Patent Application
20160078453
Embodiments of the invention generally relate to information technology, and, more particularly, to data segmentation techniques.
Customers often purchase goods and services not just for themselves, but for family members as well. Purchase records, therefore, can often reflect the aggregate of the needs, habits and requirements of an entire family, and knowing the composition of a family and the family's needs and habits enables more accurate personalization for the family. Recommendations can be made if the family composition associated with an individual purchaser is known, but such a determination derived solely from the individual purchaser's purchases faces challenges under existing segmentation approaches.
Discovered segments can be interpretable for a model which recognizes that each basket corresponds to a mixture over segments. As used herein, a basket refers to a collection or a set of items in a retail setting. For such a model, a discovered segment can include, for example, (male, PhD, middle aged). For a model that associates a single segment with a basket, an example segment might include [0.2(male, PhD, middle aged)+0.3(female, college graduate, middle aged)+0.3(male, high school graduate, teenager)+ . . . ].
Learning family purchase habits of segments is more challenging statistically when the family structure is not recognized. By way merely of example, assume that there are six people in an example family structure, each of whom can belong to four different segments, and each of whom may or may not be geographically present in a given family. With such an example framework, 56=15625 segments can be obtained for one particular case wherein the family structure is not recognized, and 4*6=24 segments can be obtained for another particular case wherein a family structure is recognized. In this example, the first case refers to a scenario wherein the segments for the family members are not taken into account, and the second case refers to a scenario wherein the segments are taken into account. Given a finite amount of data, having significantly fewer parameters to learn will likely result in a significantly higher level of confidence on the estimated values (or posterior distributions) of such parameters.
Accordingly, a need exists for techniques to determine associated groups of individuals based on multiple items of individual transaction data.
In one aspect of the present invention, techniques for determining groups of individuals based on multiple items of individual transaction data are provided. An exemplary computer-implemented method can include steps of initializing a customer segment value for each of multiple purchased items identified in a purchase record based on one or more historical purchasing patterns; updating the customer segment value for each of the multiple purchased items based on the customer segment values for the other purchased items identified in the purchase record; and determining a customer segment composition of a group of individuals associated with the multiple purchased items identified in the purchase record based on the updated customer segment values.
In another aspect of the invention, an exemplary computer-implemented method can include steps of initializing an item category for each of multiple purchased items identified in a purchase record; initializing a customer segment value for each of the multiple purchased items identified in the purchase record based on one or more historical purchasing patterns; and updating the customer segment value for each of the multiple purchased items based on (i) the customer segment values for the other purchased items identified in the purchase record and (ii) the item categories initialized for the other purchased items identified in the purchase record. The method also includes updating the item category for each of the multiple purchased items based on (i) the customer segment values for the other purchased items identified in the purchase record and (ii) the item categories initialized for the other purchased items identified in the purchase record; and determining the customer segment composition of a group of individuals associated with the multiple purchased items identified in the purchase record based on (i) the updated customer segment values and (ii) the updated item categories.
In yet another aspect of the invention, an exemplary computer-implemented method can include steps of initializing an item category for each of multiple purchased items identified in a purchase record; initializing a customer segment value for each of the multiple purchased items identified in the purchase record based on one or more historical purchasing patterns; and updating the customer segment value for each of the multiple purchased items based on (i) a customer identifier for the purchase record, (ii) the customer segment values for the other purchased items identified in the purchase record, and (iii) the item categories initialized for the other purchased items identified in the purchase record. The method also includes updating the item category for each of the multiple purchased items based on (i) the customer segment values for the other purchased items identified in the purchase record and (ii) the item categories initialized for the other purchased items identified in the purchase record; and determining the customer segment composition of a group of individuals associated with the multiple purchased items identified in the purchase record based on (i) the customer identifier for the purchase record, (ii) the updated customer segment values and (iii) the updated item categories.
Further, in another aspect of the invention, an exemplary computer-implemented method can include steps of initializing an item category for each of multiple purchased items identified in a purchase record; initializing a customer identifier for the purchase record; and determining a network of additional customers associated with the customer identifier initialized for the purchase record based on one or more parameters. The method also includes generating a distribution estimate over multiple customer segments for multiple item categories across multiple additional purchase records for the customer identifier by randomly sampling one or more of the additional customers in the network; and determining a customer segment composition of a group of individuals associated with the customer identifier based on (i) the purchase record and (ii) the distribution estimate.
Another aspect of the invention or elements thereof can be implemented in the form of an article of manufacture tangibly embodying computer readable instructions which, when implemented, cause a computer to carry out a plurality of method steps, as described herein. Furthermore, another aspect of the invention or elements thereof can be implemented in the form of an apparatus including a memory and at least one processor that is coupled to the memory and configured to perform noted method steps. Yet further, another aspect of the invention or elements thereof can be implemented in the form of means for carrying out the method steps described herein, or elements thereof; the means can include hardware module(s) or a combination of hardware and software modules, wherein the software modules are stored in a tangible computer-readable storage medium (or multiple such media).
These and other objects, features and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings.
As described herein, an aspect of the present invention includes discovering virtual families from purchase records. As used herein, the term “family” is used in a general sense to refer to a group of individuals who frequently transact as a group. One example embodiment of the invention can be carried out within the context of a traditional family unit, while one or more other embodiments of the invention can be carried out within additional contexts that can include, for instance, a group of friends who frequently shop together and/or for one another. Accordingly, one or more embodiments of the invention include determining groups of individuals based on multiple items of individual transaction data.
At least one embodiment of the invention includes inferring segments of multiple hidden customers associated with given purchase records (that is, purchase records not explicitly linked to the hidden customers). Accordingly, one or more embodiments of the invention include utilizing one or more of (i) customer segment data, (ii) purchase records, (iii) item categories, (iv) customer identifiers (IDs) associated with purchases, and (v) customer/buyer network data to associate each item of a purchase with a particular segment. Further, such embodiments can also include iterating over each item in each purchase record to determine a virtual family associated with the purchase, wherein iterating can include updating the segment and category distributions based on current segments and categories of all other items and/or current segment and category distributions making use of the customer IDs and the customer's social network.
By way of illustration, an example embodiment of the invention can include the following determination. Given T customer segments, N purchase records, K item categories, a customer ID for each purchase, and a network of buyers, an example embodiment of the invention can include associating, for each purchase item for each of the N purchase records, the segment (of the customer) for whom the item was purchased. Additionally, such an embodiment further includes aggregating and thresholding the segment counts to provide a profile of the virtual family associated with the purchase record. By way of example, aggregating can include counting the total number of items assigned to a specific segment. Additionally, thresholding can be performed, for example, by selecting all segments that have assignment counts above a specific value.
Accordingly, the example embodiment of the invention illustrated in
Additionally, such an embodiment of the invention can include utilizing a probabilistic inference such as LDA to estimate a posterior probability of segments for each purchase record, as well as an associated segment for each item in each purchase record. In at least one embodiment of the invention, such distributions can be estimated through sampling procedures. For example, a sampling algorithm repeatedly samples the segment category for each purchase item, given the segments for all other items. When the sampling converges, the posterior distribution over segments for each purchase record is obtained by normalizing the occurrence counts of each segment over items in that record.
Using an implementation such as depicted in
Additionally, an embodiment of the invention implementing a model such as depicted in
Additionally, in connection with a model such as depicted in
Additionally, at least one embodiment of the invention can include modifying the model depicted in
By way of example, at least one embodiment of the invention can include modifying the model depicted in
Further, at least one embodiment of the invention can include modifying the probabilistic inference associated with the model depicted in
Step 504 includes initializing a customer segment value for each of the multiple purchased items identified in the purchase record based on one or more historical purchasing patterns. Initializing the customer segment value for each of the multiple purchased items can include determining a distribution of each customer segment over multiple item categories. Additionally, in such an embodiment of the invention, updating can include updating the distribution of each customer segment over the multiple item categories based on (i) the customer segment values for the other purchased items identified in the purchase record and (ii) the item categories initialized for the other purchased items identified in the purchase record.
Additionally, as detailed herein, a customer segment value can include, for example, a consideration of customer age, customer gender, customer education level, etc.
Step 506 includes updating the customer segment value for each of the multiple purchased items based on (i) the customer segment values for the other purchased items identified in the purchase record and (ii) the item categories initialized for the other purchased items identified in the purchase record.
Step 508 includes determining a customer segment composition of a group of individuals associated with the multiple purchased items identified in the purchase record based on the updated customer segment values. Determining can include determining a customer segment distribution across the multiple purchased items identified in the purchase record based on the updated customer segment values. Determining can also include aggregating and thresholding the updated customer segment values to generate a profile of said group of individuals.
The group of individuals associated with the multiple purchases items identified in the purchase record is not identified in the purchase record. Additionally, the group can include, for example, a family unit, a collection of unrelated individuals, or a combination thereof.
Also, at least one embodiment of the invention includes steps of initializing an item category for each of multiple purchased items identified in a purchase record, initializing a customer identifier for the purchase record, and determining a network of additional customers associated with the customer identifier initialized for the purchase record based on one or more parameters. Such an embodiment further includes the steps of generating a distribution estimate over multiple customer segments for multiple item categories across multiple additional purchase records for the customer identifier by randomly sampling one or more of the additional customers in the network, and determining the customer segment composition of a group of individuals associated with the customer identifier based on (i) the purchase record and (ii) the distribution estimate.
In such an embodiment, the network can be derived, for example, from a social network associated with the customer identifier. Also, similar to the embodiments detailed above in connection with
The techniques depicted in
Additionally, the techniques depicted in
An aspect of the invention or elements thereof can be implemented in the form of an apparatus including a memory and at least one processor that is coupled to the memory and configured to perform exemplary method steps.
Additionally, an aspect of the present invention can make use of software running on a general purpose computer or workstation. With reference to
Accordingly, computer software including instructions or code for performing the methodologies of the invention, as described herein, may be stored in associated memory devices (for example, ROM, fixed or removable memory) and, when ready to be utilized, loaded in part or in whole (for example, into RAM) and implemented by a CPU. Such software could include, but is not limited to, firmware, resident software, microcode, and the like.
A data processing system suitable for storing and/or executing program code will include at least one processor 602 coupled directly or indirectly to memory elements 604 through a system bus 610. The memory elements can include local memory employed during actual implementation of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during implementation.
Input/output or I/O devices (including but not limited to keyboards 608, displays 606, pointing devices, and the like) can be coupled to the system either directly (such as via bus 610) or through intervening I/O controllers (omitted for clarity).
Network adapters such as network interface 614 may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modems and Ethernet cards are just a few of the currently available types of network adapters.
As used herein, including the claims, a “server” includes a physical data processing system (for example, system 612 as shown in
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method and/or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, as noted herein, aspects of the present invention may take the form of a computer program product that may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.
The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (for example, light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.
It should be noted that any of the methods described herein can include an additional step of providing a system comprising distinct software modules embodied on a computer readable storage medium; the modules can include, for example, any or all of the components detailed herein. The method steps can then be carried out using the distinct software modules and/or sub-modules of the system, as described above, executing on a hardware processor 602. Further, a computer program product can include a computer-readable storage medium with code adapted to be implemented to carry out at least one method step described herein, including the provision of the system with the distinct software modules.
In any case, it should be understood that the components illustrated herein may be implemented in various forms of hardware, software, or combinations thereof, for example, application specific integrated circuit(s) (ASICS), functional circuitry, an appropriately programmed general purpose digital computer with associated memory, and the like. Given the teachings of the invention provided herein, one of ordinary skill in the related art will be able to contemplate other implementations of the components of the invention.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of another feature, integer, step, operation, element, component, and/or group thereof.
The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed.
At least one aspect of the present invention may provide a beneficial effect such as, for example, analyzing each item in a purchase record to determine the virtual family associated with the purchase.
The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
