizontal line lengths, (2)vertical line lengths, and (3) image size.

Each document may be tagged with zero or more genres. Tohandle tagging with multiple genres, a separate classifierwas trained for independently identifying each genre. Indeveloping the genre identification classifiers, a corpus ofdocuments composed of 5098 pages from 1081 documentswas labeled with the targeted genres with a total of 3670labels. Because of the competitive performance of supportvector machines (SVM) for many classification tasks, weused an SVM classifier, SVMlight [8]. A separate classifierusing a one-against-many model was trained for each genre.We reimplemented Kim and Ross’ algorithm [10] as a base-line for comparing performance of our GenIE system andobserved that GenIE performed noticeably better (mean pre-cision of 0.59 vs. 0.94 and F1 of 0.51 vs 0.84 for Kim andRoss vs. GenIE, respectively).

To tag the documents in the DocuBrowse corpus, the fourGenIE document genre identifiers were used to score eachpage of all the documents in the corpus. For the Docu-Browse task, the emphasis is on high recall, and our methodof combining the page scores for tagging documents bygenre bears this in mind. A document genre score, Sd(g), fordocument d being genre g is computed for each genre as the

average of the individual page scores for a document. Apage score is the averaged SVM score, clipped to a mini-mum value of 0.0 and a maximum value of 1.0.

where s(p,g,c) is the SVM score for page p being classifiedas genre g by classifier c and P is the total number of pagesin a document. We used two classifiers per genre whereeach classifier was trained on a separate data partition.

In addition to GenIE’s good performance in comparison to abaseline implementation, we also found that it provided amajor contribution in the context of DocuBrowse. Theoffered genres are intuitive and filter the document collec-tion in a useful fashion. For example, restricting the docu-ment hierarchy to just slides is very helpful for finding proj-ect presentations.

RELATED WORKThere has been much work in several areas related to ourwork presented here. In this section, we highlight how ourwork draws from and extends this earlier work, and how wetailor it to address the characteristics of enterprise search.

Document Browsers and Faceted SearchFacets have been presented and used in search and browsingsystems in a variety of ways, and the set of facets supportedvaries, depending on the contents of the document reposi-tory. A study by Wilson and schraefel [18] found that “a bal-ance of exploratory and keyword searches” was performedusing the mSpace faceted browser both during early use ofthe system and in later use, indicating that both browsingand search should be supported. This is a feature in both oursystem and the faceted search systems that we will contrastwith ours.

The mSpace faceted browser lays out categorical facet val-ues in columns which can be moved to indicate the priorityof filtering relationships. Our system also provides for user-ordered filtering by facet, but the ordering is determined bythe order that a user specifies facet values of interest.

Hearst [5] provides recommendations for the design andlayout of hierarchical, categorical facets in the Flamenco

Figure 7. Sample document pages from the genres of technical papers, photos, tables, and slides.

Sd g( )1

2P------- max min s p g c, ,( ) 0.0,( ) 1.0,( )

p c,∑=

system and also comments on the use of facets in the eBayExpress interface. In Flamenco, which was developed on acollection of fine art images, the result set is shown on theright half of the interface, with result items grouped by themost recently selected facet.

Microsoft’s FacetLens system [11] lays out facets and theirattribute values in rectangular regions, with the result setalso presented on the right side of the interface. FacetMap[16], a predecessor to FacetLens, was also developed atMicrosoft for personal information stores with rich meta-data. It uses facets for organizing dataset items and dynami-cally allocates screen space based on the distributions ofattributes among the search result set. FaThumb [9] was alsodeveloped at Microsoft and provides faceted mobile search.

In contrast with these systems, our system relies on thedirectory hierarchy to provide the grounding context forusers, and results are presented by filtering out parts of thedirectory hierarchy and by highlighting directories to indi-cate those that are good matches. In addition, the facets inour system are a mix of pre-specified categories and facetswith many values, such as a date range or range of file sizes.

These earlier systems were developed for a browsing taskwith a relatively homogenous collection of item types, whileour “enterprise” document collection contains a variety ofdocument types, such as technical papers and slides. TheUpLib system [7] is a “personal digital library system” thatalso contains a variety of document types, such as technicalpapers and slides, similar to our document corpus. InUpLib, metadata can be stored with each document. Docu-ments are accessed either by viewing all the documents inthe repository or by search over the text and metadata, suchas “authors” or “keywords” using the Lucene system. Thesearch-based approach is in contrast to the faceted systemsthat prompt the user to select a facet value and so reduce thecognitive load on the user.

DocuBrowse draws from the work of these earlier facetedsearch and browsing systems. Each of these earlier systemswas developed for collections with rich metadata that is asignificantly stronger organizational factor than the filedirectory hierarchy. For our task context of enterprise col-lections, the file hierarchy plays a significant role, and wedeveloped DocuBrowse to integrate faceted search andbrowsing with a file hierarchy. As with the other facetedsystems, facet selection narrows the repository items pre-sented. But unlike earlier systems, the presentation of docu-ments is integrated with the file hierarchy.

Enterprise-Oriented RecommendationsDocuBrowse also provides recommendations, unlike theearlier faceted search systems. Although recommendationsystems for different types of data have been developed, wehave not seen a recommendation system for enterprise col-lections. DocuBrowse uses enterprise-based subgroups togenerate different types of suggestions. Abecker et al. [1]present an information architecture designed to support theactive delivery of resources in an enterprise but do notexplore the availability of information necessary to supportthe active delivery process. Plu et al. [13] describe usingrecommender techniques to suggest contacts in a corporate

environment. More related to DocuBrowse, Zhen et al. [20]focus on fact-oriented recommendations, such as experi-ences with a particular vendor or with a specific product.

Keyphrase SelectionAlthough some documents contain keyphrases, many donot. The automatic selection of document keyphrases pro-vides for greater coverage in the use of keyphrases whenvisualizing documents. There are a number of ways to iden-tify keyphrases (e.g., [17]). A straight-forward method is bytagging the part-of-speech (POS) of the text and then identi-fying POS tag sequences that correspond to a noun phrase[17]. Another method is to identify sequences of wordsbetween “stop words,” or non-content words [3]. Morerecently, supervised methods which learn how to combinedifferent features have been successfully used. One suchsystem is the KEA system [19]. These systems require atraining set of documents labeled with keyphrases. Since wedo not have a labeled corpus and it would have been time-consuming to manually label a reasonable size corpus fortraining a system, we took an unsupervised approach thatproduces reasonable keyphrases.

Genre IdentificationThe data collections used by the earlier systems were rich inmetadata, where much of the metadata was manuallyentered. For enterprise collections, the work practice ofemployees generating documents usually does not includeentering metadata for each document. Additionally, whenmetadata is entered in an ad hoc manner by many people, itis often inconsistent. By automatically extracting metadata,such as genre, from a document collection, more consistentmetadata is available for use in faceted search. Althoughgenre identification systems have been developed, thegenres covered were not a good match to our enterprise col-lection and the performance was not good enough for use asfacets. Automatic genre identification based on text andmarkup features has been proposed for web search improve-ments [2]. Other systems have been developed for classify-ing page images into different genres.

Shin and Doermann [14] perform document layout analysisto label and identify the boundaries of different types ofdocument regions (e.g., text, image, graphics) and extractfeatures for use in a decision tree classifier. Their identified“genres” correspond to page types: cover page, reference,table of contents, and form pages.

Although layout analysis can be successfully performed forlimited domains, general layout analysis is still not robust.Two other image-based approaches to genre identificationthat do not require layout analysis have been developed byGupta and Sarkar [4] and by Kim and Ross [10]. Gupta andSarkar identified salient feature points and performed classi-fication based on the points’ locations and local image char-acteristics. However, their genre classifier was tested on dis-criminating between only two types of genres: journalarticles and memos.

Kim and Ross [10] developed an image-based genre classi-fier for the first page of a document. They divide the pageinto a uniform grid of 62 by 62 tiles, count the number ofnon-white pixels in each tile to compute black pixel density,

and use the tile densities as classifier features. They com-pared the performance of the count feature using NaïveBayes, Random Forest, and support vector machine (SVM)classification methods. Their best-performing image-basedgenre classifier performance was Naïve Bayes, but the per-formance was relatively poor and is meant to be used inconjunction with a text-based genre classifier.

FUTURE WORKOur system has been in internal use for several months toprovide access to historical research documents of our labo-ratory. We have anecdotal evidence that our approach forcombining searching and browsing helps in locating docu-ments that would be missed in a pure search. For example,we located slides depicting Japanese visitors from a previ-ous visit ten years ago. We do not have a sufficient historyof interactions with this set of research documents to fullyevaluate our algorithms and interfaces for recommendingdocuments. Furthermore, our laboratory is small and has aflat organization, so we could not test recommendationsbased on organization. We are currently exploring ways todeploy DocuBrowse in a larger organization and are deter-mining requirements for additional security measures andtools.

CONCLUSIONSWe presented a new approach for searching and browsingenterprise document collections that combines faceted fil-tering and search with navigation of the document collec-tion structure. These techniques are part of a web-based sys-tem for accessing document collections. Unlike traditionaldocument search systems, our system presents searchresults within the user-created document hierarchy by onlyshowing matching documents and directories and highlight-ing promising areas. To support scanned-in documents andto better classify electronic documents, we utilize an auto-matic genre identifier that can determine genres such aspapers, slides, tables, and photos. We also automaticallydetermine keyphrases that provide users with a quick over-view of the document content. We look at access histories,job roles, and document similarities to recommend addi-tional documents that may be useful to the user. We expectthis novel approach to simplify access to enterprise docu-ment collections.

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