A9.com, Inc.

Amazon Technologies, Inc.

Colin M Saunders

Christopher Laurence Scofield

Elmore Eugene Pope

Brad Eugene Marshall

Jiong Wang

Colin D Bleckner

Jonathan Alan Leblang

Ruben Ernesto Ortega

A method for displaying a search history for a user is shown. The method includes generating a search history for a user including one or more listings of search results, displaying a first search result listing on a web page, and displaying at least a second search result listing on the web page, the second search result listing having a relationship to the first search result listing within the search history.

Search history presentations

A facility for ranking in a result set is described. The facility establishes a system of ratings for combinations of a web page with the query term that reflect the level of relevance of the web page to the query term. In response to each of a number of content interactions performed by users, each with respect to a result corresponding to a web page included in a result set that was generated in response to a query comprising a set of one or more query terms, the facility adjusts a rating score for each combination of the web page and a query term among the set of query terms. For each of a number of web pages included as results in a distinguished result set produced in response to a distinguished query comprising a set of distinguished query terms, the facility (1) selects established and adjusted reading scores for each combination of the web page and a query term among the distinguished set of query terms, and (2) determines a ranking score for the result in the distinguished result set by combining the selected reading scores.

Identifying the items most relevant to a current query based on user activity with respect to the results of similar queries

System and method for presenting a search history

Users who visit a network site typically visit other sites in the same session. The surfing pattern or 'trail' through the pages may be significant once aggregated, because it outlines the underlining relationship between those pages or sites. Once such user navigational trail data is available, the relationship between network sites on such user trails may used to make various decisions regarding advertising and keyword management. Additionally, the relative popularity of each trail may be used to rank or compute relative weights for the various user trails. Such information may then aid in keyword suggestion and advertising decisions.

Method and system for keyword selection based on proximity in network trails

An advertising system automatically generates suggested ad campaign parameters for a business based on information obtained automatically from one or more sources, such as a corresponding business directory listing, a web site of the business, and/or a user account record associated with the business. The suggested ad campaign parameters, which may include suggested keyword strings for ad targeting, are presented to a user associated with the business via a partially or wholly pre-populated electronic form that can be submitted to create an active ad campaign for the business. In one embodiment, the pre-populated form is presented in response to the user selecting an ad solicitation object displayed on a web page. The ad solicitation object may be displayed in the format of an actual ad, and may include suggested ad text for use in the ad campaign.

Automated generation of suggested online advertising campaign parameters

Users that visit a network site typically visit other sites in the same session. The surfing pattern or 'trail' through the sites is significant because it may outline the underlining relationship between those sites. Once such data is available, the relationship between sites on the same user trails may be used to determine the placement of advertisements for sites by publishers. Additionally, the popularity or volume of traffic that navigates along a particular user trail may be used to affect the ranking/ordering of advertisements placed on a certain publisher site. An advertising broker may determine advertising pricing in part based on navigational path or user trail information. Thus, placing advertising on sites that are closer in proximity to the advertised site may cost more than placing advertising on sites not as close in proximity to the advertised site.

Growing community of inventors

Menlo Park, CA, United States of America

Colin M Saunders