Abstract:
Described is an internet user clustering technology, such as useful in behavioral targeting, in which users are clustered together based on MinHash computations that produce signatures corresponding to users&#39; internet-related activities. In one aspect, users are clustered together based on commonality of signatures between each set of signatures associated with each user. The signature sets and/or clusters may be associated with timestamps, whereby clusters may be determined for a given discrete time window or set of discrete time windows. To facilitate efficient processing, existing, prior signature sets of a user may be incrementally updated (e.g., daily), and/or the MinHash computations for users are partitioned among parallel computing machines. The timestamps may be used to selectively determine a cluster within a continuous time, a time window or set of time windows.

Description:
BACKGROUND 
       [0001]    Given information about Internet users, such as what search terms they have entered, behavioral targeting is often performed, such as to send advertisements tailored to specific groups of users. Web personalization also is based on user-specific information, as are other technologies. 
         [0002]    As there are too many different users to treat each one individually, users are clustered according to similarities found from such information. In user clustering, users are classically represented by their previous activities such as their search queries or clicked URLs. However, it is a challenge task to cluster millions of users, due to the high complexity of classical clustering algorithms. 
         [0003]    Such applications are also interested in temporal clustering, such as to cluster users based on their activities in the last month. However, known temporal clustering techniques (e.g., based upon streaming data) are not adequate in that they are inefficient and inflexible, and fail to be able to cluster users in a discrete time window with any specified length. For example, streaming data techniques are unable to cluster a large number of users according to their activities on every weekend of last month, or some other discrete other time window. 
       SUMMARY 
       [0004]    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. 
         [0005]    Briefly, various aspects of the subject matter described herein are directed towards a technology by which users are clustered together based on MinHash computations that produce signatures corresponding to users&#39; internet-related activities. In one aspect, users are clustered together on the basis of having similar signature sets, e.g., based on commonality of signatures therein. The signature sets and/or clusters may be associated with timestamps or the like, whereby clusters may be determined for a given discrete time window or set of discrete time windows. 
         [0006]    In one aspect, the signature set of one user is determined by performing the MinHash computations for a user&#39;s activities relative to a number of (e.g., twenty to thirty) permutations of combined internet-related data for a plurality of (e.g., all) users. To facilitate efficient processing, existing, prior signature sets of a user are incrementally updated as each new signature set is computed (e.g., daily). To further facilitate efficient processing, the MinHash computations for users are partitioned among parallel computing machines. 
         [0007]    In one aspect, the timestamps may be used to selectively determine a cluster based on a continuous time, a time window or set of time windows. For example, an advertiser can determine which users were clustered together on the past ten weekends (had similar signature sets on Saturdays and Sundays only). 
         [0008]    Other advantages may become apparent from the following detailed description when taken in conjunction with the drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0009]    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: 
           [0010]      FIG. 1  is a block diagram showing example components for user clustering via a parallel MinHash clustering algorithm. 
           [0011]      FIG. 2  is a flow diagram showing example steps taken to perform user clustering and merging. 
           [0012]      FIG. 3  shows an illustrative example of a computing environment into which various aspects of the present invention may be incorporated. 
       
    
    
     DETAILED DESCRIPTION 
       [0013]    Various aspects of the technology described herein are generally directed towards efficiently clustering a large number of users/objects in a discrete or continuous time window. In one aspect, this is accomplished by parallel computation using a MinHash clustering algorithm with an efficient time stamp merging module. As will be understood, such clustering technology provides significant benefits in behavioral targeting, social network mining, personalization research as well as related applications. 
         [0014]    It is understood that any of the examples described herein are only examples. As such, the present invention is not limited to any particular embodiments, aspects, concepts, structures, functionalities or examples described herein. Rather, any of the embodiments, aspects, concepts, 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 data processing in general. 
         [0015]      FIG. 1  shows various aspects related to user clustering, based in part on the classical MinHash clustering algorithm. In general, each user is represented by his or her previous activity data, with the data regularly imported (e.g., daily) into the parallel computation environment; (while daily computations are described hereinafter for example purposes, it is understood that more frequent computations such as every few hours or less frequent computations such as every two days may be performed). Unlike other systems, the parallel computation is performed daily on the user data. In other words, during the computation, only the daily data is generally processed, instead of independently processing the entire dataset in each local machine. The parallel environment, in conjunction with the per-day data processing, allows the algorithm to deal with a very large scale dataset and return results on any user-specified discrete time window. 
         [0016]    As shown in  FIG. 1 , daily (or other timed) internet-related data  102  for users (e.g., MSN Passport data) is input into a preprocessing mechanism  104 . In general, the preprocessing mechanism  104 , via a data deployment component  106  and hash feed generation component  108  sorts the data of users and distributes users among different parallel machines for parallel computations. 
         [0017]    As shown in  FIG. 1 , each machine  110  includes its user data as maintained in a local user database  112  or the like, where it is processed by a MinHash clustering mechanism (algorithm)  114 , e.g., daily, on an incremental basis, as described below. The data is further processed by a daily user cluster ID generation component  116 . In general, in MinHash processing, a set of signatures is generated for each user based on his or her activities relative to the combined activities of other users, and the signature for each user is stamped with data indicating a specific time window, e.g., the day of the users&#39; activities. 
         [0018]    To this end, given a set of activities, random permutations are used to calculate MinHash signatures for users. By way of example, consider that the following comprises the set of activities typed in by users: 
         [0019]    [xbox, car, Halo3, laptop] 
         [0020]    with the activities of user1=[xbox, Halo3], 
         [0021]    and the activities of user2=[Halo3, laptop]. 
         [0022]    In a first round of permutations/minwise hashing the set of activities is reordered as: 
         [0023]    [Halo3, car, laptop, xbox]. 
         [0024]    Because Halo3 is first in this ordering and each user has Halo3 as an activity, the MinHash signature of user1, mh(user1)=Halo3, and the MinHash signature of user2, mh(user2)=Halo3. 
         [0025]    A second round permutation/minwise hash reorders the activities as: 
         [0026]    [car, laptop, xbox, Halo3]. 
         [0027]    This time, the first to appear of those entered by user 1 is “xbox” and thus the MinHash signature of user1, mh(user1)=xbox. The first corresponding activity of what user2 entered is “laptop”, and thus mh(user2)=laptop. 
         [0028]    The one or more MinHash signatures computed for each user comprises a signature set for that user. Given two users, the ratio of the number of shared MinHash signatures in each user&#39;s signature set between those users to the number of permutations approximates the similarity between users: 
         [0000]        Pr ( mh   i ( u )= mh   i ( v ))=sim( u, v ) 
         [0029]    Mathematically, this may be set forth as: 
         [0000]    Suppose H={h k |k=1,2, . . . c} be Min-wise independent permutation, i.e., Pr(min{h k (A)}=h k (a j ))=1/|A|; (c is twenty in one implementation).
 
Define min-wise hash function:
 
         [0000]        mh   k ( u   i )=arg min { h   k ( u   i )| u   i     ⊂ A}   
         [0000]    Then sim(u i , u j )=|u 1 ∩u 2 |/|u 1 ∪u 2 |=Pr(mh k (u i )=mh k (u j )) where Pr(mh k (u i )=mh k (u j )) is approximated by |{mh g (u i )=mh g (u j ), g=1,2, . . . c}|/c. 
         [0030]    Thus, similar users get hashed to the same bucket while dissimilar ones do not. 
         [0031]    To summarize the upper portion of  FIG. 1 , to perform parallel MinHash computations, users are partitioned into different machines, with MinHash independently implemented on each machine. Note that instead of re-computing the MinHash signature for a user&#39;s activities, once a signature is available for a given user, an incremental MinHash is used, in which the MinHash signature of each user can be updated by the minimum of that user&#39;s signatures: 
         [0000]        mh   [t, t+k] ( u )=min { mh   s ( u ),  s=t, t+ 1,  t+ 2, . . .  t+k}.    
         [0000]    In this way, the users activities may be regularly (e.g., daily) hashed and efficiently merged, and the incremental MinHash allows for user input of a discrete time window, e.g., every weekend in the past year, or the past 3 days, and so forth. 
         [0032]    In the lower portion of  FIG. 1 , further processing uses the signatures to merge/cluster together the user-user similarity on different machines into clusters. To this end, following the local MinHash clustering on each local machine, there is provided a strategy to efficiently integrate the daily results such that a quick response may be output for any user (e.g., advertising customer) input  120  specified time window  122 , whether continuous or discrete. In general, the MinHash values of each user on each day are parallel computed and recorded. Then, the updated MinHash value of any user-specified time window can be combined through a simple logical computation, described below and represented by blocks  124 ,  126  and  128 . In one implementation, the clusters may be indexed (blocks  130  and  132 ) with the index  132  queried via an appropriate query  134 , such as through an online service  136 . For example, an online advertiser can lookup which users are clustered together with respect to a certain type of advertisement, as well as a time window as to when those users were clustered together, to send targeted advertisements to users based upon their clusters. 
         [0033]    Turning to a detailed explanation of parallel MinHash clustering in a flexible time window, let U={u i , i=1,2, . . . } represent a set of object to process and A={a j , j=1,2, . . . } represent the set of attributes that represent the objects. Each object at time stamp t is represented by a set of attributes Cu i (t)={a i1 , a i2 , . . . }, where Cu i (t) is a subset of A,=1,2, . . . . In this scheme, i is treated as the unique identifier (ID) of u i  and j=1,2, . . . as the unique ID of a j . IDs for newly appeared objects or attributes are incrementally assigned. 
         [0034]    Consider that at time t there is a collection of n objects and a collection of m attributes. If a new user and new attribute appears at time t+1, n+1 and m+1 are incrementally assigned as IDs for the new user and new attribute, respectively. A parallel MinHash clustering algorithm in flexible time window is set forth below and visually represented in  FIG. 2 : 
         [0000]    
       
         
               
               
             
           
               
                   
               
             
             
               
                 Input: 
                 Objects represented by attributes at time stamp t, i.e. Cu i (t), 
               
               
                   
                  i = 1, 2, ..., t = 0, 1, 2, ... 
               
               
                 Output: 
                 The cluster IDs of each object. (Note one object may 
               
               
                   
                 belong to different clusters, thus each object has 
               
               
                   
                 multiple cluster IDs) 
               
               
                 Parameters: 
                 p - number of hash functions 
               
               
                   
                 q - number of rounds for MinHash approximation 
               
               
                   
                 L - a large enough integer for constructing the hash 
               
               
                   
                 functions 
               
               
                   
               
               
                 (Note that in general, the larger the p value, the better the precision that is achieved; the larger the q value, the better the recall that is achieved. However, having a larger p and/or q will increase the computational time.) 
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
             
           
               
                   
               
               
                 Step 202 - Preprocessing. 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 Generate random feeds for constructing the hash functions. 
               
               
                 Randomly generate integers f k , g k , k = 1, 2, ... p * q, where f k  ≠ f l  and 
               
               
                 g k  ≠ g l  if k ≠ l. 
               
               
                 Hash objects into different machines in the parallel environment. This can 
               
               
                 also be done by randomly deploying objects into different machines. (The 
               
               
                 information of the same user is stored on the same machine.) 
               
               
                   
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
             
           
               
                   
               
               
                 Step 204 - MinHash 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 On each local machine, 
               
               
                   
                 for t=0,1,2,... (t is finite for the first time input) 
               
               
                   
                   for each object on current machine 
               
               
                   
                     for each attribute in Cu i (t) 
               
               
                   
                       suppose the current attribute ID is j 
               
               
                   
                       for k = 1,2, ... p * q 
               
               
                   
                         hash ijk  (t) = (f k  * j + g k )mod L 
               
               
                   
                       end for 
               
               
                   
                     end for 
               
               
                   
                     MinHash ik (t) = min j (hash ijk  (t)) 
               
               
                   
                   end for 
               
               
                   
                 end for 
               
               
                   
                   
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
             
           
               
                   
               
               
                 Step 206 - Clustering in each time stamp 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 for each time stamp t, 
               
               
                   
                   for each object i 
               
               
                   
                     CIDS il (t) = Ø for l = 1,2, ... q 
               
               
                   
                     for k = 1,2, ... p * q 
               
               
                   
                       if k mod q == l−1 
               
               
                   
                         CIDS il (t) = CIDS il (t) ∪ MinHash ik (t) 
               
               
                   
                       end if 
               
               
                   
                     end for 
               
               
                   
                     link all values in the same set as an ID according to the 
               
               
                   
                     order of appearance in this set 
               
               
                   
                     CIDS il (t) -&gt;CIDS il (t) 
               
               
                   
                   end for 
               
               
                   
                 group the objects with the same ID into a cluster 
               
               
                   
                 end for 
               
               
                   
                   
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
             
           
               
                   
               
               
                 Step 208 - Clustering results generation in flexible time window 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 For any selected time stamps, (without loss of generality), suppose the 
               
               
                 selected time stamps are t = 0,1,2, ... s 
               
               
                 for each object which has new attribute appear 
               
               
                   for k = 1,2, ... p * q 
               
               
                     MinHash ik =min t=0,1,2,...s {MinHash ik (t)} 
               
               
                   end for 
               
               
                 end for 
               
               
                 call step 206 for clustering 
               
               
                   
               
             
          
         
       
     
       Exemplary Operating Environment 
       [0035]      FIG. 3  illustrates an example of a suitable computing and networking environment  300  on which the examples of  FIGS. 1-2  may be implemented. The computing system environment  300  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  300  be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment  300 . 
         [0036]    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. 
         [0037]    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. 
         [0038]    With reference to  FIG. 3 , an exemplary system for implementing various aspects of the invention may include a general purpose computing device in the form of a computer  310 . Components of the computer  310  may include, but are not limited to, a processing unit  320 , a system memory  330 , and a system bus  321  that couples various system components including the system memory to the processing unit  320 . The system bus  321  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. 
         [0039]    The computer  310  typically includes a variety of computer-readable media. Computer-readable media can be any available media that can be accessed by the computer  310  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  310 . 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 may also be included within the scope of computer-readable media. 
         [0040]    The system memory  330  includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM)  331  and random access memory (RAM)  332 . A basic input/output system  333  (BIOS), containing the basic routines that help to transfer information between elements within computer  310 , such as during start-up, is typically stored in ROM  331 . RAM  332  typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit  320 . By way of example, and not limitation,  FIG. 3  illustrates operating system  334 , application programs  335 , other program modules  336  and program data  337 . 
         [0041]    The computer  310  may also include other removable/non-removable, volatile/nonvolatile computer storage media. By way of example only,  FIG. 3  illustrates a hard disk drive  341  that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive  351  that reads from or writes to a removable, nonvolatile magnetic disk  352 , and an optical disk drive  355  that reads from or writes to a removable, nonvolatile optical disk  356  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  341  is typically connected to the system bus  321  through a non-removable memory interface such as interface  340 , and magnetic disk drive  351  and optical disk drive  355  are typically connected to the system bus  321  by a removable memory interface, such as interface  350 . 
         [0042]    The drives and their associated computer storage media, described above and illustrated in  FIG. 3 , provide storage of computer-readable instructions, data structures, program modules and other data for the computer  310 . In  FIG. 3 , for example, hard disk drive  341  is illustrated as storing operating system  344 , application programs  345 , other program modules  346  and program data  347 . Note that these components can either be the same as or different from operating system  334 , application programs  335 , other program modules  336 , and program data  337 . Operating system  344 , application programs  345 , other program modules  346 , and program data  347  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  310  through input devices such as a tablet, or electronic digitizer,  364 , a microphone  363 , a keyboard  362  and pointing device  361 , commonly referred to as mouse, trackball or touch pad. Other input devices not shown in  FIG. 3  may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  320  through a user input interface  360  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  391  or other type of display device is also connected to the system bus  321  via an interface, such as a video interface  390 . The monitor  391  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  310  is incorporated, such as in a tablet-type personal computer. In addition, computers such as the computing device  310  may also include other peripheral output devices such as speakers  395  and printer  396 , which may be connected through an output peripheral interface  394  or the like. 
         [0043]    The computer  310  may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer  380 . The remote computer  380  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  310 , although only a memory storage device  381  has been illustrated in  FIG. 3 . The logical connections depicted in  FIG. 3  include one or more local area networks (LAN)  371  and one or more wide area networks (WAN)  373 , but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. 
         [0044]    When used in a LAN networking environment, the computer  310  is connected to the LAN  371  through a network interface or adapter  370 . When used in a WAN networking environment, the computer  310  typically includes a modem  372  or other means for establishing communications over the WAN  373 , such as the Internet. The modem  372 , which may be internal or external, may be connected to the system bus  321  via the user input interface  360  or other appropriate mechanism. A wireless networking component  374  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  310 , or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,  FIG. 3  illustrates remote application programs  385  as residing on memory device  381 . 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. 
         [0045]    An auxiliary subsystem  399  (e.g., for auxiliary display of content) may be connected via the user interface  360  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  399  may be connected to the modem  372  and/or network interface  370  to allow communication between these systems while the main processing unit  320  is in a low power state. 
       Conclusion 
       [0046]    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 failing within the spirit and scope of the invention.