Chen, Xingchun

Eskridge, Kent M

Li, Jun

Nowick, Elaine Maytag

Travnicek, Daryl A

English

DigitalCommons@University of Nebraska

University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Library Philosophy and Practice (e-journal) Libraries at University of Nebraska-Lincoln 7-14-2005 A Model Search Engine Based on Cluster Analysis of User Search Terms Elaine Maytag Nowick University of Nebraska-Lincoln, enowick@unl.edu Kent M. Eskridge University of Nebraska--Lincoln, keskridge1@unl.edu Daryl A. Travnicek University of Nebraska--Lincoln Xingchun Chen University of Nebraska--Lincoln Jun Li University of Nebraska--Lincoln Follow this and additional works at: https://digitalcommons.unl.edu/libphilprac  Part of the Library and Information Science Commons Nowick, Elaine Maytag; Eskridge, Kent M.; Travnicek, Daryl A.; Chen, Xingchun; and Li, Jun, "A Model Search Engine Based on Cluster Analysis of User Search Terms" (2005). Library Philosophy and Practice (e-journal). 45. https://digitalcommons.unl.edu/libphilprac/45 “A Model Search Engine Based on Cluster Analysis of User Search Terms,”  Elaine A. Nowick et al. Library Philosophy and Practice Vol. 7, No. 2 (Spring 2005) 1Library Philosophy and Practice Vol. 7, No. 2 (Spring 2005)  (libr.unl.edu:2000/LPP/lppv7n2.htm) ISSN 1522-0222  A Model Search Engine Based on Cluster Analysis of User Search Terms  Elaine A. Nowick Associate Professor, Research and Instructional Services University of Nebraska--Lincoln Libraries Lincoln, Nebraska 68588-4100  Kent M. Eskridge Professor  Daryl A. Travnicek Computer Specialist  Department of Statistics University of Nebraska-Lincoln  Xingchun Chen Senior Software Development Engineer  Jun Li Graduate Student/Student Programmer  Communications and Information Technology University of Nebraska-Lincoln    Introduction  Methods of searching for information in electronic collections of documents or records fall into two classes: keyword matching or browsing though a collection arranged in a subject classification scheme. Classification of print documents has traditionally provided users with both intellectual and physical access via shelving of documents in call number order with call numbers corresponding to subject headings. The physical location of the document and the subject classification were inextricably linked together. While the purpose of Library of Congress (LC) Subject Headings is to provide user access to a collection, the subject headings are based on documents rather than on the users' terminology. Studies of controlled vocabularies have indicated that they work well when there is an accepted common terminology describing concepts in the subject area and when users are familiar with the terminology (Voorbij, 1998). Solomon (1991) states, "Classification schemes fail too often because they are not grounded in the language and knowledge of users or in the task or situation of use." With the advent of “A Model Search Engine Based on Cluster Analysis of User Search Terms,”  Elaine A. Nowick et al. Library Philosophy and Practice Vol. 7, No. 2 (Spring 2005) 2electronic information and the Internet, the physical location of the material is of much less importance. This has triggered a reexamination of classification schemes with a greater emphasis placed on intellectual access. With classification freed from the need to shelve one document in one location, subject hierarchies can be made more flexible and there is a greater possibility of customizing classification schemes to fit specific groups of users with particular needs. However, cataloging of information resources is labor intensive. As a practical matter, customized classification of documents will have to be at least partially automated.  One possible technique for examining the user's view of information space with the goal of producing flexible semi-automatic classifications is Cluster Analysis, a statistical technique used for identifying patterns and associations in complex data. Cluster analysis was originally used in analyzing taxonomic data and in creating phylogenetic trees. However cluster analysis has since been used in a wide range of applications from medical image analysis to market research. Cluster analysis based on documents in a collection has been explored as a means of automatic classification (Willett, 1988; McCaffrey,1991).   There has been less research in cluster analysis using user terminology rather than document keywords. Until log files of web sites were made available it was difficult to accumulate enough exact user searches to make a cluster analysis feasible. Another limitation in using searcher terms is that most users of the Internet employ short (one to two word) queries (Jansen et al., 1998). Wu, et al. (2001) used queries as a basis for clustering documents selected by searchers in response to similar queries. This paper reports on an experimental search engine based on a cluster analysis of user free-text for water quality information.  Methods  User queries were collected from a log file analysis of the University of Nebraska(UNL) Agricultural Network Information Center (AgNIC) Water Quality web site, from AgDB, a precursor of the AgNIC system at the National Agricultural Library (NAL), and from users reference questions submitted to the AgNIC web site. A total of 495 queries that included more than one concept or term were included in the cluster analysis. Four distance measures between words x and y, suitable for use with sparse data were calculated: Dice (1- 2A/(2A+B+C)), Kulczynski (1-A/(B+C)), Jaccard (1-A/(A+B+C)), and Ochiai (1- A/SQRT ((A+B)*(A+C)) where A= No.of observations with both words x and y present, B = No. of observations with word x present and word y absent, C= No.of observations with word x absent and word y present (Habalek, 1982). Comparisons were made between clusters produced with terms included in 10 or more queries and clusters produced with terms included in 5 or more queries. Truncated words were included together and some synonymous or similar words were grouped together. For example, names of specific cities were all included in "cityname". Both subject and non-subject query terms were included and some phrases, such as “Best Management Practices” were included rather than separated out as individual words. Clusters were created using the SAS Average Linkage Hierarchical Cluster Procedure (SAS Institute, Inc., 1990).  A consensus cluster map was made using the common elements from the clusters produced by the four methods and a cut-off point of ten clusters. A search program was “A Model Search Engine Based on Cluster Analysis of User Search Terms,”  Elaine A. Nowick et al. Library Philosophy and Practice Vol. 7, No. 2 (Spring 2005) 3incorporated into the Water Quality web site which utilizes the word associations from the consensus cluster map to suggest terms for broadening or narrowing a search.  Results  Word frequency  In this study, the position of terms within the hierarchical trees depended both on the frequencies of words included in the calculations and also on the distance measure.  Clusters were calculated for word frequencies of 1, 5, and 10. When terms with a frequency of one or more were included in the calculation, a “chaining” effect was observed. The results showed one very large cluster that included all the terms in the study. When only terms used in five or more searches were considered, there was less chaining and the clusters produced were more logical. When terms used ten or more times were included, the least amount of chaining was produced and the clusters were quite focused on specific subjects. However, considerable information on less frequently made searches was lost. For this reason, it was decided to incorporate terms used by five or more searchers in the analysis.  Distance measures   While clusters varied somewhat depending on the distance measure used in the calculations there were “core” terms that fell into the same cluster regardless of the distance measure used. At the lower levels of the hierarchical tree, the group membership of the terms tended to vary more. Since there are no commonly accepted statistical tests that would point out the best clusters based on mathematical criteria and there were no clear-cut differences in the logical term associations produced, a consensus cluster map was produced incorporating results from all four distance measures (Figure 1.). Terms enclosed by thick lines were found together in the same cluster for all four distance measures. Terms in thinner lines moved between clusters depending on the distance measure used. The consensus map better reflects the “fuzzy” nature of the clusters produced.  “A Model Search Engine Based on Cluster Analysis of User Search Terms,”  Elaine A. Nowick et al. Library Philosophy and Practice Vol. 7, No. 2 (Spring 2005) 4  Figure 1 Consensus cluster map.  Search program  The search engine comes into play when the user has a “failed” search, which is defined as having more than 100 or fewer than one hit. For example, a keyword search for “water” would produce more than 100 hits and the user would be offered a suggestion to try the advanced smart search (Figure 2.).   “A Model Search Engine Based on Cluster Analysis of User Search Terms,”  Elaine A. Nowick et al. Library Philosophy and Practice Vol. 7, No. 2 (Spring 2005) 5Figure 2. Results of “failed” search.  The advanced search link will take users to a screen with suggested terms to narrow the search (Figure 3.).    Figure 3. Suggested additional terms based on cluster map  “A Model Search Engine Based on Cluster Analysis of User Search Terms,”  Elaine A. Nowick et al. Library Philosophy and Practice Vol. 7, No. 2 (Spring 2005) 6When the user clicks on additional terms and then the search button, an “and” search is performed and the results returned. The additional terms are those that fell into the same cluster as the original term. If the searcher narrows the terms too much, the list of suggested terms will be expanded to include terms farther away in the cluster map. Color highlighting is used to distinguish the group of terms that are in the same core cluster as the original term from the group of terms that are in the “fuzzy” area (Figure 4).   Figure 4. Additional suggested search terms for a too narrow search.  If the searcher’s terms are too narrow and there are no results returned an “or” search is performed when the advanced smart search heading is selected.   Discussion  Traditional library cataloging and indexing has been centered on the information source, primarily books and articles. Online catalogs and search engine programming have allowed keyword searching to extend beyond the subject headings, titles, and author fields for searching, but were still centered on the document with the user having to try to fit their search needs, often poorly formulated, to the vocabulary of the author and cataloger or indexer. Programming advances now allow us to collect actual user terminology. Cluster analysis is a technique that enables the researcher to create a picture of the collective users’ view of the information space.   The results form this study, serve as a proof of concept and a model of how a user-centric and dynamic classification scheme could work for the method to be effective as a search tool for users it would need to be greatly expanded. While the cluster calculations used in this study “A Model Search Engine Based on Cluster Analysis of User Search Terms,”  Elaine A. Nowick et al. Library Philosophy and Practice Vol. 7, No. 2 (Spring 2005) 7produced slightly different classifications, the same is true for human generated subject hierarchies. As more searches are accumulated and terms added to the database the classification should become more stable. However, one of the strengths of this methodology is its flexibility. Clusters generated through a semiautomatic procedure can be more responsive to changes in search topics of users depending on current issues or scientific breakthroughs and also to changes in vocabulary usage.   A logical extension of the project would be to add controlled vocabulary terms and keywords from keywords indentified from the documents to the cluster analysis. Thus the cluster analysis could provide a logical and semiautomatic link between the user search terms and indexing terminology.   Conclusions  The clusters produced in this study provided intriguing patterns of meaning and hints for more user-friendly organization. However, it is apparent that there is a need for human intervention. Judgments are required on which words to include in stop lists and on whether to include phrases or its separated words in the analysis.   It is possible that query terms from web sites with heavier traffic would produce a more stable clustering pattern. However, a clustering technique that deals better with “fuzzy clusters” may be more appropriate. Future research plans are to explore non-hierarchical and probabilistic clustering methods such as k-means clustering.  References  Hubalek, Z. (1982). Coefficients of association and similarity, based on binary (presence-absence) data: An evaluation. Biological Review 57 Pp. 669-689.  Jansen, B. J., Spink, A., Bateman, J., Saracevic, T. (1998). Real Life Information Retrieval: A Study of User Queries on the Web. SIGIR Forum 32.1 Pp. 5-17.  McCaffrey, A. (1991). Applied cladistics: New methodologies for information classification research. Proceedings of the 2nd ASIS SIG/CR Classification Research Workshop. Washington DC, Oct 27, 1991. Pp.85-100.  SAS Institute, Inc. (1990). SAS/STAT User’s Guide, Version 6, Fourth edition, Vol. 1, Cary, NC: SAS Institute Inc. 943 pp.  Solomon, P. (1991). Use-based methods for classification development. Proceedings of the 2nd ASIS SIG/CR Classification Research Workshop. Washington, DC.  Voorbij, H. J. (1998). Title Keywords and Subject Descriptors: A Comparison of Subject Search Entries of Books in the Humanities and Social Sciences. Journal of Documentation 54 (April). Pp. 466-476.  “A Model Search Engine Based on Cluster Analysis of User Search Terms,”  Elaine A. Nowick et al. Library Philosophy and Practice Vol. 7, No. 2 (Spring 2005) 8Willett, P. (1988). Recent trends in hierarchical document clustering: A critical review. Information Processing & Management 24(5):577-597.  Wu, Y. H., Chen, Y. C., Chen, A. L. P. (2001). Enabling personalized recommendation on the web based on user interests and behaviors. Proceedings, Eleventh International Workshop on Research Issues in Data Engineering. Pp. 17 –24.   

A Model Search Engine Based on Cluster Analysis of User Search Terms

UNL | Libraries

University of Nebraska - Lincoln 
DigitalCommons@University of Nebraska - Lincoln 
Library Philosophy and Practice (e-journal) Libraries at University of Nebraska-Lincoln 
7-14-2005 
A Model Search Engine Based on Cluster Analysis of User Search 
Terms 
Elaine Maytag Nowick 
University of Nebraska-Lincoln, enowick@unl.edu 
Kent M. Eskridge 
University of Nebraska--Lincoln, keskridge1@unl.edu 
Daryl A. Travnicek 
University of Nebraska--Lincoln 
Xingchun Chen 
University of Nebraska--Lincoln 
Jun Li 
University of Nebraska--Lincoln 
Follow this and additional works at: https://digitalcommons.unl.edu/libphilprac 
 Part of the Library and Information Science Commons 
Nowick, Elaine Maytag; Eskridge, Kent M.; Travnicek, Daryl A.; Chen, Xingchun; and Li, Jun, "A Model 
Search Engine Based on Cluster Analysis of User Search Terms" (2005). Library Philosophy and Practice 
(e-journal). 45. 
https://digitalcommons.unl.edu/libphilprac/45 
“A Model Search Engine Based on Cluster Analysis of User Search Terms,”  Elaine A. Nowick et al. Library Philosophy and Practice Vol. 
7, No. 2 (Spring 2005) 1
Library Philosophy and Practice Vol. 7, No. 2 (Spring 2005)  
(libr.unl.edu:2000/LPP/lppv7n2.htm) 
ISSN 1522-0222 
 
A Model Search Engine Based on Cluster Analysis of User Search Terms 
 
Elaine A. Nowick 
Associate Professor, Research and Instructional Services 
University of Nebraska--Lincoln Libraries 
Lincoln, Nebraska 68588-4100 
 
Kent M. Eskridge 
Professor 
 
Daryl A. Travnicek 
Computer Specialist 
 
Department of Statistics 
University of Nebraska-Lincoln 
 
Xingchun Chen 
Senior Software Development Engineer 
 
Jun Li 
Graduate Student/Student Programmer 
 
Communications and Information Technology 
University of Nebraska-Lincoln 
 
 
 
Introduction 
 
Methods of searching for information in electronic collections of documents or records 
fall into two classes: keyword matching or browsing though a collection arranged in a subject 
classification scheme. Classification of print documents has traditionally provided users with 
both intellectual and physical access via shelving of documents in call number order with call 
numbers corresponding to subject headings. The physical location of the document and the 
subject classification were inextricably linked together. While the purpose of Library of 
Congress (LC) Subject Headings is to provide user access to a collection, the subject headings 
are based on documents rather than on the users' terminology. Studies of controlled vocabularies 
have indicated that they work well when there is an accepted common terminology describing 
concepts in the subject area and when users are familiar with the terminology (Voorbij, 1998). 
Solomon (1991) states, "Classification schemes fail too often because they are not grounded in 
the language and knowledge of users or in the task or situation of use." With the advent of 
“A Model Search Engine Based on Cluster Analysis of User Search Terms,”  Elaine A. Nowick et al. Library Philosophy and Practice Vol. 
7, No. 2 (Spring 2005) 2
electronic information and the Internet, the physical location of the material is of much less 
importance. This has triggered a reexamination of classification schemes with a greater emphasis 
placed on intellectual access. With classification freed from the need to shelve one document in 
one location, subject hierarchies can be made more flexible and there is a greater possibility of 
customizing classification schemes to fit specific groups of users with particular needs. However, 
cataloging of information resources is labor intensive. As a practical matter, customized 
classification of documents will have to be at least partially automated. 
 
One possible technique for examining the user's view of information space with the goal 
of producing flexible semi-automatic classifications is Cluster Analysis, a statistical technique 
used for identifying patterns and associations in complex data. Cluster analysis was originally 
used in analyzing taxonomic data and in creating phylogenetic trees. However cluster analysis 
has since been used in a wide range of applications from medical image analysis to market 
research. Cluster analysis based on documents in a collection has been explored as a means of 
automatic classification (Willett, 1988; McCaffrey,1991).  
 
There has been less research in cluster analysis using user terminology rather than 
document keywords. Until log files of web sites were made available it was difficult to 
accumulate enough exact user searches to make a cluster analysis feasible. Another limitation in 
using searcher terms is that most users of the Internet employ short (one to two word) queries 
(Jansen et al., 1998). Wu, et al. (2001) used queries as a basis for clustering documents selected 
by searchers in response to similar queries. This paper reports on an experimental search engine 
based on a cluster analysis of user free-text for water quality information. 
 
Methods 
 
User queries were collected from a log file analysis of the University of Nebraska(UNL) 
Agricultural Network Information Center (AgNIC) Water Quality web site, from AgDB, a 
precursor of the AgNIC system at the National Agricultural Library (NAL), and from users 
reference questions submitted to the AgNIC web site. A total of 495 queries that included more 
than one concept or term were included in the cluster analysis. Four distance measures between 
words x and y, suitable for use with sparse data were calculated: Dice (1- 2A/(2A+B+C)), 
Kulczynski (1-A/(B+C)), Jaccard (1-A/(A+B+C)), and Ochiai (1- A/SQRT ((A+B)*(A+C)) 
where A= No.of observations with both words x and y present, B = No. of observations with 
word x present and word y absent, C= No.of observations with word x absent and word y present 
(Habalek, 1982). Comparisons were made between clusters produced with terms included in 10 
or more queries and clusters produced with terms included in 5 or more queries. Truncated words 
were included together and some synonymous or similar words were grouped together. For 
example, names of specific cities were all included in "cityname". Both subject and non-subject 
query terms were included and some phrases, such as “Best Management Practices” were 
included rather than separated out as individual words. Clusters were created using the SAS 
Average Linkage Hierarchical Cluster Procedure (SAS Institute, Inc., 1990). 
 
A consensus cluster map was made using the common elements from the clusters 
produced by the four methods and a cut-off point of ten clusters. A search program was 
“A Model Search Engine Based on Cluster Analysis of User Search Terms,”  Elaine A. Nowick et al. Library Philosophy and Practice Vol. 
7, No. 2 (Spring 2005) 3
incorporated into the Water Quality web site which utilizes the word associations from the 
consensus cluster map to suggest terms for broadening or narrowing a search. 
 
Results 
 
Word frequency 
 
In this study, the position of terms within the hierarchical trees depended both on the 
frequencies of words included in the calculations and also on the distance measure.  
Clusters were calculated for word frequencies of 1, 5, and 10. When terms with a frequency of 
one or more were included in the calculation, a “chaining” effect was observed. The results 
showed one very large cluster that included all the terms in the study. When only terms used in 
five or more searches were considered, there was less chaining and the clusters produced were 
more logical. When terms used ten or more times were included, the least amount of chaining 
was produced and the clusters were quite focused on specific subjects. However, considerable 
information on less frequently made searches was lost. For this reason, it was decided to 
incorporate terms used by five or more searchers in the analysis. 
 
Distance measures  
 
While clusters varied somewhat depending on the distance measure used in the 
calculations there were “core” terms that fell into the same cluster regardless of the distance 
measure used. At the lower levels of the hierarchical tree, the group membership of the terms 
tended to vary more. Since there are no commonly accepted statistical tests that would point out 
the best clusters based on mathematical criteria and there were no clear-cut differences in the 
logical term associations produced, a consensus cluster map was produced incorporating results 
from all four distance measures (Figure 1.). Terms enclosed by thick lines were found together in 
the same cluster for all four distance measures. Terms in thinner lines moved between clusters 
depending on the distance measure used. The consensus map better reflects the “fuzzy” nature of 
the clusters produced. 
 
“A Model Search Engine Based on Cluster Analysis of User Search Terms,”  Elaine A. Nowick et al. Library Philosophy and Practice Vol. 
7, No. 2 (Spring 2005) 4
 
 
Figure 1 Consensus cluster map. 
 
Search program 
 
The search engine comes into play when the user has a “failed” search, which is defined 
as having more than 100 or fewer than one hit. For example, a keyword search for “water” would 
produce more than 100 hits and the user would be offered a suggestion to try the advanced smart 
search (Figure 2.). 
 
 
“A Model Search Engine Based on Cluster Analysis of User Search Terms,”  Elaine A. Nowick et al. Library Philosophy and Practice Vol. 
7, No. 2 (Spring 2005) 5
Figure 
2. Results of “failed” search. 
 
The advanced search link will take users to a screen with suggested terms to narrow the 
search (Figure 3.).  
 
 
Figure 3. Suggested additional terms based on cluster map 
 
“A Model Search Engine Based on Cluster Analysis of User Search Terms,”  Elaine A. Nowick et al. Library Philosophy and Practice Vol. 
7, No. 2 (Spring 2005) 6
When the user clicks on additional terms and then the search button, an “and” search is 
performed and the results returned. The additional terms are those that fell into the same cluster 
as the original term. If the searcher narrows the terms too much, the list of suggested terms will 
be expanded to include terms farther away in the cluster map. Color highlighting is used to 
distinguish the group of terms that are in the same core cluster as the original term from the 
group of terms that are in the “fuzzy” area (Figure 4).  
 
Figure 
4. Additional suggested search terms for a too narrow search. 
 
If the searcher’s terms are too narrow and there are no results returned an “or” search is 
performed when the advanced smart search heading is selected.  
 
Discussion 
 
Traditional library cataloging and indexing has been centered on the information source, 
primarily books and articles. Online catalogs and search engine programming have allowed 
keyword searching to extend beyond the subject headings, titles, and author fields for searching, 
but were still centered on the document with the user having to try to fit their search needs, often 
poorly formulated, to the vocabulary of the author and cataloger or indexer. Programming 
advances now allow us to collect actual user terminology. Cluster analysis is a technique that 
enables the researcher to create a picture of the collective users’ view of the information space.  
 
The results form this study, serve as a proof of concept and a model of how a user-centric 
and dynamic classification scheme could work for the method to be effective as a search tool for 
users it would need to be greatly expanded. While the cluster calculations used in this study 
“A Model Search Engine Based on Cluster Analysis of User Search Terms,”  Elaine A. Nowick et al. Library Philosophy and Practice Vol. 
7, No. 2 (Spring 2005) 7
produced slightly different classifications, the same is true for human generated subject 
hierarchies. As more searches are accumulated and terms added to the database the classification 
should become more stable. However, one of the strengths of this methodology is its flexibility. 
Clusters generated through a semiautomatic procedure can be more responsive to changes in 
search topics of users depending on current issues or scientific breakthroughs and also to changes 
in vocabulary usage.  
 
A logical extension of the project would be to add controlled vocabulary terms and 
keywords from keywords indentified from the documents to the cluster analysis. Thus the cluster 
analysis could provide a logical and semiautomatic link between the user search terms and 
indexing terminology.  
 
Conclusions 
 
The clusters produced in this study provided intriguing patterns of meaning and hints for 
more user-friendly organization. However, it is apparent that there is a need for human 
intervention. Judgments are required on which words to include in stop lists and on whether to 
include phrases or its separated words in the analysis.  
 
It is possible that query terms from web sites with heavier traffic would produce a more 
stable clustering pattern. However, a clustering technique that deals better with “fuzzy clusters” 
may be more appropriate. Future research plans are to explore non-hierarchical and probabilistic 
clustering methods such as k-means clustering. 
 
References 
 
Hubalek, Z. (1982). Coefficients of association and similarity, based on binary (presence-
absence) data: An evaluation. Biological Review 57 Pp. 669-689. 
 
Jansen, B. J., Spink, A., Bateman, J., Saracevic, T. (1998). Real Life Information Retrieval: A 
Study of User Queries on the Web. SIGIR Forum 32.1 Pp. 5-17. 
 
McCaffrey, A. (1991). Applied cladistics: New methodologies for information classification 
research. Proceedings of the 2nd ASIS SIG/CR Classification Research Workshop. Washington 
DC, Oct 27, 1991. Pp.85-100. 
 
SAS Institute, Inc. (1990). SAS/STAT User’s Guide, Version 6, Fourth edition, Vol. 1, Cary, NC: 
SAS Institute Inc. 943 pp. 
 
Solomon, P. (1991). Use-based methods for classification development. Proceedings of the 2nd 
ASIS SIG/CR Classification Research Workshop. Washington, DC. 
 
Voorbij, H. J. (1998). Title Keywords and Subject Descriptors: A Comparison of Subject Search 
Entries of Books in the Humanities and Social Sciences. Journal of Documentation 54 (April). 
Pp. 466-476. 
 
“A Model Search Engine Based on Cluster Analysis of User Search Terms,”  Elaine A. Nowick et al. Library Philosophy and Practice Vol. 
7, No. 2 (Spring 2005) 8
Willett, P. (1988). Recent trends in hierarchical document clustering: A critical review. 
Information Processing & Management 24(5):577-597. 
 
Wu, Y. H., Chen, Y. C., Chen, A. L. P. (2001). Enabling personalized recommendation on the 
web based on user interests and behaviors. Proceedings, Eleventh International Workshop on 
Research Issues in Data Engineering. Pp. 17 –24.  
 


https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1044&amp;context=libphilprac

A Model Search Engine Based on Cluster Analysis of User Search Terms

Abstract

Similar works

Full text

Available Versions

DigitalCommons@University of Nebraska

UNL | Libraries