Patent Publication Number: US-2018052929-A1

Title: Search of publication corpus with multiple algorithms

Description:
RELATED APPLICATION(S) 
     This application claims the benefit of priority under 35 U.S.C. 119(e) to Provisional Application No. 62/375,821, filed Aug. 16, 2016 and entitled “SEARCH OF PUBLICATION CORPUS WITH MULTIPLE ALGORITHMS,” which is incorporated herein by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     Embodiments of the present disclosure relate generally to indexing with multiple algorithms to improve search results of a large corpus. 
     BACKGROUND 
     Present indexing techniques index a corpus based on one algorithm or another. This results in search results that ignore the advantages of different indexing algorithms and fail to be comprehensive. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various ones of the appended drawings merely illustrate example embodiments of the present disclosure and cannot be considered as limiting its scope. 
         FIG. 1  is a block diagram illustrating a networked system, according to some example embodiments. 
         FIG. 2  is a block diagram illustrating operation of the intelligent assistant, according to some example embodiments. 
         FIG. 3  illustrates features of an artificial intelligence (AI) framework, according to some example embodiments. 
         FIG. 4  is a diagram illustrating a service architecture according to some example embodiments. 
         FIG. 5  illustrates a flow diagram of a method for training semantic machine learned models. 
         FIG. 6  illustrates a flow diagram of a method for using a forward semantic search index to find closest matches in a publication corpus in response to a user search. 
         FIG. 7  illustrates a block diagram of a method for making and using a reverse semantic search index to find closest matches in a publication corpus in response to a user search. 
         FIG. 8  illustrates a more detailed version of the block diagram  FIG. 7  of a method for making and using a reverse semantic search index to find closest matches in a publication corpus in response to a user search. 
         FIG. 9  illustrates a flow diagram of a method for trimming candidate publications to improve efficiency of finding closest matches in a publication corpus in response to a user search. 
         FIG. 10  illustrates a flow diagram of a method for using multiple types of reverse indexes to more comprehensively find closest matches in a publication corpus in response to a user search. 
         FIG. 11  illustrates an offline training process for search based on semantic vectorization. 
         FIG. 12  illustrates a runtime search process for search based on semantic vectorization. 
         FIG. 13  illustrates a query to publication retrieval process where multiple levels of relevance filtration/ranking are performed. 
         FIG. 14  is a block diagram illustrating a process of generating semantic forward and reverse indexes. 
         FIG. 15  is a block diagram illustrating an example of a software architecture that may be installed on a machine, according to some example embodiments. 
     
    
    
     The headings provided herein are merely for convenience and do not necessarily affect the scope or meaning of the terms used. 
     DETAILED DESCRIPTION 
     The description that follows includes systems, methods, techniques, instruction sequences, and computing machine program products that embody illustrative embodiments of the disclosure. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide an understanding of various embodiments of the inventive subject matter. It will be evident, however, to those skilled in the art, that embodiments of the inventive subject matter may be practiced without these specific details. In general, well-known instruction instances, protocols, structures, and techniques are not necessarily shown in detail. 
     Some embodiments generate a plurality of candidate publications in the publication corpus in response to a search query, comprising identifying multiple pluralities of candidate publications via multiple indexes that are mutually, independent. This results in search results that leverage the different advantages of different indexing algorithms, such that the search results are comprehensive. 
       FIG. 1  is a block diagram illustrating a networked system, according to some example embodiments. With reference to  FIG. 1 , an example embodiment of a high-level client-server-based network architecture  100  is shown. A networked system  102 , in the example forms of a network-based publication system, provides server-side functionality via a network  104  (e.g., the Internet or wide area network (WAN)) to one or more client devices  110 . One or more portions of network  104  may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, a wireless network, a Win network, a WiMax network, another type of network, or a combination of two or more such networks. The client device  110  may comprise, but is not limited to, a mobile phone, desktop computer, laptop, portable digital assistants (PDAs), smart phones, tablets, ultra books, netbooks, laptops, multi-processor systems, microprocessor-based or programmable consumer electronics, game consoles, set-top boxes, or any other communication device that a user may utilize to access the networked system  102 . In some embodiments, the client device  110  may comprise a display module (not shown) to display information (e.g., in the form of user interfaces). In further embodiments, the client device  110  may comprise one or more of a touch screens, accelerometers, gyroscopes, cameras, microphones, global positioning system (GPS) devices, and so forth. The client device  110  may be a device of a user that is used to perform a transaction involving digital publications within the networked system  102 . In one embodiment, the networked system  102  is a network-based publication system that responds to requests for listings or publications and publishes publications. Examples of publications are item listings for sale. 
     Each of the client device  110  may include one or more applications (also referred to as “apps”) such as, but not limited to, a web browser, messaging application, electronic mail (email) application, an e-commerce site application (also referred to as a marketplace application), and the like,  FIG. 1  illustrates, for example, a web client  112  (e.g., a browser, such as the Internet Explorer® browser developed by Microsoft® Corporation of Redmond, Wash. State), an application  114 , and a programmatic client (not shown) executing on the client device  110 . The web client  112  may access an intelligent personal assistant system  142  via the web interface supported by the web server  122 . Similarly, the programmatic client accesses the various services and functions provided by the intelligent personal assistant system  142  via the programmatic interface provided by the API server  120 . 
     In some embodiments, if the e-commerce site application is included in a given one of the client device  110 , then this application is configured to locally provide the user interface and at least some of the functionalities with the application configured to communicate with the networked system  102 , on an as needed basis, for data or processing capabilities not locally available (e.g., access to a publication database of items available for sale, to authenticate a user, to verify a method of payment). Conversely if the e-commerce site application is not included in the client device  110 , the client device  110  may use its web browser to access the e-commerce site (or a variant thereof) hosted on the networked system  102 . 
     One or more users  106  may be a person, a machine, or other means of interacting with the client device  110 . In example embodiments, the user  106  is not part of the network architecture  100 , but may interact with the network architecture  100  via the client device  110  or other means. For instance, the user provides input (e.g., touch screen input or alphanumeric input) to the client device  110  and the input is communicated to the networked system  102  via the network  104 . In this instance, the networked system  102 , in response to receiving the input from the user  106 , communicates information to the client device  110  via the network  104  to be presented to the user. In this way, the user  106  can interact with the networked system  102  using the client device  110 . 
     An application program interface (API) server  120  and a web server  122  are coupled to, and provide programmatic and web interfaces respectively to, one or more application servers  140 . The application server  140  host an intelligent personal assistant system  142 , which includes an artificial intelligence framework  144 , each of which may comprise one or more modules or applications and each of which may be embodied as hardware, software, firmware, or any combination thereof. 
     The application server  140  is, in turn, shown to be coupled to one or more database servers  124  that facilitate access to one or more information storage repositories or databases  126 . In an example embodiment, the databases  126  are storage devices that store information to be posted (e.g., publications or listings) to a publication system (not shown). The databases  126  may also store digital publication information in accordance with example embodiments. 
     Additionally, a third-party application  132 , executing on third-party servers  130 , is shown as having programmatic access to the networked system  102 . via the programmatic interface provided by the API server  120 . For example, the third-party application  132 , utilizing information retrieved from the networked system  102 , supports one or more features or functions on a website hosted by the third party. The third-party website, for example, provides one or more functions that are supported by the relevant applications of the networked system  102 . 
     Further, while the client-server-based network architecture  100  shown in  FIG. 1  employs a client-server architecture, the present inventive subject matter is of course not limited to such an architecture, and could equally well find application in a distributed, or peer-to-peer, architecture system, for example. The intelligent personal assistant system  142  can also be implemented as a standalone system, which does not necessarily have networking capabilities. 
       FIG. 2  is a diagram illustrating the operation of the intelligent assistant  142 , according to some example embodiments. Conventionally, one cannot really perform a search using natural language. For example, today&#39;s online shopping is impersonal, unidirectional, and keyword-based, which is inadequate for buyers to express their exact intent, and is much harder compared to shopping in a store with the assistance of a helpful salesperson. 
     Example embodiments present a personal assistant, also referred to as an intelligent assistant, that supports human-like dialog with the user, enabling delivery of more relevant and personalized search results. The natural, human-like experience increases the likelihood that the user will reuse the intelligent assistant for future searches. 
     The artificial intelligence framework  144  understands the user and the available publications (e.g., inventory) to respond to natural-language queries and has the ability to deliver incremental improvements in anticipating and understanding the user and the user&#39;s needs. 
     The artificial intelligence framework (AIF)  144  includes a dialogue manager  204 , natural language understanding (NLU)  206 , computer vision  208 , speech recognition  210  module, search module  218 , and orchestrator  220 . The ALE  144  is able to receive different kinds of inputs, such as text input  212 , image input  214 , voice input  216 , or any combination of the three, to generate relevant results  222 . As used herein, the AIF  144  includes a plurality of services (e.g., NLU  206 , computer vision  208 ) that are implemented by corresponding servers, and the terms service or server may be utilized to identity the service and the corresponding server. 
     A natural language understanding (NLU)  206  unit processes natural language text input  212 , both formal and informal language, detects the intent of the text, and extracts (e.g., parses) useful information, such as objects of interest and their attributes. The natural language text input  212  can thus be transformed into a structured query using rich information from additional knowledge to enrich the query even further. This information is then passed on to the dialog manager  204  through the orchestrator  220  for further actions with the user or with the other components in the overall system. The structured and enriched query is also consumed by the search  218  for improved matching. The text input  212  may be a query for a publication, a refinement to a previous query (e.g., size of shoes). 
     The computer vision  208  takes an image as an input and performs image recognition to identify characteristics of the image (e.g., object the user wants to search for), which are then transferred to the NLU  206  for processing. Similarly, the speech recognition  210  takes the speech  216  as an input and performs language recognition to convert speech to text, which is then transferred to the NLU  206  for processing. 
     The NLU  206  determines the object, the aspects associated with the object, how to create the search interface input, and how to generate the response. For example, the AIF  144  may ask questions to the user to clarify what the user is looking for. This means that the AIF  144  not only generates results, but also may create a series of interactive operations to get to relevant results  222 . 
     For example, in response to the query, “Can you find me a pair of red Nike shoes?” the AIF  144  may generate the following parameters: &lt;intent:shopping, statement-type:question, dominant-object:shoes, target:self, color:red, brand:nike&gt;. To the query, “I am looking for a pair of sunglasses for my wife,” the NLU may generate &lt;intent: shopping, statement-type: statement, dominant-object: sunglasses, target:wife, target-gender:female&gt;. In other example embodiments, digital embedding of image or voice data can be used directly, or in addition to text extraction, for search. 
     The dialogue manager  204  is the module that analyzes the query of a user to extract meaning, and determines if there is a question that needs to be asked in order to refine the query, before sending the query to the search  218 . The dialogue manager  204  uses the current communication in context of any previous communication between the user and the artificial intelligence framework  144 . The questions are automatically generated dependent on the combination of the accumulated knowledge (e.g., provided by a knowledge graph) and what the search  218  can extract out of the inventory. The dialogue manager  204  creates a response for the user. For example, if the user says, “hello,” the dialogue manager  204  generates a response, “Hi, my name is hot.” The dialogue manager  204  pulls predetermined responses from a database or table that correlates the input “hello” to the predetermined responses, or generate a response from a machine-learned model. 
     The orchestrator  220  coordinates the interactions between the other services within the artificial intelligence framework  144 . More details are provided below regarding the interactions of the orchestrator  220  with other services with reference to  FIG. 5 . 
       FIG. 3  illustrates the features of the artificial intelligence framework (AIF)  144 , according to some example embodiments. The AIF  144  is able to interact with several input channels  304 , such as native commerce applications, chat applications, social networks, browsers, etc. In addition, the AIF  144  understands intent  306  expressed by the user. For example, the intent  306  may include a user looking for a good deal, a user looking for a gift, a user on a mission to buy a specific product, a user looking for suggestions, etc. 
     Further, the AIF  144  performs proactive data extraction  310  from multiple sources, such as social networks, email, calendar, news, market trends, etc. The AIF  144  knows or has access to user details  312 , such as user preferences, desired price ranges, sizes, affinities, etc. The AIF  144  facilitates a plurality of services within the service network, such as search, recommendations, user profile/preference learning, checkout, etc. An output  308  may include search results, recommendations, checkout confirmation, etc. 
     The AIF  144  is an intelligent and friendly system that understands the user&#39;s intent (e.g., targeted search, compare, shop, browse), mandatory publication parameters (e.g., product, product category, item), optional publication parameters (e.g., aspects of the item, color, size, occasion), as well as implicit information (e.g., geo location, personal preferences, age, gender). The AIF  144  responds with a well-designed response in natural language. 
     For example, the AIF  144  may process input queries, such as: “Hey! Can you help me find a pair of light pink shoes for my girlfriend please? With heels Up to $200. Thanks;” “I recently searched for a men&#39;s leather jacket with a classic James Dean look. Think almost Harrison Ford&#39;s in the new Star Wars movie. However, I&#39;m looking for quality in a price range of $200-300. Might not be possible, but I wanted to see!;” or “I&#39;m looking for a black North face Thermoball jacket.” 
     Instead of a hardcoded system, the AIF  144  provides a configurable, flexible interface with machine learning capabilities for ongoing improvement. The AIF  144  supports a publication system that provides ease of use, value (e.g., connecting the user to the things that the user wants), intelligence (e.g., knowing and learning from the user and the user behavior to recommend the right publications), convenience (e.g., offering a plurality of user interfaces), and efficiency (e.g., saves the user time and money). 
       FIG. 4  is a diagram illustrating a service architecture environment  400  according to some embodiments. The service architecture environment  400  presents various views of the service architecture in order to describe how the service architecture may be deployed on various data centers or cloud services. The service architecture environment  400  represents a suitable environment for implementation of the embodiments described herein. 
     A service architecture  402  represents how a cloud architecture typically appears to a user, developer, and so forth. The service architecture  402  is generally an abstracted representation of the actual underlying architecture implementation, represented in the other views of  FIG. 1 . For example, the service architecture  402  comprises a plurality of layers that represent different functionality and/or services associated with the service architecture  402 . 
     An experience service layer  404  represents a logical grouping of services and features from an end user&#39;s point of view, built across different client platforms, such as applications running on a platform (e.g., mobile phone, desktop), web based presentation (e.g., mobile web, desktop web browser), and so forth. The experience service layer  404  includes rendering user interfaces and providing information to the client platform so that appropriate user interfaces can be rendered, capturing client input, and so forth. In the context of a marketplace, examples of services that would reside in this layer include, for example, a home page (e.g., home view), view publication, search/view search results, shopping cart, buying user interface and related services, selling user interface and related services, after sale experiences (e.g., posting a transaction; feedback), and so forth. In the context of other systems, the experience service layer  404  incorporates those end user services and experiences that are embodied by the system. 
     An API layer  406  contains APIs which allow interaction with business process and core layers. This allows third party development against the service architecture  402  and allows third parties to develop additional services on top of the service architecture  402 . 
     A business process service layer  408  is where the business logic resides for the services provided. In the context of a marketplace, this is where services such as user registration, user sign in, listing creation and publication, add to shopping cart, place an offer, checkout, send invoice, print labels, ship item, return item, and so forth are implemented. The business process service layer  408  also orchestrates between various business logic and data entities and thus represents a composition of shared services. The business processes in this layer can also support multi-tenancy in order to increase compatibility with some cloud service architectures. 
     A data entity service layer  410  enforces isolation around direct data access and contains the services upon which higher level layers depend. Thus, in the marketplace context, this layer comprises underlying services like order management, financial institution management, user account services, and so forth. The services in this layer typically support multi-tenancy. 
     An infrastructure service layer  412  comprises those services that are not specific to the type of service architecture being implemented. Thus, in the context of a marketplace, the services in this layer are services that are not specific or unique to a marketplace. Thus, functions like cryptographic functions, key management, CAPTCHA, authentication and authorization, configuration management, logging, tracking, documentation and management, and so forth reside in this layer. 
     Embodiments of the present disclosure will typically be implemented in one or more of these layers, such as the AIF  144 , as well as the orchestrator  220  and the other services of the AIF  144 . 
     A data center  414  is a representation of various resource pools  416  along with their constituent scale units. The data center  414  representation illustrates the scaling and elasticity that comes with implementing the service architecture  402  in a cloud computing model. The resource pool  416  is comprised of server (or compute) scale units  420 , network scale units  418  and storage scale units  422 . A scale unit is a server, network, or storage unit that is the smallest unit capable of deployment within the data center. The scale units  418 ,  420 , and  422  allow for more capacity to be deployed or removed as the need increases or decreases. 
     The network scale unit  418  contains one or more networks (e.g., network interface units) that can be deployed. The networks can include, for example virtual LANs. The compute scale unit  420  typically comprise a unit (e.g., server) that contains a plurality processing units, such as processors. The storage scale unit  422  contains one or more storage devices such as disks, storage attached networks (SAN), network attached storage (NAS) devices, and so forth. These are collectively illustrated as SANs in the description below. Each SAN may comprise one or more volumes, disks, and so forth. 
     The remainder of  FIG. 4  illustrates another example of a service architecture environment  400 . This view is more hardware focused and illustrates the resources underlying the more logical architecture in the other views of  FIG. 4 . A cloud computing architecture typically has a plurality of servers or other systems  424 ,  426 . These servers comprise a plurality of real or virtual servers. Thus the server  424  comprises server  1  along with virtual servers  1 A,  1 B,  1 C and so forth. 
     The servers are coupled to or interconnected by one or more networks such as network A  428  or network B  430 . The servers are also coupled to a plurality of storage devices, such as SAN  1  ( 436 ), SAN  2  ( 438 ) and so forth. SANs are typically coupled to the servers through a network such as SAN access A  432  or SAN access B  434 . 
     The compute scale units  420  are typically some aspect of servers  424  or  426 , like processors, and other hardware associated therewith. The network scale units  418  typically include, or at least utilize, the illustrated networks A ( 428 ) and B ( 432 ). The storage scale units typically include some aspect of SAN  1  ( 436 ) or SAN  2  ( 438 ). Thus, the logical service architecture  402  can be mapped to the physical architecture. 
     Services and other implementation of the embodiments described herein will run on the servers or virtual servers and utilize the various hardware resources to implement the disclosed embodiments. 
     In various embodiments a machine-learned model encodes a search query or a portion of a search query into a continuous, dimensional vector space where semantic level similar sequences will have closer representation in this vector space. This machine-learned model approach can automatically capture the deep latent semantic meaning of a publication title, a search query, a portion of a publication title, or a portion of a search query and project its semantic level meaning into a shared multi-dimensional vector space. Such a model can be adapted to encode additional information such as user profile and session context on the input side, and other publication attributes on the inventory side. 
     Deep learning has recently shown much promise in Natural Language Processing (NLP). NLP researchers in this area are trying various ways to encode a sequence of symbols (e.g., phrases, sentences, paragraphs, and documents) into a multi-dimensional vector space, called semantic space. Semantic level similar sequences will have closer representation in this multi-dimensional space. Research in this area has led to an adoption of vector space representations of sentences instead of just words. Generally, phrases or sentences better define the contextual information rather than a single word. In various embodiments, research in sentence embedding is leveraged to recommend publications of a seller on a publication system. 
     In an example embodiment, a machine-learned model is used to embed the deep latent semantic meaning of a given publication title and project it to a shared semantic vector space. A vector space can be referred to as a collection of objects called vectors. Vectors spaces can be characterized by their dimension, which specifies the number of independent directions in the space. A semantic vector space can represent phrases and sentences and can capture semantics for NLP tasks. In further embodiments, a semantic vector space can represent audio sounds, phrases, or music; video clips and images and can capture semantics for NLP tasks. 
     In various embodiments; machine learning is used to maximize the similarity between the source (X), for example, a publication title, and the target (Y), the search query. A machine-learned model may be based on deep neural networks (INN) or convolutional neural networks (CNN). The DNN is an artificial neural network with multiple hidden layers of units between the input and output layers. The DNN can apply the deep learning architecture to recurrent neural networks. The CNN is composed of one or more convolution layers with fully connected layers (such as those matching a typical artificial neural network) on top. The CNN also uses tied weights and pooling layers. Both the DNN and CNN can he trained with a standard backpropagation algorithm. 
       FIG. 5  illustrates a flow diagram of a method for training semantic machine learned models. 
     When a machine-learned model is applied to mapping a specific &lt;source, target&gt; pair, the parameters for machine-learned Source Model and machine-learned Target Model are optimized so that relevant &lt;source, target&gt; pair has closer vector representation distance. The following formula can be used to compute the minimum distance. 
     
       
         
           
             
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             TgtVec=a continuous vector representation for a target sequence (also referred to as semantic vector of the target). 
           
         
       
    
     The source machine-learned model encodes the source sequence into continuous vector representation. The target machine-learned model encodes the target sequence into a continuous vector representation. In an example embodiment, the vectors each have approximately 100 dimensions. 
     In other embodiments, any number of dimensions may be used. In example embodiments, the dimensions of the semantic vectors are stored in a KD tree structure. The KD tree structure can be referred to a space-partitioning data structure for organizing points in a KD space. The KD tree can be used to perform the nearest-neighbor lookup. Thus, given a source point in space, the nearest-neighbor lookup may be used to identify the closest point to the source point. 
     In  FIG. 5 , the left column is directed to training a machine learned model on search queries. In operation  510 , a training search query is input to a first machine learned model. In operation  520 , the training search query is processed by the first machine learned model of the neural network. In operation  530 , the first machine learned model outputs a training semantic search vector. 
     In  FIG. 5 , the right column is directed to training a machine learned model on publications. In operation  540 , a training publication is input to a second machine learned model. In operation  550 , the training publication is processed by the second machine learned model of the neural network. In operation  560 , the second machine learned model outputs a training publication semantic vector. 
     In operation  570 , the training semantic search vector and the training publication semantic vector are compared to adjust the first machine learned model and the second machine learned model, such that semantically similar training search queries and training publications result in respective semantic vectors that are proximate, and such that semantically dissimilar training search queries and training publications result in respective semantic vectors that are not proximate. 
       FIG. 6  illustrates a flow diagram for using a forward semantic search index to find closest matches in a publication corpus in response to a user search. 
     In operation  610 , a search query is received which is received from a device operated by a user searching for a publication listed in a publication corpus. In operation  620 , a semantic search vector is accessed that encodes the search query, such that the semantic search vector represents the semantic meaning of the search query. The semantic search vector is pre-generated and just accessed in one embodiment. In another embodiment, the semantic search vector is generated and then accessed. The semantic search vector is based on the entire search query, multiple terms of the search query, or a single term in the event of short search queries. 
     Alternative paths exist for finding the closest matches between search queries and publications. In operation  630 , the semantic search vector and publication vector are converted into binary representations. In operation  640 , the closest matches are identified between the binary representations via XOR operations (which can be XOR or XNOR operations). In another embodiment, the semantic search vector or the publication vector are formed as binary representations to make the binary conversion superfluous. Alternatively in operation  650 , closest matches are identified in a non-binary representation such as a floating point. In one embodiment, operation  650  gives higher quality results as operation  630  is not a lossless conversion. In one embodiment, operations  630 - 640  have sufficiently good results with a lower (and feasible) latency. 
     In operation  660 , the closest matches are sent to the device operated by the user,and in some embodiments are displayed on the device. In operation  670 , a selection signal is received, which is transmitted from the device operated by the user indicating a selection from the closest matches. 
       FIG. 7  illustrates a block diagram of making and using a reverse semantic search index to find closest matches in a publication corpus in response to a user search.  FIG. 7  shows word vector training and clustering  710 , reverse index generation  720 , and run-time recall retrieval  730 . 
     A word vector based reverse-indexing approach captures various synonyms for a search query but without the costly requirement of maintaining a big synonym dictionary for every possible keyword. Keywords in the search query and publications are converted into semantic vectors. Those semantic vectors are grouped into a finite number of clusters in word vector training and clustering  710  and then used in reverse index generation  720 . Based on those clusters of semantic vectors, a list of publications are fetched based on any search query in run-time recall retrieval  730 . 
     In contrast with  FIG. 6 , in  FIG. 7  the semantic vector is a vector representing a single keyword in a search query or publication. For example, a search query “red namebrand shoes” has a single semantic vector [1, 0, 2.4. 3.0]. While it has 3 different word vectors: “red” has a semantic vector of [3.2, 1.1, 0.5], “namebrand” has semantic vector of [−0.3, 2.6, 1.1], and “shoes” has a semantic vector of [1.3, 2.4, 3.5]. In other embodiments, the semantic vector is a vector representing multiple keywords in a search query or publication, but less than the whole search query or publication. 
       FIG. 8  illustrates a more detailed version of the block diagram  FIG. 7  of making and using a reverse semantic search index to find closest matches in a publication corpus in response to a user search. The block diagram has primary components of word semantic vector training and clustering  810 , reverse index generation  820 , and run-time recall retrieval  830 . Each primary block is discussed in turn. 
     In the beginning of semantic vector training and clustering  810 , a publication corpus is collected  811 . A Word Vec or other tool performs word embedding  812  for every keyword entry in the vocabularies within the search query corpus and publication title corpus to make a word embedding dictionary  813 . Clustering technology such as k-means clustering groups those word semantic vectors into a finite number of clusters based on their distance to each other  814 . The result is a word vector cluster dictionary  815 . 
     In the beginning of reverse index generation  820 , publication IDs are accessed  821 , and keywords are extracted from the publication  822 . For each keyword in the publication title, the encoding word vector value and is accessed and then the cluster ID is accessed  823 . The publication ID is added to that cluster ID  824 . After all keywords are processed, the word2vec reverse index is finalized  825 . 
     For example, publication ID 12345 has a title of: “red athletic shoes” with keywords: red, athletic, and shoes. The encoding word vector clusterIDs are C1, C5, and C5. Thus, a word2vec reverse-index includes: 
     C1: 12345, (other PublicationIDs containing a word vector belonging to C1 cluster) 
     C5: 12345, (other PublicationIDs containing a word vector belonging to C5 cluster) 
     In the beginning of run-time recall retrieval  830 , at search run-time, a search query is received  831 . Keywords are extracted from the search query  832 . The encoding word vector value and its cluster ID are accessed for each keyword  833 . The clusterID is used to access PublicationIDs of publications  834 , and the candidate publications are merged  835 . 
     For example, a search query is “red running shoe” with keywords red, running, and shoe. The encoding word vector cluster IDs are: C1, C5 and C5. The publications are retrieved from in the reverse index for clusterID entries C1 and C5. The resulting PublicationIDs (and any others belonging to cluster IDs C1 and C5) are retrieved and merged. 
       FIG. 9  illustrates a flow diagram of a method for trimming candidate publications to improve the efficiency of finding closest matches in a publication corpus in response to a user search. 
     At operation  910 , for a keyword among potential search keywords, candidate publications are identified in the publication corpus. At operation  915 , this is iterated for other keywords among potential search keywords. 
     At operation  920 , for a keyword among the publication corpus, candidate publications are identified in the publication corpus. At operation  925 , this is iterated for other keywords among potential search keywords. 
     At operation  930 , the candidate publications are aggregated from operations  910  and  920 . At operation  940 , candidate publications are trimmed, based on (i) machine-learned model of keywords to relevant publications or (ii) historic user behavior such as purchase, selection, and/or mouse or other control signal. Such trimming may occur offline. 
     At operation  950 , a search query is received from a device operated by a user searching for a publication listed in a publication corpus. At operation  960 , responsive to the search query, the trimmed aggregated candidate publications are processed for closest matches with the search query. Because of the trimming, finding matches with the search query among the publications is faster. 
       FIG. 10  illustrates a flow diagram of a method for using multiple types of reverse indexes to more comprehensively find closest matches in a publication corpus in response to a user search. 
     At operation  1005 , a search query is received which was received from a device operated by a user searching for a publication listed in a publication corpus. 
     At operation  1010 , for a keyword among potential search keywords, candidate publications are identified in the publication corpus with a semantic vector-based reverse index. At operation  1015 , this is iterated for other keywords among potential search keywords. 
     At operation  1020 , for a keyword among potential search keywords, candidate publications are identified in the publication corpus with another reverse index. At operation  1025 , this is iterated for other keywords among potential search keywords. 
     At operation  1030 , candidate publications are aggregated from operations  1010  and  1020 . At operation  1040 , a search query is received from a device operated by a user searching for a publication listed in a publication corpus. At operation  1050 , in response to the search query, the aggregated candidate publications are processed for closest matches with the search query. Because of the multiple indexes of different types, the search results are more comprehensive. 
     Other example indexes comprise:
     (i) N-gram based which is inclusive in what is indexed, but in some embodiments does not retain semantic meaning.   (ii) Name entity based which has more structure than N-gram, but in some embodiments does not understand semantic meaning.   (iii) Head query based memorization which saves time and is accurate due to the system relying on prior input from the user device. However, because head query based memorization is based on prior input from the user device, head query based memorization has limited coverage, is less adaptive, and does not cover new publications or new publication types that have not been the subject of prior input from the user device.   (iv) A variant of (iii) where and index of head to relevant publications are pre-populated and updated near-real-time.   

       FIG. 11  illustrates an offline training process for search based on semantic vectorization. 
     Referring to  FIG. 11 , the source machine-learned model and the target machine-learned model are trained. In a bottom portion, training data flow  1103  accesses training data. In a left column, a search query flow  1123  trains the machine-learned model for search queries. In a right column a publication flow  1124  trains the machine-learned model for publications. Labeled training data pairs (e.g., search query, publication) are provided for training both the first machine-learned model and the second machine-learned model. 
     In the bottom portion for the training data flow  1103 , training data is accessed. In  1110 , a historic user behavior data table is accessed, which has &lt;query, publication&gt; value pairs. The historic user behavior data table may include data such as impression count, click count, watch count, adding to purchase cart count, and purchase count. 
     In  1120 , out of the &lt;query, publication&gt; value pairs from the historic user behavior data table, relevant pairs are accessed from a pre-determined or selectable time period (e.g., the last 4 weeks). 
     In the left column for the search query flow  1123 , the search query (Y) is received, shown as an example query  1125  of a user search query of a wireless baby monitor. Word hashing is performed on the search query (Y) with a raw sentence sequence  1130 . In situations where there is a very large vocabulary word, hashing is performed on a sub-word unit. In various embodiments, in  1140  letter 3-gram word hashing is performed. 
     In the search query flow  1123 , a deep neural network (DNN) is used to extract semantic vector representations of the search query (Y). The DNN uses more than one neural network layer to project input sequences into a semantic vector space. In an example embodiment, a convolution layer  1150 , a maximum pooling layer  1160 , and a semantic layer  1170  represent neural network layers. Some embodiments also have a binary classifier converting a floating point vector in to a binary representation. A number of nodes (e.g., 100 nodes, 500 nodes, and multiple groups of 500 nodes as shown) may be configured in those neural network layers. In other embodiments, the number of nodes may be changed or configured to a different number, depending on the data size. Keywords and concepts are identified from the search query (Y) using convolution and max-pooling. 
     In the right column for the publication flow  1124 , a publication (X) is received, shown as an example publication  1126  with a seller publication title of video monitors or a white wireless video baby monitor. For an example embodiment, a publication system is used to list publications and may include millions of publications including example publication  1126 . Word hashing may be performed on the target publication with a raw sentence sequence  1130 . In situations where there is a very large vocabulary word, hashing is performed on a sub-word unit. In various embodiments, in  1140  letter 3-gram word hashing is performed. 
     In the publication flow  1124 , a deep neural network (DNN) is used to extract semantic vectors representations of the target publication (X). The DNN uses more than one neural network layer to project input sequences into a semantic vector space. In an example embodiment, the convolution layer  1150 , the maximum pooling layer  1160 , and the semantic layer  1170  represent neural network layers. Some embodiments also have a binary classifier converting the floating point vector into a binary representation. A number of nodes (e.g., 100 nodes, 500 nodes, and multiple groups of 500 nodes) may be configured in those neural network layers. In other embodiments, the number of nodes may be changed or configured to a different number, depending on the data size. Keywords and concepts are identified from the target publication (X) using convolution and max-pooling. 
     Finally, at  1180  semantic vector distance between X and Y is used to measure the similarity between the semantic vectors representations of the search query (Y) and the semantic vector representations of publications (X). In an example embodiment, the semantic relevance, represented by the function sim (X, Y), is measured by cosine similarity. In an embodiment with binary representations of the semantic vectors, XOR operations are performed. 
     When both the source machine-learned model and the target machine-learned model are trained, the semantic vector representations for all of the search query entries can be pre-computed in advance using the machine-learned model. Additionally, when there is a need to map any new publication from a seller, the semantic vector representation of the publication can be projected into shared semantic vector space with the semantic vectors representations of the publication. 
     As indicated above, when the machine-learned model is applied to mapping a specific &lt;source sequence, target sequence&gt; pair, the parameters for machine-learned Source Model and machine-learned Target Model are optimized so that relevant &lt;source, target&gt; pair has closer vector representation distance. The following formula can be used to compute the minimum distance. 
     
       
         
           
             
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             ScrSeq=a source sequence; 
             TgtSeq=a target sequence; 
             SrcMod=source machine-learned model; 
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             SrcVec=a continuous vector representation for a source sequence (also referred to the semantic vector of the source); and 
             TgtVec=a continuous vector representation for a target sequence (also referred to as semantic vector of the target). 
           
         
       
    
     The source machine-learned model encodes the source sequence into a continuous vector representation. The target machine-learned model encodes the target sequences into a continuous vector representations. In an example embodiment, the vectors each have approximately 100 dimensions. Alternatively, XOR operations can be performed on the binary representations of the semantic search vector and the publication vectors. 
     The results of the training process are two optimized neural network models—one the query model, another the publicationTitle model). 
     When a relevant pair of &lt;query, publicationTitle&gt; is input to the &lt;query_model, publicationTitle_podel&gt; respectively, the inferenced semantic vector pair &lt;query_semantic_vector, publicationTitle_semantic_vector&gt; will have a closer distance. 
     When a non-relevant pair of &lt;query, publicationTitle&gt; is input to the &lt;query_model, publicationTitle_model&gt; respectively, the inferenced semantic vector pair &lt;query semantic vector, publicationTitle semantic vector&gt; will have a larger distance. 
       FIG. 12  illustrates a runtime search process for search based on semantic vectorization. The left column is directed to the addition of a publication to the publication corpus. The right column is directed to searching for a publication in a publication corpus, in response to a search query transmitted from a device operated by a user. 
     Addition of a publication to the publication corpus is covered generally at  1202  to  1212 . At  1202 , a new publication X is added to the publication website or other storage. At  1204 , the information of the new publication X is parsed into its components, such as title, description, and aspects. At  1206 , sequence semantic embedding (SSE) ncodes publication titles (e.g., title keywords for publications being listed) and category tree paths into semantic vector representations as &lt;source sequence, target sequence&gt; pairs. In various embodiments, SSE is used to encode a sequence of symbols (e.g., a phrase, a sentence or a paragraph) into a continuous, dimensional vector space where semantic level similar sequences will have closer representation in this semantic vector space. This SSE approach can automatically capture the deep latent semantic meaning of a publication title and project its semantic level meaning into a shared multi-dimensional vector space. 
     At  1208 , a semantic vector is generated for publication X, applying the SSE approach to the remainder of publication X, such as the description and aspects. At  1210 , publication X is inserted into one or more forward indexes. At  1212 , all live publication vectors are stored in memory to reduce access times. 
     Searching for a publication in a publication corpus, in response to a search query transmitted from a device operated by a user, is generally covered at  1230  to  1242 . At  1230 , a search query Y is received from a user device. At  1232 , sequence semantic embedding (SSE) query elements into semantic vector representations as &lt;source sequence, target sequence&gt; pairs. At  1234 , a semantic vector is generated for search query Y, applying the SSE approach to the search query Y. 
     At  1220 , L0 filtering is performed using both candidate publications X from and on search query Y. L0 filtering is shown in more detail at  FIG. 13 . At  1236 , semantic vector distance between X and Y is used to measure the similarity between the semantic vectors representations of the search query (Y) and the semantic vector representations of publications (X). In an example embodiment, the semantic relevance, represented by the function sim (X, Y), is measured by cosine similarity. In an embodiment with binary representations of the semantic vectors, XOR operations are performed. 
     At  1238 , the best matched publication X to the search query Y is identified, according to the shortest distance in semantic space between semantic vectors of candidate publications X and the semantic vector of search query Y. 
     Certain publication updates impact the search index. The resulting search index update is performed similarly to a removal and a subsequent addition. 
       FIG. 13  illustrates a query to publication retrieval process where multiple levels of relevance filtration/ranking are performed. 
     At  1302 , a search query is received from a user device. At  1304 , the search query is processed according to one or more algorithms, such as artificial intelligence (AI), named entity recognition (NER), or natural language understanding (NLU). 
     At  1310 , L0 filtration is performed. Responsive to the processed search query from  1304 , various components assist with gathering candidate publications to match against the search query. 
     At  1312 , semantic vector category mapping maps a search query to most relevant categories. Similar publications are placed in the same category. For example, a cellphone is listed under the “smartphone” category; while a book is listed under the “book” category. By applying the categorization block, search engine performance is much faster and returns more relevant results to the user. For example, if a user search query is “Smartphone X development,” without applying the categorization block, the search engine can search against a large quantity (e.g., 600 million+) of publications for those keywords and might return some publications that are “Smartphone X” instead of a book about “Smartphone X development.” After applying categorization, the “Category classification” block knows that this search query is about a book, and thus guides the search engine to only search a small quantity (e.g., around 100K) of publications under the book category for those keywords and return more relevant publications about the books of “Smartphone X development.” This block thus significantly improves search engine&#39;s speed and accuracy. 
     At  1314 , a semantic word2vector index is applied to a reverse semantic search index to extend query expansion. By adding search words that are semantically related to query words, the recall set of candidate publications is increased. 
     At  1316 , query rewrite increases a search engine&#39;s recall set by capturing as many synonyms as possible. For example, if a search query is “sneakers for men” and a publication has a publication title of “running shoes,” then without query rewrite, the publication will not be returned to the user because the publication title does not contain any keywords in the search query. So a synonym dictionary captures synonyms for keywords. For example, “sneakers,” “running shoes,” and “athletic shoes” are in a same synonym dictionary entry as having the same semantic meaning. “Query Rewrite” re-writes/expands the raw search query into multiple possible search queries according to the synonym dictionary. For example, the “sneakers for men” is rewritten/expanded into three search queries: “sneakers for men,” “running shoes for men,” “athletic shoes for men.” The search engine returns publications which match with any of these rewrittten/expanded search queries. By applying this “Query Rewrite” block, a publication with the publication title “running shoes” is returned to user even if its title does not contain any keywords in the raw search query. 
     At  1318 , candidate publications are gathered into a reverse index publication list partitioned by category. The reverse index is generated from categorization service  1312 , semantic word2vec reverse index  1314 , and query rewrite  1316 . At  1320 , a top number (e.g., top 1000) of candidate publications are selected from the reverse index of  1318 . A publication has a publicationID, an L0 score, and a BM25 score. The ranking is based on, for example, L0 score and BM25 score. 
     L1 ranking  1330  is performed after L0 filtration  1310 . At  1332 , a forward index is generated of semantic vectors for the candidate publications from  1320 . The semantic vector mapping assists with mapping a query to a title at the sentence level and with ranking. In the semantic vector forward-index case of a collection of key-value pairs, the “key” is the Publication:ID of the publication, and the “value” is the semantic vector. Examples with a limited number of semantic vector dimensions are:
     Publication123: [0.2, 1.5, −3.2]   Publication456: [0.5, 3.2, −2.1]   

     In one example, the semantic vector is a 200 bit floating point vector. Other examples have a different number of bits, and/or different representations such as fixed point. In one example, the semantic vector is generated from the machine learned model trained in the publication flow  1124  of  FIG. 11 . At  1334 , the query from  1304  is vectorized for comparison with the publication semantic vectors. In one example, the semantic vector is a 200 bit floating point vector. Other examples have a different number of bits, and/or different representations such as fixed point. In one example, the semantic vector is generated from the machine learned model trained in the search query flow  1123  of  FIG. 11 . 
     L2 aspect filtration  1340  is performed after L1 ranking  1330 . At  1342 , a forward aspect index  1342  extends beyond aspects provide by seller to find the most relevant candidate publications. Additional aspects are found from the publication via NER (name entity recognition). 
     In the publication aspect forward-index case of a collection of key-value pairs, the “key” is the PublicationID, and the “value” is some aspect associated with the publication. An example is:
     Publication123: [color_red, type_shoes, brand_shoebrand, . . . ]   

     L3 final ranking  1350  is performed after L2 aspect filtration  1340 . The L3 ranking module  1352  is a machine-learned model that takes inputs such as historic user behavior data, L0 score, BM25 score, L1 score, reputation score, value, publication condition, user profile, session context, and demotion signal. Finally, at  1360 , the most relevant N publications are returned to the user device, based on L3 ranking  1350 . 
       FIG. 14  is a block diagram illustrating the process of generating, from the publications, semantic forward and reverse indexes used in connection with other figures. The forward indexes have key-value pairs with the PublicationID as the key. The reverse indexes have key-value pairs with the PublicationID as the value. 
     The semantic forward and reverse indexes originate from data of publications of a publication corpus  1402 , such as title, category, images, aspects, publication party such as a seller, etc. The publication data  1402  is processed by a NER (name entity recognition service)  1412  that extracts from publication entities information such as color, brand, type; an image recognition service  1416  that converts images to semantic vectors and text; and a semantic vectorization service  1414  that converts publications to a semantic vector. 
     Reverse indexes  1422  of collections of key-value pairs include: 
     (i) N-gram reverse index  1424  where the “key” is the keyword, and the value is a list of PublicationIDs of publications containing this keyword. A simple example with the keyword “red” is:
         red: [publication123, publication456, . . . ]       

     (ii) Entity-reverse index  1426  where the “key” is the entity word, and the value is a list of PublicationIDs containing of publications this entity word. A simple example with an aspect of the publication is:
         type_shoes: [publication123, publication456, . . . ]       

     (iii) Word2Vec-reverse index  1428  where the “key” is the word vector&#39;s clusterID, and the value is a list of PublicationIDs which contain a keyword whose word-vector is this clusterID. A simple example is:
         word_vector_cluster#123: [publication342, publication456, . . . ]       

     Reverse index  1429  combines n-gram reverse index  1424 , entity-reverse index  1426 , and word2Vec-reverse index  1428 . Reverse index  1429  is partitioned by publication category. 
     The forward indexes  1432  of collections of key-value pairs include: 
     (i) Forward semantic vector index  134  for publications in the publication corpus. Each publication in the publication corpus is represented by a semantic vector, such a vector of 200 bit floating point value. 
     (ii) Forward image index  1436  derived from publication images. 
     (iii) Forward aspect index  1438  derived from publication aspects. 
     Example Machine Architecture and Machine-readable Medium 
       FIG. 15  is a block diagram illustrating components of a machine  1500 , according to some example embodiments, able to read instructions from a machine-readable medium (e.g., a machine-readable storage medium) and perform any one or more of the methodologies discussed herein. Specifically,  FIG. 15  shows a diagrammatic representation of the machine  1500  in the example form of a computer system, within which instructions  1510  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  1500  to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions  1510  may cause the machine  1500  to execute the flow diagrams of  FIGS. 5-10 . Additionally, or alternatively, the instructions  1510  may implement the servers associated with the services and components of the  FIGS. 1-14 , and so forth. The instructions  1510  transform the general, non-programmed machine  1500  into a particular machine  1500  programmed to carry out the described and illustrated functions in the manner described. 
     In alternative embodiments, the machine  1500  operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine  1500  may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine  1500  may comprise, but not be limited to, a switch, a controller, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions  1510 , sequentially or otherwise, that specify actions to be taken by the machine  1500 . Further, while only a single machine  1500  is illustrated, the term “machine” shall also be taken to include a collection of machines  1500  that individually or jointly execute the instructions  1510  to perform any one or more of the methodologies discussed herein. 
     The machine  1500  may include processors  1504 , memory/storage  1506 , and I/O components  1518 , which may be configured to communicate with each other such as via a bus  1502 . In an example embodiment, the processors  1504  (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor  1508  and a processor  1512  that may execute the instructions  1510 . The term “processor” is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although  FIG. 15  shows multiple processors  1504 , the machine  1500  may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof. 
     The memory/storage  1506  may include a memory  1514 , such as a main memory, or other memory storage, and a storage unit  1516 , both accessible to the processors  1504  such as via the bus  1502 . The storage unit  1516  and memory  1514  store the instructions  1510  embodying any one or more of the methodologies or functions described herein. The instructions  1510  may also reside, completely or partially, within the memory  1514 , within the storage unit  1516 , within at least one of the processors  1504  (e.g., within the processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  1500 . Accordingly, the memory  1514 , the storage unit  1516 , and the memory of the processors  1504  are examples of machine-readable media. 
     As used herein, “machine-readable medium” means a device able to store instructions and data temporarily or permanently and may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., Erasable Programmable Read-Only Memory (EEPROM)), and/or any suitable combination thereof. The term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store the instructions  1510 . The term “machine-readable medium” shall also be taken to include any medium, or combination of multiple media, that is capable of storing instructions (e.g., instructions  1510 ) for execution by a machine (e.g., machine  1500 ), such that the instructions, when executed by one or more processors of the machine (e.g., processors  1504 ), cause the machine to perform any one or more of the methodologies described herein. Accordingly, a “machine-readable medium” refers to a single storage apparatus or device, as well as “cloud-based” storage systems or storage networks that include multiple storage apparatus or devices. The term “machine-readable medium” excludes signals per se. 
     The I/O components  1518  may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components  1518  that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components  1518  may include many other components that are not shown in  FIG. 15 . The I/O components  1518  are grouped according to functionality merely for simplifying the following discussion, and the grouping is in no way limiting. In various example embodiments, the I/O components  1518  may include output components  1526  and input components  1528 . The output components  1526  may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components  1528  may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instruments), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like. 
     In further example embodiments, the I/O components  1518  may include biometric components  1530 , motion components  1534 , environmental components  1536 , or position components  1538  among a wide array of other components. For example, the biometric components  1530  may include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram based identification), and the like. The motion components  1534  may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components  1536  may include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detect concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components  1538  may include location sensor components (e.g., a Global Position System (GPS) receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components magnetometers), and the like. 
     Communication may be implemented using a wide variety of technologies. The I/O components  1518  may include communication components  1540  operable to couple the machine  1500  to a network  1532  or devices  1520  via a coupling  1524  and a coupling  1522 , respectively. For example, the communication components  1540  may include a network interface component or other suitable device to interface with the network  1532 . In further examples, the communication components  1540  may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices  1520  may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB). 
     Moreover, the communication components  1540  may detect identifiers or include components operable to detect identifiers. For example, the communication components  1540  may include Radio Frequency Identification (RNID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components  1540 , such as location via Internet Protocol (IP) geo-location, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth. 
     In various example embodiments, one or more portions of the network  1532  may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), the Internet, a portion of the Internet, a portion of the Public Switched. Telephone Network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, the network  1532  or a portion of the network  1532  may include a wireless or cellular network and the coupling  1524  may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or another type of cellular or wireless coupling. In this example, the coupling  1524  may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1xRTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long range protocols, or other data transfer technology. 
     The instructions  1510  may be transmitted or received over the network  1532  using a transmission medium via a network interface device (e.g., a network interface component included in the communication components  1540 ) and utilizing any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions  1510  may be transmitted or received using a transmission medium via the coupling  1522  (e.g., a peer-to-peer coupling) to the devices  1520 . The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying the instructions  1510  for execution by the machine  1500 , and includes digital or analog communications signals or other intangible media to facilitate communication of such software. 
     Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code embodied on a machine-readable medium) or hardware modules. A “hardware module” is a tangible unit capable of performing certain operations and may be configured or arranged in a certain physical manner. In various example embodiments, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein. 
     In some embodiments, a hardware module may be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware module may include dedicated circuitry or logic that is permanently configured to perform certain operations. For example, a hardware module may be a special-purpose processor, such as a Field-Programmable (late Array (FPGA) or an Application Specific Integrated Circuit (ASIC). A hardware module may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware module may include software executed by a general-purpose processor or other programmable processor. Once configured by such software, hardware modules become specific machines (or specific components of a machine) uniquely tailored to perform the configured functions and are no longer general-purpose processors. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations. 
     Accordingly, the phrase “hardware module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. As used herein, “hardware-implemented module” refers to a hardware module. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where a hardware module comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising different hardware modules) at different times. Software accordingly configures a particular processor or processors, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time. 
     Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) between or among two or more of the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). 
     The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented module” refers to a hardware module implemented using one or more processors. 
     Similarly, the methods described herein may be at least partially processor-implemented, with a particular processor or processors being an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented modules. Moreover, the one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an Application Program Interface (API)). 
     The performance of certain of the operations may be distributed among the processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processors or processor-implemented modules may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the processors or processor-implemented modules may be distributed across a number of geographic locations. 
     The modules, methods, applications and so forth described herein are implemented in some embodiments in the context of a machine and an associated software architecture. The sections below describe representative software architecture(s) and machine (e.g., hardware) architecture that are suitable for use with the disclosed embodiments. 
     Software architectures are used in conjunction with hardware architectures to create devices and machines tailored to particular purposes. For example, a particular hardware architecture coupled with a particular software architecture will create a mobile device, such as a mobile phone, tablet device, or so forth. A slightly different hardware and software architecture may yield a smart device for use in the “internet of things.” While yet another combination produces a server computer for use within a cloud computing architecture. Not all combinations of such software and hardware architectures are presented here as those of skill in the art can readily understand how to implement the invention in different contexts from the disclosure contained herein. 
     Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein. 
     Although an overview of the inventive subject matter has been described with reference to specific example embodiments, various modifications and changes may be made to these embodiments without departing from the broader scope of embodiments of the present disclosure. Such embodiments of the inventive subject matter may be referred to herein, individually or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single disclosure or inventive concept if more than one is, in fact, disclosed. 
     The embodiments illustrated herein are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. The Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled. 
     As used herein, the term “or” may be construed in either an inclusive or exclusive sense. Moreover, plural instances may be provided for resources, operations, or structures described herein as a single instance. Additionally, boundaries between various resources, operations, modules, engines, and data stores are somewhat arbitrary, and particular operations are illustrated in a context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within a scope of various embodiments of the present disclosure. In general, structures and functionality presented as separate resources in the example configurations may be implemented as a combined structure or resource. Similarly, structures and functionality presented as a single resource may be implemented as separate resources. These and other variations, modifications, additions, and improvements fall within a scope of embodiments of the present disclosure as represented by the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.