Patent Publication Number: US-7711735-B2

Title: User segment suggestion for online advertising

Description:
BACKGROUND 
   In the context of online advertising, behavioral targeting refers to the ability, through the use of anonymous data, to deliver advertisements to consumers based on their recent behavior. In this kind of advertisement model, advertisers review and buy a user segment to which they deliver their advertisements. For example, a video game advertiser may be interested in a user segment corresponding to users that have queried for “Xbox games” and/or have visited www.xbox.com, and so forth. 
   More formally, a user segment in a behavioral targeting system is defined as some user conditions met relative to some attributes, wherein each attribute represents the behavior of a set of users, such as the queries they have searched, or some set of URLs they have visited. There are several types of attributes; e.g., the URL pattern attribute represents the URLs the users visit, the query attribute represents the queries the users search in the search engine, the URL domain attribute represents the URL domain the users visit, and the product and service attribute represents the product and/or the service the users consume. 
   Attribute expansion tools have been developed to help advertisers define reasonably good user segments. With these expansion tools, advertisers can reach more users by expanding the set of users that meet certain attributes. The core functionality of these tools is to expand a set of attributes according to the original attributes provided by the advertiser. 
   The simplest way to do such an expansion is to write a separate algorithm for each special type of attribute expansion, e.g. an algorithm for expanding the query attribute to the URL pattern attribute. However, if there are N types of attributes, this technique requires writing N*N different expansion algorithms to meet the different expansion requirements. 
   SUMMARY 
   This Summary is provided to introduce a selection of representative concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used in any way that would limit the scope of the claimed subject matter. 
   Briefly, various aspects of the subject matter described herein are directed towards a technology in a behavioral targeting environment by which an original attribute is expanded into an expanded attribute set, including by aggregating users that meet the original attribute into a mid-result, and using the mid-result to determine user similarity relative to each of a plurality of candidate attribute types. The candidate attribute types are sorted by their determined user similarity into sorted candidate attributes, with a subset (e.g., the topmost similar N) of the sorted candidate attributes selected for the expanded attribute. 
   Another aspect described is a technology in a behavioral targeting environment that suggests URL and/or URL patterns from a set of users and information on URLs visited by the set of users. The URL and/or URL patterns are computed by separating at least some URLs into a data structure, such as nodes of a URL tree. The number of users in the user set who have visited the URL corresponding to the node is calculated, along with the number of users in the set of users who have visited the URL on a sub-tree whose root is the node. The user set intersection between an input user set and a user set on each node is determined, and URL similarity and/or URL pattern similarity is computed and output in decreasing order of similarity to determine the URL and/or URL patterns. 
   Another aspect described is a technology directed towards suggesting domains from a set of users and domains visited by the set of users. The suggested domains may be computed from a set of users comprises by determining similarity between pairs of domains, which may be determined offline with respect to an online behavioral targeting system. More particularly, a similarity between each pair of domains may be computed using a Pearson coefficient function, with an output for a given domain provided in decreasing order of similarity with respect to each other domain. 
   Other advantages may become apparent from the following detailed description when taken in conjunction with the drawings. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The present invention is illustrated by way of example and not limited in the accompanying figures in which like reference numerals indicate similar elements and in which: 
       FIG. 1  is a block diagram representing a uniform attribute expansion mechanism that provides expanded attributes in a behavioral targeting system. 
       FIG. 2  is a flow diagram representing example steps taken by the uniform attribute expansion mechanism. 
       FIG. 3  is a representation of an expanded attribute provided by the uniform attribute expansion mechanism from an original attribute. 
       FIG. 4  is a block diagram representing a URL pattern suggestion mechanism that outputs URL and URL patterns for a set of users in a behavioral targeting system. 
       FIG. 5  is a flow diagram representing example steps taken by the URL pattern suggestion mechanism. 
       FIG. 6  is a representation of a URL tree corresponding to users&#39; visit logs as processed by the URL pattern suggestion mechanism. 
       FIG. 7  is a block diagram representing a domain suggestion mechanism that outputs domains based on computed similarity in a behavioral targeting system. 
       FIG. 8  is a flow diagram representing example steps taken by the domain suggestion mechanism. 
       FIG. 9  shows an illustrative example of a general-purpose network computing environment into which various aspects of the present invention may be incorporated. 
   

   DETAILED DESCRIPTION 
   Various aspects of the technology described herein are generally directed towards improved behavioral targeting in the context of online advertising, including by providing a uniformed attribute expansion mechanism (algorithm) that simplifies the expansion between various attribute types. Further, unlike other types of attributes, there is provided a way to suggest the URL patterns and/or domains for a set of users. 
   As will be understood, various examples are shown that facilitate a uniformed attribute expansion mechanism and suggestion of a user segment. However, these are only non-limiting examples for the purposes of describing the various concepts and aspects of the technology described herein. As such, the present invention is not limited to any particular embodiments, aspects, concepts, protocols, formats, structures, functionalities or examples described herein. Rather, any of the embodiments, aspects, concepts, protocols, formats, structures, functionalities or examples described herein are non-limiting, and the present invention may be used various ways that provide benefits and advantages in computing and online advertising technology in general. 
   Turning to  FIG. 1 , there is shown a uniform attribute expansion mechanism  102 A and  102 B (e.g., implemented in the form of an algorithm) that simplifies the expansion between various attribute types. The input and the output of the algorithm are the original attribute  104  and the expanded attribute  106 , respectively. The two attributes ordinarily have the same topic, and the expanded attribute can reach more users than the original attribute. 
   In one example implementation, the unified attribute expansion mechanism  102 A and  102 B generally includes a first example component  102 A directed towards reaching a user set. In this component, the mechanism aggregates the users  110  who have met the original attribute into an aggregated user set  112 . This aggregated user set  112  is the core target, to whom the advertiser wants to deliver this advertisement. Because the uniform expansion mechanism  102 A and  102 B supports different types of attribute expansion, the general concepts are similar to other aggregate approaches that reach a set of users. 
   In the example of  FIG. 1 , to expand with a new attribute, a set of candidate attributes  114  is defined and evaluated, in which each candidate attributes relates in some way to the users in the user set  112 . For each candidate attribute in the set  114 , a similarity value is computed in a second example component of the uniform attribute expansion mechanism  102 B. For example, the user similarity value for each candidate attribute may be computed by u′/U, where U is the size of the user set of users belonging to that candidate attribute, and u′ is the size of the user set belonging to both the candidate attribute and the original attribute. Candidate attributes are then sorted by user similarity, with some number N of the top (most similar) results chosen as the expanded attribute or attributes for the expanded attribute set  106 . 
     FIG. 2  summarizes the steps of the uniform attribute expansion mechanism components  102 A and  102 B. Step  202  represents the aggregation step as described above. Step  204  then selects a candidate attribute, with step  206  computing the similarity value for that candidate attribute and associating it with that candidate attribute. 
   Step  208  repeats the computation for other candidate attributes until all have been processed. Then, the similarity values associated with their respective candidates are used to sort the candidate attributes (step  210 ). The top N are then chosen as the expanded attribute or attributes in the set  106  (step  212 ). 
     FIG. 3  is a representation of how each original attribute may be expanded to reach more users. As can be readily appreciated, leveraging the aggregated user set  112  as a mid-result of the attribute expansion unifies the expansion algorithm between various attribute types. Because the aggregation is already implemented (or is straightforward to implement) for different types of attributes, the unified attribute expansion only needs to implement N algorithms (where N is the chosen number of the attribute types) of the user set to expand the attributes for different type attributes. 
   Turning to another aspect, there is described a way to suggest URLs and URL patterns for a set of users in a behavioral targeting system. Note that compared with other types of attributes, there was heretofore not any known way to suggest URL patterns from a given set of users. 
   To suggest a URL pattern, as represented in  FIG. 4 , a URL pattern suggestion mechanism (e.g., an algorithm)  440  is provided that constructs a URL tree  442 , whereby URLs and URL patterns can be suggested at the same time. The URL pattern suggestion mechanism  440  takes as inputs a set of users  442  and the URL visit log set  444  from the set of users. As described below, the mechanism  440  analyzes the log set  444  and outputs the URL and URL patterns  445  according to the user set in similarity decreasing order. 
   To this end, steps  502 ,  504   506  and  508  of the example flow diagram of  FIG. 5  represent the building of a log tree  446 . More particularly, for each URL as selected at step  502 , at step  504  the URL is split into n levels, each level corresponding to a node in the tree  446 . For example, given a URL such as http://update.microsoft.com/microsoftupdate/v6/default.aspx?ln=en-us, the URL may be split it into four levels, corresponding to http://a/b/c/d, each added into the tree  446  via step  506 . Step  508  repeats the process for other URLs in the visit log  444 . 
     FIG. 6  represents a simple example tree  660  which includes the nodes corresponding to http://a/b/c/d. In this example, Node a represents “update.microsoft.com”; node b represents “microsoftupdate”; node c represents “v6” and node d represents “default.aspx?ln=en-us”. In the URL tree  660 , the node d represents the full URL, with nodes a, b and c being the ancestor nodes of node d. Note that adding each URL in the URL visit log  444  into the tree, as represented by step  506  (as repeated via step  508 ), provides the entire URL tree  660 . 
   On each tree node, there are two user sets. For each node of URL tree (as processed via steps  512  and  518 ), step  514  calculates U, the number users in this user set who have visited the URL on the node, and TU, the number users in this user set who have visited the URL on the sub-tree whose root is the node. For example, if user X visits http://update.microsoft.com/microsoftupdate/v6/default.aspx?ln=en-us, then user X is counted in the U value for node d, and user X is also in the TU value for nodes a, b, c, d. As can be seen, for any leaf node (e.g., the node d) in the URL tree  550 , U=TU; for any non-leaf node on the URL tree  660 , U is a subset of TU, that is, U ⊂ TU. 
   Note that because the entire URL tree may be extremely large, it may take a relatively long time to calculate Ux and TUx for every node. However, most of the nodes are irrelevant to the user set, and thus a user-to-node table  448  ( FIG. 4 , in which arranging the users in either the columns or rows is equivalent) may be used, e.g.: 
   
     
       
         
             
             
             
             
             
           
             
                 
                 
             
             
                 
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               User 1 
               User 2 
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   Step  516  represents calculating the user set intersection between the input user set and the user set on each node. For example, if the input user set is U input , for each node on the URL tree, the intersection is as follows:
 
U X =U input ∩U
 
TU X =U input ∩TU
 
   For each user (steps  522  and  528 ), steps  524  and  526  represent calculating and outputting the suggested URL and URL patterns in decreasing order of similarity, where for a user X:
 
Similarity URL   =|Ux|/|U| 
 
Similarity pattern   =|TUx|/|TU| 
 
   In addition to URL suggestion and URL pattern suggestion, an example URL domain suggestion mechanism  770  (algorithm) may be employed, as generally represented in  FIGS. 7 and 8 . Note that in general, the URL domain attribute has limited value relative to other types of attributes (e.g., because the number of the URL domains visited is far less than the number of URL patterns or queries), whereby in one implementation, the results are calculated offline, which helps reduce the CPU load of the online system and shortens the response time. As will be understood, this mechanism further promotes the suggestion precision in a behavioral targeting system. 
   To rank the candidates in one example implementation, a Pearson coefficient function is used by the URL domain suggestion mechanism  770 , which is of higher precision than other ranking functions (such as described above). As described above, an online part of the example behavioral targeting system uses a unified expansion algorithm between various attribute types, which uses a user set as a mid-result and ranks the candidates by the ratio of users. Because the Pearson coefficient function considers not only the users, but also the average of the visits, it ordinarily has higher precision. 
   As represented in  FIG. 7 , the inputs are the users&#39; URL visit log  772  and URL domains  774 . The mechanism  770  analyzes the log  772  and output the similarities between each pair of URL domains. 
   As represented in the flow diagram of  FIG. 8 , step  800  transfers the users&#39; URL visit log  772  to a table/matrix  776  (in which arranging the users in either the columns or rows is equivalent). The value in each cell is the visit count of the corresponding domain and user. For example, in the example table below, the value in the cell [0,0] is “3”, which means that in the log, the User 0 visited the Domain 0 three times. 
   
     
       
         
             
             
             
             
             
           
             
                 
                 
             
             
                 
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   Steps  802  and  804  represent selecting a pair of domains, one to process by comparing with each other domain. Step  806  calculates the Pearson coefficient similarities for the selected pair of URL domains according to the following formula: 
   
     
       
         
           
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   As can be seen, the above formula calculates the coefficient similarity of two URL domains. R u bar (  R u   ) is the average value of domain u, and R u ′bar (  R u′   ) is the average value of domain u′. 
   Step  808  repeats the comparison for other domains to compare with to the selected domain that is being processed. When the comparisons for the selected domain are finished, step  810  represents outputting the set of suggested domains in decreasing order of Pearson coefficient similarity. Thus, for a given domain Di, for example, the above formula provides the Pearson coefficient similarities between Di and each other domain, which are then output as suggested domains in decreasing order of Pearson coefficient similarity. Step  812  repeats the processing for each other domain. 
   Exemplary Operating Environment 
     FIG. 9  illustrates an example of a suitable computing system environment  900  on which the examples represented in  FIGS. 1-8  may be implemented. The computing system environment  900  is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the invention. Neither should the computing environment  900  be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment  900 . 
   The invention is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known computing systems, environments, and/or configurations that may be suitable for use with the invention include, but are not limited to: personal computers, server computers, hand-held or laptop devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. 
   The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and so forth, which perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in local and/or remote computer storage media including memory storage devices. 
   With reference to  FIG. 9 , an exemplary system for implementing various aspects of the invention may include a general purpose computing device in the form of a computer  910 . Components of the computer  910  may include, but are not limited to, a processing unit  920 , a system memory  930 , and a system bus  921  that couples various system components including the system memory to the processing unit  920 . The system bus  921  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus. 
   The computer  910  typically includes a variety of computer-readable media. Computer-readable media can be any available media that can be accessed by the computer  910  and includes both volatile and nonvolatile media, and removable and non-removable media. By way of example, and not limitation, computer-readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by the computer  910 . Communication media typically embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of the any of the above should also be included within the scope of computer-readable media. 
   The system memory  930  includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM)  931  and random access memory (RAM)  932 . A basic input/output system  933  (BIOS), containing the basic routines that help to transfer information between elements within computer  910 , such as during start-up, is typically stored in ROM  931 . RAM  932  typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit  920 . By way of example, and not limitation,  FIG. 9  illustrates operating system  934 , application programs  935 , other program modules  936  and program data  937 . 
   The computer  910  may also include other removable/non-removable, volatile/nonvolatile computer storage media. By way of example only,  FIG. 9  illustrates a hard disk drive  941  that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive  951  that reads from or writes to a removable, nonvolatile magnetic disk  952 , and an optical disk drive  955  that reads from or writes to a removable, nonvolatile optical disk  956  such as a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive  941  is typically connected to the system bus  921  through a non-removable memory interface such as interface  940 , and magnetic disk drive  951  and optical disk drive  955  are typically connected to the system bus  921  by a removable memory interface, such as interface  950 . 
   The drives and their associated computer storage media, described above and illustrated in  FIG. 9 , provide storage of computer-readable instructions, data structures, program modules and other data for the computer  910 . In  FIG. 9 , for example, hard disk drive  941  is illustrated as storing operating system  944 , application programs  945 , other program modules  946  and program data  947 . Note that these components can either be the same as or different from operating system  934 , application programs  935 , other program modules  936 , and program data  937 . Operating system  944 , application programs  945 , other program modules  946 , and program data  947  are given different numbers herein to illustrate that, at a minimum, they are different copies. A user may enter commands and information into the computer  910  through input devices such as a tablet, or electronic digitizer,  964 , a microphone  963 , a keyboard  962  and pointing device  961 , commonly referred to as mouse, trackball or touch pad. Other input devices not shown in  FIG. 9  may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  920  through a user input interface  960  that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). A monitor  991  or other type of display device is also connected to the system bus  921  via an interface, such as a video interface  990 . The monitor  991  may also be integrated with a touch-screen panel or the like. Note that the monitor and/or touch screen panel can be physically coupled to a housing in which the computing device  910  is incorporated, such as in a tablet-type personal computer. In addition, computers such as the computing device  910  may also include other peripheral output devices such as speakers  995  and printer  996 , which may be connected through an output peripheral interface  994  or the like. 
   The computer  910  may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer  980 . The remote computer  980  may be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer  910 , although only a memory storage device  981  has been illustrated in  FIG. 9 . The logical connections depicted in  FIG. 9  include one or more local area networks (LAN)  971  and one or more wide area networks (WAN)  973 , but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. 
   When used in a LAN networking environment, the computer  910  is connected to the LAN  971  through a network interface or adapter  970 . When used in a WAN networking environment, the computer  910  typically includes a modem  972  or other means for establishing communications over the WAN  973 , such as the Internet. The modem  972 , which may be internal or external, may be connected to the system bus  921  via the user input interface  960  or other appropriate mechanism. A wireless networking component  974  such as comprising an interface and antenna may be coupled through a suitable device such as an access point or peer computer to a WAN or LAN. In a networked environment, program modules depicted relative to the computer  910 , or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,  FIG. 9  illustrates remote application programs  985  as residing on memory device  981 . It may be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used. 
   An auxiliary subsystem  999  (e.g., for auxiliary display of content) may be connected via the user interface  960  to allow data such as program content, system status and event notifications to be provided to the user, even if the main portions of the computer system are in a low power state. The auxiliary subsystem  999  may be connected to the modem  972  and/or network interface  970  to allow communication between these systems while the main processing unit  920  is in a low power state. 
   CONCLUSION 
   While the invention is susceptible to various modifications and alternative constructions, certain illustrated embodiments thereof are shown in the drawings and have been described above in detail. It should be understood, however, that there is no intention to limit the invention to the specific forms disclosed, but on the contrary, the intention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention.