Abstract: Systems and methods of classifying sessions are disclosed. A particular method monitors user activity at one or more servers accessible via a network and capturing event entries in an activity log for user activity that is detected. The event entries include descriptive information regarding a user action, a client identifier and a session identifier. The method also includes attempting to classify sessions associated with a plurality of event entries of the activity log as legitimate use or illegitimate use of resources of the one or more servers. The method further includes identifying unclassified sessions. The method also includes determining a count of a number of unclassified sessions associated with a particular client identifier and determining a total number of sessions associated with the particular client identifier. The method further includes classifying the unclassified sessions as legitimate use or illegitimate use of the resources of the one or more servers.

Abstract: Systems and methods of classifying sessions are disclosed. A particular method monitors user activity at one or more servers accessible via a network and capturing event entries in an activity log for user activity that is detected. The event entries include descriptive information regarding a user action, a client identifier and a session identifier. The method also includes attempting to classify sessions associated with a plurality of event entries of the activity log as legitimate use or illegitimate use of resources of the one or more servers. The method further includes identifying unclassified sessions. The method also includes determining a count of a number of unclassified sessions associated with a particular client identifier and determining a total number of sessions associated with the particular client identifier. The method further includes classifying the unclassified sessions as legitimate use or illegitimate use of the resources of the one or more servers.

Abstract: Systems and methods of classifying sessions are disclosed. A particular method monitors user activity at one or more servers accessible via a network and capturing event entries in an activity log for user activity that is detected. The event entries include descriptive information regarding a user action, a client identifier and a session identifier. The method also includes attempting to classify sessions associated with a plurality of event entries of the activity log as legitimate use or illegitimate use of resources of the one or more servers. The method further includes identifying unclassified sessions. The method also includes determining a count of a number of unclassified sessions associated with a particular client identifier and determining a total number of sessions associated with the particular client identifier. The method further includes classifying the unclassified sessions as legitimate use or illegitimate use of the resources of the one or more servers.

Abstract: Systems and methods described and suggested herein utilize various techniques to assess the degree of specificity of search queries. By assessing the degree of specificity of search queries, the systems and methods may be used to determine where the submitting users' intentions fall along the continuum between specific and general searching. Thus, the systems and methods may be used to, among other uses, deliver search results that more closely coincide with the users' intentions. For example, if data associated with a search query indicates that users typically submit the search query with the intention of specific searching, embodiments may tailor search results to include a limited number of highly relevant search-result items. On the other hand, for example, if data associated with a search query indicates that users typically submit the search query with the intention of general searching, embodiments may tailor search results to include a diverse set of search-result items from multiple categories.

Abstract: Systems and methods of classifying sessions are disclosed. A particular method monitors user activity at one or more servers accessible via a network and capturing event entries in an activity log for user activity that is detected. The event entries include descriptive information regarding a user action, a client identifier and a session identifier. The method also includes attempting to classify sessions associated with a plurality of event entries of the activity log as legitimate use or illegitimate use of resources of the one or more servers. The method further includes identifying unclassified sessions. The method also includes determining a count of a number of unclassified sessions associated with a particular client identifier and determining a total number of sessions associated with the particular client identifier. The method further includes classifying the unclassified sessions as legitimate use or illegitimate use of the resources of the one or more servers.

Abstract: Systems and methods described and suggested herein utilize various techniques to assess the degree of specificity of search queries. By assessing the degree of specificity of search queries, the systems and methods may be used to determine where the submitting users' intentions fall along the continuum between specific and general searching. Thus, the systems and methods may be used to, among other uses, deliver search results that more closely coincide with the users' intentions. For example, if data associated with a search query indicates that users typically submit the search query with the intention of specific searching, embodiments may tailor search results to include a limited number of highly relevant search-result items. On the other hand, for example, if data associated with a search query indicates that users typically submit the search query with the intention of general searching, embodiments may tailor search results to include a diverse set of search-result items from multiple categories.

Abstract: Systems and methods use behavioral data to assess listings for items. The behavior data may relate to information regarding user actions in connection with listings for the items. The listings may be collections of information about the items. Behavioral data in connection with a collection of listings is used to assess how changing listings affects one or more performance measures and/or to score listing quality. The assessment may be used for various purposes, such as identifying listings that may be changed to achieve one or more improved performance measures for corresponding items and for other applications.