Search (3 results, page 1 of 1)

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Abstract

This article reports on a model and patterns of use of a library catalog that can be accessed through the Internet. Three categories of users are identified. individuals who perform a search of the catalog, tourists who look only at opening pages of the library catalog's site, and Web spiders that come to the site to obtain pages for indexing the Web. A number of types of use activities are also identified, and can be grouped with the presearch phase (which takes place before any searching begins): the search phase, the display phase (in which users display the results of their search), and phases in which users make errors, ask the system for help or assistance, and take other actions. An empirical investigation of patterns of use of a university Web-based library catalog was conducted for 479 days. During that period, the characteristics of about 2.5 million sessions were recorded and analyzed, and usage trends were identified. Of the total, 62% of the sessions were for users who performed a search, 27% were from spiders, and 11% were for tourists. During the study period, the average search session lasted about 5 minutes when the study began and had increased to about 10 minutes 16 months later. An average search consisted of about 1.5 presearch actions lasting about 25 seconds, about 5.3 display actions, and 2.5 searches per session. The latter two categories are in the range of 35-37 seconds per session each. There were major differences in usage (number of searches, search time, number of display actions, and display time), depending upon the database accessed

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a

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Abstract

Different users of a Web-based information system will have different goals and different ways of performing their work. This article explores the possibility that we can automatically detect usage patterns without demographic information about the individuals. First, a set of 47 variables was defined that can be used to characterize a user session. The values of these variables were computed for approximately 257,000 sessions. Second, principal component analysis was employed to reduce the dimensions of the original data set. Third, a twostage, hybrid clustering method was proposed to categorize sessions into groups. Finally, an external criteriabased test of cluster validity was performed to verify the validity of the resulting usage groups (clusters). The proposed methodology was demonstrated and tested for validity using two independent samples of user sessions drawn from the transaction logs of the University of California's MELVYL® on-line library catalog system (www.melvyl.ucop.edu). The results indicate that there were six distinct categories of use in the MELVYL system: knowledgeable and sophisticated use, unsophisticated use, highly interactive use with good search performance, known-item searching, help-intensive searching, and relatively unsuccessful use. Their characteristics were interpreted and compared qualitatively. The analysis shows that each group had distinct patterns of use of the system, which justifies the methodology employed in this study

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a

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Abstract

Users move from one state (or task) to another in an information system's labyrinth as they try to accomplish their work, and the amount of time they spend in each state varies. This article uses continuous-time stochastic models, mainly based on semi-Markov chains, to derive user state transition patterns (both in rates and in probabilities) in a Web-based information system. The methodology was demonstrated with 126,925 search sessions drawn from the transaction logs of the University of California's MELVYL® library catalog system (www.melvyLucop.edu). First, user sessions were categorized into six groups based on their similar use of the system. Second, by using a three-layer hierarchical taxonomy of the system Web pages, user sessions in each usage group were transformed into a sequence of states. All the usage groups but one have third-order sequential dependency in state transitions. The sole exception has fourth-order sequential dependency. The transition rates as well as transition probabilities of the semi-Markov model provide a background for interpreting user behavior probabilistically, at various levels of detail. Finally, the differences in derived usage patterns between usage groups were tested statistically. The test results showed that different groups have distinct patterns of system use. Knowledge of the extent of sequential dependency is beneficial because it allows one to predict a user's next move in a search space based on the past moves that have been made. It can also be used to help customize the design of the user interface to the system to facilitate interaction. The group CL6 labeled "knowledgeable and sophisticated usage" and the group CL7 labeled "unsophisticated usage" both had third-order sequential dependency and had the same most-frequently occurring search pattern: screen display, record display, screen display, and record display. The group CL8 called "highly interactive use with good search results" had fourth-order sequential dependency, and its most frequently occurring pattern was the same as CL6 and CL7 with one more screen display action added. The group CL13, called "known-item searching" had third-order sequential dependency, and its most frequently occurring pattern was index access, search with retrievals, screen display, and record display. Group CL14 called "help intensive searching," and CL18 called "relatively unsuccessful" both had thirdorder sequential dependency, and for both groups the most frequently occurring pattern was index access, search without retrievals, index access, and again, search without retrievals.

Type

a