1132 results about "Click-through rate" patented technology

A search engine includes a decision component that determines whether documents that are returned in response to a user search query are likely to be very relevant to the search query. Links that refer to documents that the search engine determines to likely be very relevant may be displayed with visual cues that assist the user in browsing the links. The decision component may base its decision on a number of parameters, including: (1) the position of the document in a ranked list of search results, (2) the click through rate of the document, (3) relevance scores for the document and other documents that are returned as hits in response to the search query, and (4) whether the document is classified as a pornographic document (the search engine may refrain from showing visual cues for potentially pornographic documents).

Systems and methods for determining the value of bids placed by content providers for placement positions on a page, e.g., a web page, rendered according to a given context, for instance, the search results listing for a particular query initiated on a search engine web site, are provided. Additionally, systems and methods are provided for determining placement of content items, e.g., advertisements and / or images, on a rendered page relative to other content items on the page based upon bid value.

Various techniques are disclosed for facilitating on-line contextual analysis and / or advertising operations implemented in a computer network. According to some embodiments, various aspects may be used for enabling advertisers to provide contextual advertising promotions to end-users based upon real-time analysis of web page content which may be served to an end-user's computer system. In at least one embodiment, the information obtained from the real-time analysis may be used to select, in real-time, contextually relevant information, advertisements, and / or other content which may then be displayed to the end-user, for example, via real-time insertion of textual markup objects and / or dynamic content. According to specific embodiments, various operations may be performed for adapting or modifying a conventional context-based advertising systems to improve various features such as, for example, ad relevance estimation, click-through rate estimation, advertisement selection and layout, balancing exploration and exploitation, etc.

If some aspect of serving or scoring an ad is subject to a performance (e.g., click-through rate, etc.) threshold, such a threshold may be adjusted using a confidence factor of the ad targeting used. For example, ads served pursuant to a more relaxed notion of match might have to meet a higher performance threshold (e.g., than the threshold applied to ads served pursuant to a stricter notion of match). Alternatively, or in addition, ads served pursuant to a stricter notion of match might be subject to a lower performance threshold (e.g., than the threshold applied to ads served pursuant to a more relaxed notion of match. Thus, in general, a performance threshold could increase as match confidence decreases, and / or a performance threshold could decrease as match confidence increases.

Systems and methods for detecting instances of click fraud are disclosed. Click fraud occurs when, for example, a user, malware, bot, or the like, clicks on a pay per click advertisement (e.g., hyperlink), a paid search listing, or the like without a good faith interest in the underlying subject of the hyperlink. Such fraudulent clicks can be expensive for an advertising sponsor. Statistical information, such as ratios of unpaid clicks to pay per clicks, are extracted from an event database. The statistical information of global data is used as a reference data set to compare to similar statistical information for a local data set under analysis. In one embodiment, when the statistical data sets match relatively well, no click fraud is determined to have occurred, and when the statistical data sets do not match relatively well, click fraud is determined to have occurred.

Advertisements over a network, such as the Internet, by extracting an index file of suppliers of search result sets for a plurality of keywords and / or an index of ads to be placed. A keyword in a received query may be matched to the suppliers of related search result sets and / or the ads to be placed in the index file(s). The search results may be ranked based on various criteria such as yield, click through rates and history of dealings. Search result sets for the keywords from the matching suppliers and the ads to be placed may be combined to produce a search result set ranked in accordance with estimated yields for advertisements associated with each result in the search result set and delivered in response to the received query. The search results in the combined set may be reranked and / or repriced.

The invention discloses a method and device for predicting advertisement click rate based on user behaviors. The method comprises the following steps of: behavior directional prediction: training a behavior directional model by analyzing the user behaviors, and predicting the click rates of the user on different types of advertisements to obtain a predicted value of the click rate; in-type sorting: sorting the advertisements of each type according to the current behavior type of the user to obtain an in-type advertisement sorting list; and comprehensive sorting: sorting the click rates of all advertisements according to the predicted value of click rate and the in-type advertisement sorting list to obtain a comprehensive sorting list. Through the method disclosed by the invention, a prediction model can be established for each type of advertisements so as to sort the advertisements more accurately according to the type characteristics of the advertisements.

Various techniques are disclosed for facilitating on-line contextual analysis and / or advertising operations implemented in a computer network. According to some embodiments, various aspects may be used for enabling advertisers to provide contextual advertising promotions to end-users based upon real-time analysis of web page content which may be served to an end-user's computer system. In at least one embodiment, the information obtained from the real-time analysis may be used to select, in real-time, contextually relevant information, advertisements, and / or other content which may then be displayed to the end-user, for example, via real-time insertion of textual markup objects and / or dynamic content. According to specific embodiments, various operations may be performed for adapting or modifying a conventional context-based advertising systems to improve various features such as, for example, ad relevance estimation, click-through rate estimation, advertisement selection and layout, balancing exploration and exploitation, etc.

The present invention relates generally to an individualized web-based browser interface and associated software for accessing at least one web-based search engine with the ability to wrapper and present some or all of the search results obtained from the at least one search engine in a presentation format or order that is based upon the profile of a particular end user for a computer system where the browser is installed. The invention further relates to a method for improved web-based advertising by validating advertising click-throughs of web page thumbnails or advertisements that are presented to an individual having a particular profile with respect to an advertiser's metrics in place of, or in addition to, presenting such a thumbnail from the advertiser to anyone who structures a search having certain terms or meta data criteria corresponding to a specific group of corresponding web pages.

In one embodiment, a method of optimizing an advertising campaign on a computer network includes the steps of delivering an advertisement to a client computer over a computer network, measuring an efficacy of the advertisement to generate a result, and changing a characteristic of the advertisement based on the result. The characteristic of the advertisement may be an aesthetic feature or a processing trigger, for example. The aesthetic feature may include the presentation vehicle used to display the advertisement, the artwork of the advertisement, and so on. The processing trigger may include rules on when to display the advertisement. The efficacy of the advertisement may be measured by determining its conversion rate or click-through rate, for example.

Various techniques are disclosed for facilitating on-line contextual analysis and / or advertising operations implemented in a computer network. According to some embodiments, various aspects may be used for enabling advertisers to provide contextual advertising promotions to end-users based upon real-time analysis of web page content which may be served to an end-user's computer system. In at least one embodiment, the information obtained from the real-time analysis may be used to select, in real-time, contextually relevant information, advertisements, and / or other content which may then be displayed to the end-user, for example, via real-time insertion of textual markup objects and / or dynamic content. According to specific embodiments, various operations may be performed for adapting or modifying a conventional context-based advertising systems to improve various features such as, for example, ad relevance estimation, click-through rate estimation, advertisement selection and layout, balancing exploration and exploitation, etc.

The invention belongs to internet massage processing field, characterized in that: common inquiry set which is cared by user, according to one ore more search engine logs, is screened out firstly using the inquiry of corresponding information of user number; then calculating user touching ratio corresponding to user touching page of inquiry in common inquiry set, if multi-search engine logs is used, user touching ratio are united; user touching pages are effectively screened out based on user touching ratio, the ralated inquiry and corresponding result page address are stored related database; at last, the result from user information and the result by searching by search engine are united effectively when user puts in the demand of inquiry, and are reordered to return to user. The method has the advantage that computer is completed automatically and efficiently submits search engine performance in real time and objectively.

A computer implemented method comprises analyzing a plurality of attributes of a sample of online documents using a boosted decision tree and generating a model from it. The model is used to predict a click through rate (CTR) of an additional online document based on the analyzing. The predicted CTR is output to a display device, storage medium or network.

The invention relates to the technical field of information, in particular to a method for recommending information. The method comprises the steps that the feature information, corresponding to an access request, of a current user is extracted when the access request is received, and whether the current user is a new user or not is determined according to the feature information; when the current user is the new user, and hot spots are recommended to the current user according to the historical access record of the current user and the click rate rank; when the current user is an old user, the pre-stored user information and item information are clustered to generate a user cluster; in the user cluster, a first recommending list based on item contents is generated, and a second recommending list based on user-item network collaborative filtering is generated; a mixed recommending list is generated according to the first recommending list and the second recommending list; the information is recommended to the current user according to the mixed recommending list. The real-time performance, accuracy and relevance of the recommending information formed through the information recommending method are improved, and information can be effectively recommended for the user.

An internet marketing analytics system to quantify visitor website activity, the system including a database having a plurality of parameters having any portion of the following; site visits, total revenue, bounce rate, conversion rate, orders, average order value, value per visit, pages per visit, new visits, units, cart additions, cart removals, internal search, click through rate, revenue per visit, open rate, email list, impressions, visit duration, percent new visitors, percent return visitors, post volume, net promoter score, social referring traffic, total cost, search term, exit rate, page views, and product type, plus non website related visitor internet activity. The system producing a multi-variate visual spatial display of the database including at least three parameters being simultaneously displayed and modifying the display to selectively emphasize a parameter to be displayed as an X, Y, or Z axis for clarity, for modifying a component of the database to optimize website efficiency.

Embodiment of the present invention relate to algorithms for computing the causal effect of position in search engine advertising listings on outcomes such as click-through rates and sales orders.

A method of targeted online advertising provides to a user advertisements that meet the user preferences. The method stores user information of users, organize the users into user layers, identifies the stored user information of a visiting user based on a user identifier, and identify a target user layer associated with the visiting user. The method then determines a targeted advertisement type for the visiting user based on the favorite advertisement type of the target user layer and the user information of the current visiting user, and accordingly selects a targeted advertisement to be presented to the visiting user. The user information of the visiting user and the related user layer(s) are updated with the new user information including the records of the user's visit activities. The method provides targeted ads to users, and improves the click rates and the efficiency of the online advertisements.

The invention discloses an article information recommending method and device. The method comprises the steps of obtaining attribute information and user behavior data of an access user when an article access request is received; obtaining a corresponding candidate article set; determining articles satisfying preset conditions in the candidate article set based on a similarity matrix, the attribute information and the user behavior data, wherein the similarity matrix is used for indicating the similarities among the candidate articles, and the similarities between the candidate articles and the attribute information; and recommending the information of the articles satisfying the preset conditions to the access user. Through adoption of the similarity matrix, the attribute information of the access user and recent different click and consumption behaviors to the articles, intention prediction is carried out on user access; and therefore, the articles suitable for the user are determined and recommended to the user. Compared with the mode of carrying out recommendation through prediction of the click-through-rate scores of the user to the articles based on a linear model, the method and the device have the advantages of improving individuation of the recommendation results and improving the accuracy of the recommendation results.

Methods and apparatus for a recommendation system based on collaborative filtering is provided. Explicit and implicit ratings of items by network users are used to create a contextual model. The explicit ratings comprise different rating types regarding different item attributes. The implicit ratings comprise different rating types derived from different user events and may include recency, intensity, or frequency ratings. The contextual model may be optimized for a specific objective function, such as click-through-rate or conversion rate. In other embodiments, item information is used to produce a content model where item information for an item is encoded as metadata into a document that represents the item. The contextual or content model is used to recommend one or more items to a current user. The basic unit of the recommendation system may be an item set of two or more items or a particular sequence of two or more items.

If some aspect of serving or scoring an ad is subject to a performance (e.g., click-through rate, etc.) threshold, such a threshold may be adjusted using a confidence factor of the ad targeting used. For example, ads served pursuant to a more relaxed notion of match might have to meet a higher performance threshold (e.g., than the threshold applied to ads served pursuant to a stricter notion of match). Alternatively, or in addition, ads served pursuant to a stricter notion of match might be subject to a lower performance threshold (e.g., than the threshold applied to ads served pursuant to a more relaxed notion of match). Thus, in general, a performance threshold could increase as match confidence decreases, and / or a performance threshold could decrease as match confidence increases.

The present invention provides a solution for predicting an advertisement click-through rate. The solution comprises: acquiring characteristic related information of multiple characteristic types related to multiple past delivered advertisements in a predetermined past time period; performing cross combination on characteristic related information of at least two characteristic types of each past delivered advertisement, to determine multiple cross characteristic sets, and calculating to determine cross characteristic identifiers separately corresponding to the multiple cross characteristic sets; extracting an advertisement display quantity and an advertisement click quantity corresponding to each cross characteristic set, and calculating to determine an advertisement click-through rate corresponding to each cross characteristic set, so as to use the advertisement click-through rate as a cross characteristic value; performing training on a logistic regression model based on the cross characteristic identifiers and the cross characteristic values separately corresponding to the multiple cross characteristic sets, and calculating to determine a model training parameter; and performing prediction calculation on an advertisement click-through rate of a to-be-predicted advertisement based on the model training parameter. According to the solution, more reliable training data is provided for prediction calculation of an advertisement, so that accuracy of a prediction calculation result of an advertisement click-through rate is ensured.

A system and method for achieving optimal advertising is disclosed. In Internet advertising embodiments, small quantities of experimental advertising banner designs are tested to extract valuable information from the experiments. One or more embodiments can also incorporate array mathematics to help select and analyze the ad design elements that improve the results (e.g., click-thru-rate, revenue-per-impression, etc.) of the overall advertising campaigns. Embodiments of the present invention can also utilize a process of identifying influential design elements, selecting and testing banners representative of such design elements, obtaining feedback, and analyzing it to extract information from the experiments about which design elements are most important and which combination of design elements lead to the best overall banner. By providing substantive results via fewer test banner designs, the present invention decreases the costs associated with running advertising campaigns and otherwise improves the efficiency and success rates of an advertising provider.

In a web page advertising method with a learning mechanism, a plurality of advertisement files are transformed to vector equations in advance. Content of a web page file that is being displayed is analyzed and transformed to a vector equation in real time. An advertisement search conditional set is determined according to the analyzed web page file. The advertisement files are subjected to a similarity analysis using vector computations so as to select at least one advertisement file that has a high correlation to the overall concept of the content of the web page. Adjustment and training with the advertisement file are conducted through analyzing actions of a user, thereby increasing considerably the click rate of the advertisement file and achieving full utilization of advertising resources. An advertising agent employing the web page advertising method purchases advertising spaces from a medium and bills advertisers on a pay-per-click basis.

The invention provides systems and methods that can be used for targeted advertising. The system determines where to present impressions, such as advertisements, to maximize an expected utility subject to one or more constraints, which can include quotas and minimum utilities for groups of one or more impression. The traditional measure of utility in web-based advertising is click-though rates, but the present invention provides a broader definition of utility, including measures of sales, profits, or brand awareness, for example. This broader definition permits advertisements to be allocated more in accordance with the actual interests of advertisers.