Patent ID: 12217296

DETAILED DESCRIPTION

The description of the preferred embodiments is intended to be read in connection with the accompanying drawings, which are to be considered part of the entire written description of this invention. The drawing figures are not necessarily to scale and certain features of the invention may be shown exaggerated in scale or in somewhat schematic form in the interest of clarity and conciseness. Terms concerning data connections, coupling and the like, such as “connected” and “interconnected,” and/or “in signal communication with” refer to a relationship wherein systems or elements are electrically and/or wirelessly connected to one another either directly or indirectly through intervening systems, as well as both moveable or rigid attachments or relationships, unless expressly described otherwise. The term “operatively coupled” is such a coupling or connection that allows the pertinent structures to operate as intended by virtue of that relationship.

In various embodiments, systems and methods of personalized user and item recommendations are disclosed. A trained inference model is generated using a deep joint variational autoencoder architecture. The trained inference model is configured to use latent space representations to provide complex understanding of a feature space without the need to perform time consuming feature extraction processes. The trained inference model may be trained by a training set including past interaction data. The trained inference model is configured to retrieve a set of candidate items from a pool of items based on latent space representations.

FIG.1illustrates a computer system configured to implement one or more processes, in accordance with some embodiments. The system2is a representative device and may comprise a processor subsystem4, an input/output subsystem6, a memory subsystem8, a communications interface10, and a system bus12. In some embodiments, one or more than one of the system2components may be combined or omitted such as, for example, not including an input/output subsystem6. In some embodiments, the system2may comprise other components not combined or comprised in those shown inFIG.1. For example, the system2may also include, for example, a power subsystem. In other embodiments, the system2may include several instances of the components shown inFIG.1. For example, the system2may include multiple memory subsystems8. For the sake of conciseness and clarity, and not limitation, one of each of the components is shown inFIG.1.

The processor subsystem4may include any processing circuitry operative to control the operations and performance of the system2. In various aspects, the processor subsystem4may be implemented as a general purpose processor, a chip multiprocessor (CMP), a dedicated processor, an embedded processor, a digital signal processor (DSP), a network processor, an input/output (I/O) processor, a media access control (MAC) processor, a radio baseband processor, a co-processor, a microprocessor such as a complex instruction set computer (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, and/or a very long instruction word (VLIW) microprocessor, or other processing device. The processor subsystem4also may be implemented by a controller, a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a programmable logic device (PLD), and so forth.

In various aspects, the processor subsystem4may be arranged to run an operating system (OS) and various applications. Examples of an OS comprise, for example, operating systems generally known under the trade name of Apple OS, Microsoft Windows OS, Android OS, Linux OS, and any other proprietary or open source OS. Examples of applications comprise, for example, network applications, local applications, data input/output applications, user interaction applications, etc.

In some embodiments, the system2may comprise a system bus12that couples various system components including the processing subsystem4, the input/output subsystem6, and the memory subsystem8. The system bus12can be any of several types of bus structure(s) including a memory bus or memory controller, a peripheral bus or external bus, and/or a local bus using any variety of available bus architectures including, but not limited to, 9-bit bus, Industrial Standard Architecture (ISA), Micro-Channel Architecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), Peripheral Component Interconnect Card International Association Bus (PCMCIA), Small Computers Interface (SCSI) or other proprietary bus, or any custom bus suitable for computing device applications.

In some embodiments, the input/output subsystem6may include any suitable mechanism or component to enable a user to provide input to system2and the system2to provide output to the user. For example, the input/output subsystem6may include any suitable input mechanism, including but not limited to, a button, keypad, keyboard, click wheel, touch screen, motion sensor, microphone, camera, etc.

In some embodiments, the input/output subsystem6may include a visual peripheral output device for providing a display visible to the user. For example, the visual peripheral output device may include a screen such as, for example, a Liquid Crystal Display (LCD) screen. As another example, the visual peripheral output device may include a movable display or projecting system for providing a display of content on a surface remote from the system2. In some embodiments, the visual peripheral output device can include a coder/decoder, also known as Codecs, to convert digital media data into analog signals. For example, the visual peripheral output device may include video Codecs, audio Codecs, or any other suitable type of Codec.

The visual peripheral output device may include display drivers, circuitry for driving display drivers, or both. The visual peripheral output device may be operative to display content under the direction of the processor subsystem6. For example, the visual peripheral output device may be able to play media playback information, application screens for application implemented on the system2, information regarding ongoing communications operations, information regarding incoming communications requests, or device operation screens, to name only a few.

In some embodiments, the communications interface10may include any suitable hardware, software, or combination of hardware and software that is capable of coupling the system2to one or more networks and/or additional devices. The communications interface10may be arranged to operate with any suitable technique for controlling information signals using a desired set of communications protocols, services or operating procedures. The communications interface10may comprise the appropriate physical connectors to connect with a corresponding communications medium, whether wired or wireless.

Vehicles of communication comprise a network. In various aspects, the network may comprise local area networks (LAN) as well as wide area networks (WAN) including without limitation Internet, wired channels, wireless channels, communication devices including telephones, computers, wire, radio, optical or other electromagnetic channels, and combinations thereof, including other devices and/or components capable of/associated with communicating data. For example, the communication environments comprise in-body communications, various devices, and various modes of communications such as wireless communications, wired communications, and combinations of the same.

Wireless communication modes comprise any mode of communication between points (e.g., nodes) that utilize, at least in part, wireless technology including various protocols and combinations of protocols associated with wireless transmission, data, and devices. The points comprise, for example, wireless devices such as wireless headsets, audio and multimedia devices and equipment, such as audio players and multimedia players, telephones, including mobile telephones and cordless telephones, and computers and computer-related devices and components, such as printers, network-connected machinery, and/or any other suitable device or third-party device.

Wired communication modes comprise any mode of communication between points that utilize wired technology including various protocols and combinations of protocols associated with wired transmission, data, and devices. The points comprise, for example, devices such as audio and multimedia devices and equipment, such as audio players and multimedia players, telephones, including mobile telephones and cordless telephones, and computers and computer-related devices and components, such as printers, network-connected machinery, and/or any other suitable device or third-party device. In various implementations, the wired communication modules may communicate in accordance with a number of wired protocols. Examples of wired protocols may comprise Universal Serial Bus (USB) communication, RS-232, RS-422, RS-423, RS-485 serial protocols, FireWire, Ethernet, Fibre Channel, MIDI, ATA, Serial ATA, PCI Express, T-1 (and variants), Industry Standard Architecture (ISA) parallel communication, Small Computer System Interface (SCSI) communication, or Peripheral Component Interconnect (PCI) communication, to name only a few examples.

Accordingly, in various aspects, the communications interface10may comprise one or more interfaces such as, for example, a wireless communications interface, a wired communications interface, a network interface, a transmit interface, a receive interface, a media interface, a system interface, a component interface, a switching interface, a chip interface, a controller, and so forth. When implemented by a wireless device or within wireless system, for example, the communications interface10may comprise a wireless interface comprising one or more antennas, transmitters, receivers, transceivers, amplifiers, filters, control logic, and so forth.

In various aspects, the communications interface10may provide data communications functionality in accordance with a number of protocols. Examples of protocols may comprise various wireless local area network (WLAN) protocols, including the Institute of Electrical and Electronics Engineers (IEEE) 802.xx series of protocols, such as IEEE 802.11a/b/g/n, IEEE 802.16, IEEE 802.20, and so forth. Other examples of wireless protocols may comprise various wireless wide area network (WWAN) protocols, such as GSM cellular radiotelephone system protocols with GPRS, CDMA cellular radiotelephone communication systems with 1×RTT, EDGE systems, EV-DO systems, EV-DV systems, HSDPA systems, and so forth. Further examples of wireless protocols may comprise wireless personal area network (PAN) protocols, such as an Infrared protocol, a protocol from the Bluetooth Special Interest Group (SIG) series of protocols (e.g., Bluetooth Specification versions 5.0, 6, 7, legacy Bluetooth protocols, etc.) as well as one or more Bluetooth Profiles, and so forth. Yet another example of wireless protocols may comprise near-field communication techniques and protocols, such as electro-magnetic induction (EMI) techniques. An example of EMI techniques may comprise passive or active radio-frequency identification (RFID) protocols and devices. Other suitable protocols may comprise Ultra Wide Band (UWB), Digital Office (DO), Digital Home, Trusted Platform Module (TPM), ZigBee, and so forth.

In some embodiments, at least one non-transitory computer-readable storage medium is provided having computer-executable instructions embodied thereon, wherein, when executed by at least one processor, the computer-executable instructions cause the at least one processor to perform embodiments of the methods described herein. This computer-readable storage medium can be embodied in memory subsystem8.

In some embodiments, the memory subsystem8may comprise any machine-readable or computer-readable media capable of storing data, including both volatile/non-volatile memory and removable/non-removable memory. The memory subsystem8may comprise at least one non-volatile memory unit. The non-volatile memory unit is capable of storing one or more software programs. The software programs may contain, for example, applications, user data, device data, and/or configuration data, or combinations therefore, to name only a few. The software programs may contain instructions executable by the various components of the system2.

In various aspects, the memory subsystem8may comprise any machine-readable or computer-readable media capable of storing data, including both volatile/non-volatile memory and removable/non-removable memory. For example, memory may comprise read-only memory (ROM), random-access memory (RAM), dynamic RAM (DRAM), Double-Data-Rate DRAM (DDR-RAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory (e.g., NOR or NAND flash memory), content addressable memory (CAM), polymer memory (e.g., ferroelectric polymer memory), phase-change memory (e.g., ovonic memory), ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS) memory, disk memory (e.g., floppy disk, hard drive, optical disk, magnetic disk), or card (e.g., magnetic card, optical card), or any other type of media suitable for storing information.

In one embodiment, the memory subsystem8may contain an instruction set, in the form of a file for executing various methods, such as methods including implementation of trained inference models configured to utilize latent space representations, as described herein. The instruction set may be stored in any acceptable form of machine readable instructions, including source code or various appropriate programming languages. Some examples of programming languages that may be used to store the instruction set comprise, but are not limited to: Java, C, C++, C#, Python, Objective-C, Visual Basic, or .NET programming. In some embodiments a compiler or interpreter is comprised to convert the instruction set into machine executable code for execution by the processing subsystem4.

FIG.2illustrates a network environment20configured to provide personalized recommendations based at least in part on a trained deep joint variational autoencoder model, in accordance with some embodiments. The network environment20may include, but is not limited to, one or more user systems22a,22b, a network interface system24, an item retrieval system26, a model training system28, one or more item databases30, and/or any other suitable system. Each of the systems22a-28and/or the databases30may include a system as described above with respect toFIG.1. Although embodiments are illustrates herein having discrete systems, it will be appreciated that one or more of the illustrated systems may be combined into a single system configured to implement the functionality and/or services of each of the combined systems. For example, although embodiments are illustrated and discussed herein including each of a network interface system24, an item retrieval system26, and a model training system28, it will be appreciated that these systems may be combined into a single logical and/or physical system configured to perform the functions and/or provide services associated with each of the individual systems. It will also be appreciated that each of the illustrated systems may be replicated and/or split into multiple systems configured to perform similar functions and/or parts of a function. Each of the systems22a-28and/or the databases30are in signal communication via one or more intervening network elements, illustrates as network cloud40.

In some embodiments, the network interface system24is configured to provide a network interface to the one or more user systems22a,22b. The network interface may include any suitable type of network interface, such as, for example, an e-commerce interface, a search interface, an inventory interface, etc. Although embodiments are discussed herein with reference to an e-commerce network interface, it will be appreciated that the disclosed systems and methods are applicable to any interface including sets of items that may be retrieved based on search queries and rankings.

In some embodiments, a user system22a,22binteracts with a network interface provided by the network interface system24. The network interface may be any suitable interface. For example, in some embodiments, the network interface is an e-commerce interface configured to present one or more products, product pages, descriptions, etc. to enable a user to view and purchase items. In some embodiments, the network interface24is configured to provide recommended items and/or categories of items to a user system22a,22bbased on prior interactions between the network interface system24and a user associated with the user system22a,22b. As discussed in greater detail below, the recommended items/categories may be selected by a trained selection model configured to utilize latent space representations of a feature set. In some embodiments, interaction data is stored in database30.

In some embodiments, the network interface system24receives a set, or list, of recommended items for a specific user system22a,22bfrom an item recommendation system26. The item recommendation system26generates the set of recommended items based on a trained inference model. The trained inference model includes a deep joint variational encoder architecture, as discussed in greater detail below. In some embodiments, the item recommendation system26is configured to retrieve a set of candidate items and generate a set of recommended items from the set of candidate items. For example, in some embodiments, the item recommendation system26utilizes a trained inference model to identify a set of recommended items from a pool of potential items, e.g., the set of candidate items. As discussed in greater detail below, in some embodiments, the identification of recommended items is performed by a trained inference model based on latent space representations.

In some embodiments, the item recommendation system26is in signal (e.g., data) communication with one or more item databases30containing a representation for each item in the pool of items. In some embodiments, the item recommendation system26is configured calculate ranking scores for each item in the pool. In some embodiments, representations in the item database30may be sorted by predefined categories, geographic areas, etc. and the comparison process may use only one or more of the predefined categories.

In some embodiments, the item recommendation system26is in signal (e.g., data) communication with a model training system28. The model training system28may be configured to generate a trained inference model by training an untrained or previously trained inference model using a training data set. For example, in some embodiments, the model training system28is configured to generate a trained inference model using an untrained machine learning model. It will be appreciated that the specific machine learning model used may be any suitable machine learning model.

In some embodiments, the model training system28is configured to generate a trained inference model and provide the trained inference model to the item recommendation system26and/or a production environment for use in item recommendation tasks. In other embodiments, the trained inference model may be implemented directly on the model training system28and/or any other system and accessed by the item recommendation system26using one or more network connections. The trained prediction model may be updated by replacing the current trained prediction model with a new trained prediction model generated by the prediction model generation system28at a predetermined interval, such as, for example, bi-monthly, monthly, weekly, etc. As discussed below, each trained prediction model is generated, to utilize latent space representations of a feature set. It will be appreciated that any suitable network or system architecture may be used to implement the disclosed systems and methods.

FIG.3is a flowchart illustrating a method100of selecting content from a pool using a trained inference model, in accordance with some embodiments.FIG.4is a process flow150illustrating various steps of the method100of selecting content from a pool using a trained inference model illustrated inFIG.3, in accordance with some embodiments. At step102, a target input152is received. The target input may include any suitable target input152, such as, for example, a user identifier, a target string, a target item, etc. The target input152may be received from any suitable system, such as, for example, a user system22a,22b. In some embodiments, the target input152includes an alphanumeric string, such as, for example, a search query, an item identifier, a stock-keeping unit (SKU), and/or any other suitable alphanumeric string. In some embodiments, the target input152includes a beacon response, cookie identifier, and/or other unique user identifier. Although specific embodiments are discussed herein, it will be appreciated that any suitable target input may be used.

In some embodiments, the target input152includes a set of prior user interactions (or impressions) with one or more systems (such as aa network interface system24). User interactions may include, but are not limited to, user item interactions such as an item view (e.g., user seeing an item in search results, recommended items, ad, etc.), item click-through (e.g., user clicking on link to go to product-specific information), item add-to-cart (e.g., user adding the product to a virtual cart for future purchase), item purchase (user ordering the item), etc. User interactions may also include, but are not limited to, user specified or derived information such as user preferences (color preference, brand preference, etc. of the specific user), aggregated information, anonymized information, and/or any other suitable user interactions.

In some embodiments, the target input152may be received from a database configured to maintain prior interaction data, such as, for example, database30. The set of prior system interactions152may be obtained from file system data, such as, for example, log data. The log data may be generated and/or maintained by any suitable system, such as the network interface system24. In some embodiments, log data is maintained for each user.

At optional step104, a set of candidate items154is selected from a pool of items156. In some embodiments, the set of candidate items154are selected based on, for example, a basic comparison between the target input and the items in the pool of items. It will be appreciated that any suitable search algorithm or process may be used to select the set of candidate items154from the pool of items156. The pool of items156may be stored in a database, such as database30discussed above. The initial search and selection of the set of candidate items may be performed by an item inference model158, a search engine, other query system. In some embodiments, step104is skipped and the set of candidate items154is the same as the pool of items156.

At optional step106, the target input152may be preprocessed. The preprocessing may be performed by any suitable mechanism, such as, for example, a preprocessing element implemented by the prediction model training system28, although it will be appreciated that preprocessing may be performed by any suitable system and the set of prior system interactions152may be processed prior to being stored in and/or after being retrieved from a storage mechanism, such as the database30. In some embodiments, preprocessing includes limiting a set of prior system interactions to user sets having at least a predetermined number of interactions or a maximum number of interactions, user sets having at least one interaction within a predetermined time period, interactions within a predetermined category, and/or otherwise limited. For example, in various embodiments, a target input152including a set of prior interactions152may be limited to the last N interactions (where N is any integer greater than 1), limited to users having at least N interactions (e.g., 5, 10, 15, etc.), limited to products having at least N user interactions (e.g., 3, 4, 5, 6, etc.), and/or limited using any other suitable criteria. In some embodiments, the set of prior interactions is split into multiple sets, including, for example, a training set, a validation set, a test set, etc. Preprocessing may further include, but is not limited to, normalization of the item/product interactions and/or time values.

At step108, the target input152(and optionally the set of candidate items154) are provided to a trained inference model158configured to generate a set of recommended items160. The trained inference model158may be implemented by one or more systems, such as the item retrieval system26. In some embodiments, the candidate item selection process executed at step104and the trained inference model158may be implemented by the same system and/or by different systems.

In some embodiments, the trained inference model158is configured to utilize latent space representations of prior user interactions to select recommended items160from the set of candidate items154. As discussed in greater detail below, the latent space representations are configured to incorporate implicit feedback, such as feedback derived from both interactions (e.g., a user has interacted with an item in the pool previously) and non-interactions (e.g., a user has not interacted with an item in the pool previously). In some embodiments, interactions between a set of users uiand a set of items ijmay be represented as a matrix. If a user has interacted with an item, the matrix may have a value of 1 for the user/item intersection and if a user has not interacted with an item, the matrix may a value of 0 for the user/item intersection. For example, in some embodiments, a matrix representing interactions between a set of three users and a set of four items may be represented as:

I1I2I3I4U11011U20001U30111
Although embodiments are illustrated herein using a binary matrix (e.g., a matrix having values of 0 or 1) it will be appreciated that any suitable matrix values may be used to represent user and item interactions. Rows may be extracted from the matrix to provide sets representing a specific user's uiinteractions with all items in the set of items154and columns may be extracted to provide sets representing a specific item's ijinteractions across the set of users in a dataset.

In some embodiments, the trained inference model158is configured to utilize learnt latent space representations to generate item recommendations. For example, in some embodiments, the trained inference model158is configured to compare a latent space representation of a user query to latent space representations of items in a pool of candidate items154to generate a set of recommended items160. The latent space representations are generated based on prior interactions of the user and represent a likelihood of the user interacting with certain items in the pool of candidate items154. In some embodiments, the latent space representations are generated using a deep joint variational autoencoder process, as discussed in greater detail below. The deep joint variational autoencoder process generates latent space representations that capture non-linear relationships between users and items, identifies diverse user preferences, and provides increased performance on sparse datasets as compared to traditional methods.

In some embodiments, the item inference model158includes an inference engine generated by a downstream model training process170configured to train an item inference model, such as an embedding comparison model, using latent space embeddings generated by a deep joint variational autoencoder network172. The trained inference engine may include any suitable inference model. Item recommendation systems and methods are described, for example, in U.S. patent application Ser. No. 17/163,383, filed on Jan. 30, 2021, entitled “Composite Embedding Systems and Methods for Multi-Level Granularity Similarity Relevance Scoring,” the disclosure of which is incorporated by reference herein in its entirety.

FIG.5illustrates one embodiment of a deep joint variational autoencoder network200configured to use at least two joint variational autoencoder processes202a,202bto simultaneously capture user-user and item-item correlations, in accordance with some embodiments. An input matrix204is received as an input to the deep joint variational network200. Each row in the input matrix204is extracted as a user input206a-206cand each column in the input matrix204is extracted as an item input208a-208d.

The user inputs206a-206care provided to a user variational autoencoder210aconfigured to generate probability distributions based on user-user similarities. Simultaneously, the item inputs208a-208dare provided to an item variational autoencoder210bconfigured to generate probability distributions based on item-item similarities. The user variational autoencoder210aand the item variational autoencoder210bare implemented independently.

Sampling is performed to obtain values within each of the generated distributions to generate a user output matrix212ahaving similar values to those of the user inputs206a-206cand an item output matrix212bhaving similar values to those of the item inputs208a-208d. Although the output matrices212a,212bare similar to the inputs206a-206c,208a-208d, the output matrices212a,212bcan include estimated values for user-item interactions that have not actually occurred. For example, even if a user has not interacted with a certain item, the generated output matrices212a,212bcan include an estimated likelihood that a user uiwill interact with an item ij.

The output matrices212a,212bmay be combined to generate a final output matrix214. In the illustrated embodiment, the final output matrix214is generated by averaging the values in the corresponding cells of the user output matrix212aand the item output matrix212b. Although embodiments are discussed herein including average values, it will be appreciated that the output matrices212a,212bmay be combined to generate a final output matrix214using any suitable combination, such as, for example, any suitable weighted and/or unweighted combination.

In some embodiments, the joint variational autoencoder network200is configured to operate on implicit data (e.g., user/item interactions and non-interactions) to generate user and item representations for the top-k recommendation tasks (e.g., for recommending a set of top-k items). The joint variational autoencoder network200is configured to capture user-brand interaction pattern and generate embeddings that provide better implicit understanding of the user and brand space and the non-trivial relations between entities within that space. In some embodiments, the joint variational autoencoder network200is configured to improve upon existing collaborative filtering based approaches for generating user and brand embeddings. Examples of collaborative filtering processes are described in U.S. Provisional Patent Application Ser. No. 63/264,925, entitled “Systems and Methods for Determining Temporal Loyalty,” filed Dec. 3, 2021, which is incorporated by reference herein in its entirety.

FIG.6illustrates an autoencoder model300configured to generate latent space representations having discrete values, in accordance with some embodiments. In some embodiments, the autoencoder model300includes an encoder portion302, a latent space representation (or vector)304, and a decoder portion306. The encoder portion302is configured to receive an input, such as, for example, an interaction matrix (e.g., matrix204illustrated inFIG.5) and/or a portion of an interaction matrix. The encoder portion302converts the received input into a latent space representation304. Similarly, the decoder portion306is configured to receive the latent space representation304and generate an output substantially equivalent to the input (e.g., the extracted row or column of an interaction matrix).

In some embodiments, the latent space representation304includes a vector representative of the input received at the input layer302. The latent space representation304may be extracted (e.g., by truncating the autoencoder model300to remove the decoder portion306). The autoencoder model300describes observations in latent space in a deterministic manner (e.g., discrete values). The autoencoder model300may be configured to generate a latent space representation304having multiple dimensions representing different learned attributes for a specific data set. In some embodiments, a deep joint variational autoencoder is used to convert input samples into encoding vectors having multiple probabilistic dimensions, as discussed below.

FIG.7illustrates a variational autoencoder model350(“VA model350”), in accordance with some embodiments. The VA model350is similar to the autoencoder model300described above, and similar description is not repeated herein. The VA model350includes an encoder portion352configured to generate vectors having a single value for each of a plurality of encoding dimensions learnt by the VA model350.

The encoder portion352of the VA model350includes multiple hidden encoding layers358a,358bconfigured to generate latent state representations360a,360b(collectively “latent space representations360”) including a plurality of latent state distributions. The latent state representations360a,360binclude probability distributions defined by, for example, mean, variance, and/or other values. In some embodiments, sample distributions364are obtained from the latent state representations360a-360band provided to a decoder portion356configured to generate output values370a-370f, which are expected to be accurate reconstructions of the input values364a-364f.

The latent space representations304,360generated by the autoencoder model300and/or the VA model350may be used by a second model, such as a trained inference model158, to perform personalized item recommendation, personalized search, and/or any other suitable item recommendation and/or personalization process.

FIG.8illustrates a process of encoding/decoding a given input sample380, in accordance with some embodiments. The input sample380is received by the encoding portion of a VA model, such as the encoding portion352of the VA model350. The encoding portion352generates a latent state representation360cincluding a set of latent state probabilities representing probabilistic ranges for a set of attributes (or dimensions) that have been learned for the dataset. In the illustrated embodiment, the latent space representation360cincludes six latent state probabilities, although it will be appreciated that any suitable number of latent state probabilities may be generated by an encoder portion of a VA model.

Sample sets382a,382bare obtained from the latent state probability distributions and are provided to the decoder portion, such as decoder portion356, which generates a set of output values384a,384b. The set of output values384a,384bare expected to be substantially similar to the input value380(i.e., the decoder portion352is configured to provide an accurate reconstruction from any sample from the latent state distributions). Because sampling generates similar latent state probability distributions, it is possible to generate latent state representations360for item and user pairs lacking data based on similar sample values from item and user pairs having similar distributions.

In some embodiments, variational inference may be performed to generate distribution likelihoods for hidden variables to generate latent space representations. The variational inference may be performed to maximize generation of the distribution likelihoods. For example, in some embodiments, a set of observable data x is provided and is related to a set of hidden variables z, where:
x={x1,x2, . . . ,xn}
z={z1,z2, . . . ,zn}
The probability of a hidden value z, given a value x, is represented by:

pϕ(z❘x)=p⁡(x❘z)⁢p⁡(z)p⁡(x)
where p(z|x) is the posterior distribution and p(z) is a prior distribution. This equation can be rewritten to provide the distribution p(x):
p(x)=∫p(x|z)p(z)dz
The distribution p(z|x) is intractable, e.g., cannot be solved. However, the distribution p(z|x) can be approximated as:
min KL(q(z|x)∥p(z|x))
where KL (Kullback-Leibler) divergence is a measure of two distributions, the distribution p(z|x) and a second distribution q(z|x) which is defined such that it has a tractable distribution. The parameters of q(z|x) are defined similar to p(z|x) such that q(z|x) may be used to perform an approximate inference of the intractable distribution p(z|x). In order to ensure p(z|x) and q(z|x) are similar, the KL divergence is minimized. In some embodiments, q(z|x) may be determined as:
qϕ(z|x)=(μϕ(x),σϕ2(x)I)
where the two multivariate functions μϕ(x) and σϕ2(x) map the input x to the mean and standard deviation vectors. The KL divergence may be minimized by maximizing:
Eq(z|x)logp(x|z)−KL(q(z|x))∥p(z))
wherein Eq(z|x)log p(x|z) represents the reconstruction likelihood for the variational autoencoder system and KL(q(z|x))μp(z)) provides that a learned distribution q is similar to the true prior distribution p. A loss function, L, can be represented as:
LVAE(x|θ)=−Eqϕ(Z|X)[logpψ(x|z)]+KL(qϕ(z|x))∥p(z))
where θ=[ψ, ϕ]. A regularization parameter, α, may be introduced such that:

LVAE(x❘θ,α)=-Eq⁢ϕ⁡(Z❘X)[log⁢pψ(x❘z)]+α⁢KL⁡(qϕ(z❘x))∥p⁡(z))⁢where:⁢log⁢pψ(x❘z)=∑ixi⁢log⁢σ⁡(oi)+(1-xi)⁢(1-σ⁡(oi))⁢where:⁢σ⁡(x)=1(1+exp⁡(-x))
is the logistic function. By minimizing the differences between the distributions with respect to item-item and user-user similarities, the KL divergence can be used to determine the likelihood a user uiwill interact with an item ij.

With reference again toFIGS.3and4, at step110, a set of top-k recommended items160is selected by the trained inference model158. The set of top-k recommended items160includes items ranked from, for example, highest to lowest, based on the likelihood of a user interacting with a specific item, such as illustrated in the output matrix214illustrated inFIG.5. The top-k recommended items may be generated by the inference model158using any suitable process, such as, for example, an embedding comparison process as known in the art.

At step110, the set of ranked items160is provided to additional systems and/or processes of a search backend. The additional systems and/or processes are configured to select a subset of the set of ranked items160for presentation to a user in response to the user's search query. Any suitable additional selection processes and/or criteria may be applied to the set of ranked items160to selected items for presentation to a user.

FIG.9is a flowchart illustrating a method400of generating user-specific predictions in a network interface environment, in accordance with some embodiments.FIG.10is a process flow450illustrating various steps of the method400, in accordance with some embodiments. At step402, a user identifier452is received by a suitable system, such as a network interface system24and/or an item recommendation system26. The user identifier452may be received, for example, from a user system22a,22b. The user identifier452may include any suitable identifier, such as, for example, a cookie, a beacon, a user log-in, an IP address, and/or any other suitable user identifier. In some embodiments, the user identifier452may be received by the network interface system24and subsequently provided to one or more other systems by the network interface system24, such as, for example, the item recommendation system26.

At step404, a set of prior user interactions454associated with the user identifier452are received by a suitable system, such as the item recommendation system26. The set of prior user interactions454includes a plurality of product interactions and time values for each product interaction for a specific user associated with the user identifier452. The product interactions may include, but are not limited to, item searches, add-to-cart interactions, product views, product purchases, user specific preferences, and/or any other suitable interaction. The set of prior user interactions454may be maintained by any suitable storage mechanism, such as an interaction database32. The set of prior user interactions454may be stored and/or retrieved in any suitable form, such as, for example, log files maintained by one or more systems within network20, such as, for example, the network interface system24. Although specific embodiments are discussed herein, it will be appreciated that any suitable data structure, format, location, etc. may be used to store prior user interactions.

At optional step406, the item recommendation system26generates and/or receives a set of candidate items456. The set of candidate items456may include one or more items458a-458fpreviously identified and/or selected for one or more items (or products) included in the set of prior user interactions454. For example, in various embodiments, the set of candidate items456may include items458a-458fthat are frequently purchased with, in the same category as, made by the same manufacturer as, and/or otherwise associated with a product in the set of prior user interactions454(e.g., complimentary items). The set of candidate items456may be generated using any suitable method, such as, for example, a trained neural network, clustering, and/or any other suitable mechanism. In some embodiments, the set of candidate items456may include latent space representations of the items generated using a trained inference model, such as the inference model158discussed above with respect toFIGS.3-4.

At step408, the set of prior user interactions454and the optional set of candidate items456are provided to a trained inference model158a. The trained inference model158amay be maintained by any suitable system, such as, for example, the item recommendation system26. The trained inference model158ais configured to generate item recommendations based on latent space representations of prior user interactions and/or candidate items, for example, according to the method100discussed above in conjunction withFIGS.3-4. The trained inference model158amay be retrieved from a production environment and implemented on one or more systems within a computing cluster.

At step410, the trained inference model158agenerates personalized item recommendations460. The personalized item recommendations460includes a set of ranked items462a-462ecorresponding to user interests based on various user interactions represented using latent space representations. For example, in some embodiments, the trained inference model158aincludes a neural network structure configured to compare latent space representations of items within the set of candidate items456to latent space representations of prior user interactions with the network interface system24. The trained inference model158acaptures intricate user relationships in a brand space and helps to capture a brand space, for example, by defining “similar” brands, “complementary” brands, etc. The generation of latent space representations using a deep learning based variational autoencoder process allows the disclosed systems and methods to derive non-obvious features from raw signals. In some embodiments, the set of ranked items462a-462eincludes items selected from the set of complimentary items458a-458f. In other embodiments, the set of ranked items462a-462eare selected and ranked by the trained inference model158afrom a set of all available items. The personalized item recommendations460may be provided to the network interface system24for presentation to a corresponding user system22a,22b.

The disclosed systems and methods for top-k item recommendation based on latent space representations provides an improvement over both manual feature selection processes and standard deep learning-based recommendations. The disclosed systems and methods enhance user experience by using latent space representations of users and attributes to identify relevant item recommendations. The user of latent space representations, as disclosed herein, captures intricate user relationships in a brand space and helps to capture a brand space, for example, by defining “similar” brands, “complementary” brands, etc. The generation of latent space representations using a deep learning based variational autoencoder process allows the disclosed systems and methods to derive non-obvious features from raw signals.

The disclosed systems and methods utilizing joint variational autoencoders provides end-to-end embedding generation and evaluation systems that jointly generate user and brand embeddings. The disclosed systems and methods improve both precision of recommendations and cold-start recommendation processes by generating embeddings for brands which do not have any prior interaction data.

Although the subject matter has been described in terms of exemplary embodiments, it is not limited thereto. Rather, the appended claims should be construed broadly, to include other variants and embodiments, which may be made by those skilled in the art.