Indexing and presentation of new digital content

Systems and methods are disclosed for a system configured to access first content to determine that textual features common to queries received in a website are available in text within the first content. An identifier within the first content is determined where the identifier references second content that comprises details associated with the textual features. In response to a content query including the textual features received in the website, display content is generated. The display content includes a first area to display matching content indexed from prior user searches and also includes a second area to be modified. A portion of the details and the identifier is provided in the second area.

BACKGROUND

Cold start for new items refers to the unavailability of behavioral context for newly released items—either products or services. A computer and provided software are unaware of newly released items. As a result, content engines may not list new items in a content display, even if a user is seeking additional information for the newly released items based in part on their knowledge. While crawling is provided to index new webpages, an absence of context may render such a process meaningless. As result webpages are static, there may be no electronic real estate to support new item discovery. In addition, when the result webpages are static, they include content popular within the website to which the search is made—setting aside new content.

DETAILED DESCRIPTION

Systems and methods in accordance with various embodiments of the present disclosure may overcome one or more of the aforementioned and other deficiencies experienced in conventional approaches to indexing and providing content for newly released products in an electronic environment. In particular, the example systems and methods herein determine that a rate of receipt of queries at a website is higher than a rate threshold. Such a determination may be indicative of spike in interest in a new item from users aware of the new item. However, electronic media may suffer from information asymmetry and may not have the ability to contextually identify that new webpages relate to new items. The present disclosure provides configuration to a computer system that enables determination of the spike in interest, then enables crawling and analysis of news and social media content to determine that textual features common to the queries from the spike in interest are available within news or social media content. For example, a new product called MODEL ONE—a drone toy—which is newly released by the ACME company may be discussed among individuals and described in press releases and news website, but may not be available to a content provider of an entity unrelated to the ACME company and its retailers or distributors (e.g., as an electronic marketplace or auction website). In the alternative, the new product represents content that may exist in the content provider as a new entry but may not be provided to users because it was never purchased or recognized by a user interaction from prior queries.

In an aspect, when queries spike in the electronic marketplace or auction website, common textual features in the queries are used as a basis to crawl and analyze news or social media content to determine if any of the news or social media content include the common textual features. Such news or social media content, when found, are likely to describe new content that is subject in the queries. The news or social media content reflect webpages that include the common textual features that may be crawled for identifiers, such as hyperlinks. The identifiers within the individual one of the news or social media content may provide access to additional content that are specifications and/or product release information (e.g., hyperlinks to a manufacturer's or retailer's product release webpages). The specifications and/or product release information provide content details for the new content that is intended as responsive to the queries. The computer system may be configured to index one or more of the identifiers and the related content or a portion of the information contained therein. Then, in response to a content query that is determined as to the newly released content, subsequent or concurrent with the spike, the website receiving the content query is able to be responsive and generate display content with the new content (e.g., the identifier, the related content, or the portion of the information contained therein). For example, instead of a static results webpage, the display content is dynamic and includes areas existing content (from prior user searches), but also with an additional area for dynamic modification to comprise the new content. Alternatively, a ranking of matching content indexed from prior user searches in the website may be provided. A modification of the ranking occurs so that the new content is placed in an intervening position in the ranking for the display content.

Various other functions can be implemented within the various embodiments as well as discussed and suggested elsewhere herein.

FIG. 1illustrates an example of display content102that can be presented in an application100on a screen of a computer in accordance with various embodiments. In an example, the display content102is a window, a website, or application screen of a web browser or a stand-alone application for interacting with the display content and for executing dynamic scripts within the display content. A user may search or interact with the display content via a search field108. The content may be products, items, services, or electronic media. A query118is provided in the search field108in one implementation of the searching or the interaction with display content. When the submit option is selected, the search may be initiated and processed on the computer or on a server as discussed subsequently in this disclosure. In example100ofFIG. 1A, a query118for “ACME MODEL ONE” is entered into the search field108, and the display content is provided with a results section104—e.g., a webpage or display content102. As illustrated, there are three search results110-114in the display content, with an indication (e.g., down arrow with text “MORE” at the bottom of the page102or the grey scroll indicator on the right of the page102) for more search results if the display content was scrolled down. The display content may each include summary specifications as shown in example114by reference numeral116. Categories within the display content are presented on one side106B of the search results, while sponsored content may be displayed in other available area106A.

The search results110-114are illustrated as search results that may be based on prior behavioral clues from users' prior searches. For example, existing items and existing item names that were previously searched by users and that share terms with the query may be provided for the search results110-114in an allocated area or slot. Indeed, the provided title of the content of each search result110-114may reflect a product underlying the result. For example, BRAND ONE MODEL may be a brand name (i.e., BRAND ONE) and/or a product name (i.e., ONE). In the alternative, the title provided may be an effort to target specific product searches, long after the searches have been made and the search queries analyzed. The above search results110-114, however, may be limited to analysis of user queries within the website102and may not reflect a newly released product with the name ACME MODEL ONE. As a result, the search results110-114may be limited to items previously searched by other users. Here, even though the user may be more informed about new releases and may be seeking information on the website102for the new release, the website102may be relying on past user behavior and may not generate information as to the newly released item in response to the query provided because the newly release product has never been provided in a search results or in a proper recognizable area of the search results. This may also be the case when the newly release item is newly entered into a catalog of the website102, but was never previously produced in a search result or purchased via website102. Search results110-114are the closest matches the query, from different categories, based on past searches using similar keywords in the query.

In a further aspect, the search results110-114are partly generated by a ranking that may be based on prior behavioral clues from users' prior searches. For example, existing items and existing item names that were previously searched by users and that share terms with the query may be retrieved from a data store. The existing items and existing item names may be ranked according to popularity of interactions—e.g., purchases, clicks, hovering cursors, etc. In another aspect, click-through-rates or user time spent on item pages within the website providing the search results may be used to rank the search results110-114. One of ordinary skill would recognize that the website and its hosting servers may not provide new items—either newly available from other websites or newly cataloged in the website as part of the search results—reflecting an issue of cold start for the new items. Alternatively, it may be the case that the new items do not appear with sufficient visibility in the rankings of search results110-114to be displayed on the first page the search results or in subsequent pages of search results till the end of the search results. These issues may be a consequence of an inability of the host servers to index data in a manner to include new items. These issues may also be a consequence of the host server's inability to efficiently process data and results in latency issues and increased traffic, which culminate in decreased user experience. The solutions in this disclosure includes systems, and software or firmware configurations, that are available to modify rankings of the search results110-114to incorporate new items from third-party website or cataloged results without attributed interactions. As a result, a website incorporating the present solutions as configurations in the host server, for example, may provide reduced latency and misdirected traffic by providing accurate search results based at least in part on new item releases or cataloged in the website.

FIG. 2illustrates an example system architecture200for performing the disclosed embodiments in accordance with an aspect of this disclosure. The example environment200may include computing components and network relationships that are applicable for providing the content in the webpage or display content102ofFIG. 1. The example environment200includes a content server210for serving content in response to a search or interaction on the webpage or display content102; a database212for storing content from content providers204(e.g., product information, service information, advertisement, and other related information; news, social media, and other product/service related content from which information is gleaned for use in the present system. Further, advertisement networks may provide paid content (e.g., content in section106of content page102), and users with computing devices202may send queries or interact with the content server to access the paid content (e.g., advertisement) or unpaid content (e.g., digital information for products and services that may be referred to or available for purchase via the content server210).

Content providers204are able to utilize respective computing systems and/or devices to interact with the content server210through the network208, for example, a local area network (LAN) or wide area network (WAN), e.g., the Internet. Similarly, users with computing devices202are able to utilize their respective computing device to access content (e.g., websites or stand-alone web enabled applications102) that may be offered through the content server210or via the content providers204over the network208. For example, content providers204can provide content (e.g., webpages, product information, etc.) that is accessible over the network208(e.g., the Internet). In such an example, the content providers use the content server as a host for interacting with the users/client devices202. In an alternate implementation, the content server hosts its own services for providing content of the content providers204, such as an electronic marketplace. In yet another alternative implementation, the content providers204may utilize one or more of its own computing systems to provide a website or web-enabled application that is accessible through the network208. In such an implementation, the content server210may provide referral links for content to the content providers' websites for purchase of associated products and/or services.

The content providers' website or web-enabled applications may offer opportunities to present additional, and in some instances, paid content to users accessing the website. For example, electronic advertisements or other digital media may be provided for newly released products based on the analysis of queries and news websites described herein. The computing devices and/or systems for each of the content server210, content providers204, content webpages206(e.g., news and social media), and users with computing devices202will each generally include memory for storing instructions and data, and at least one processor for executing the stored instructions that configure the computing devices and/or systems to perform the features disclosed.

When a user with a computing device202uses the computing device to access content from the content server210or content providers204, the relevant content provider204can send, either directly or via the content server210, responsive content to the computing device202. This access for content can include various requests or searches to find specific content hosted by the content server210or content providers204. Further, content server210may be a special status host and may have special access ability to index content from various content providers204that are hosted or released to content webpages206. This process allows at least a portion of new content to be indexed to provide sufficient new information to interested parties in future searches via content server210. Accordingly, content server210may include cookies, authentication certificates, or signed certificates to enable such access to protected content hosted in content webpages206.

FIG. 3illustrates an example system architecture300for performing the disclosed embodiments in accordance with a further aspect of this disclosure. The system architecture300includes content providers306in communication with content servers316and with client devices302, via network314. As noted with respect toFIG. 2, the content providers306may incorporate features of the content servers316as discussed subsequently herein to process content in a similar manner as the content servers316. Client devices302and content provider devices306can include any processor and memory based electronic devices with capabilities as disclosed herein, but at least with the capability to execute computer-readable instructions in a browser or to execute computer-readable instructions with or without a browser. These electronic devices are described in detail below and include specific configuration to perform the functions herein. Such electronic devices may include personal computers, tablets, ultrabooks, smartphones, cell phones, wearable watches and related devices, handheld messaging devices, laptop computers, set-top boxes, personal data assistants, electronic book readers and the like. Each of these electronics devices may be configured to include a browser or a stand-alone application304/308that is capable of being configured in the manner of this disclosure. In an implementation, content providers306may use one or more of content servers316to run tests on hardware and software features or services offered in the system architecture300. Users or consumers of electronic or online products and/or services use the client devices302to interface with a website providing such content.

Network314can include any appropriate network, including an intranet, the internet, a cellular network, a local area network or any other such network or combination thereof. The network could be a “push” network, a “pull” network, or a combination thereof. In a “push” network, one or more of the servers push out data to the client device. In a “pull” network, one or more of the servers send data to the client device upon request for the data by the client device. Components used for such system architecture300can depend at least in part upon the type of network and/or environment selected. Protocols and components for communicating via such a network are well known and will not be discussed herein in detail. Communication over the network314can be enabled via wired or wireless connections and combinations thereof. In this example, content server316may include one or more local servers in communication with each other and with other remote servers via the network314. In an example, the content server316includes a web server for receiving requests and serving content from the client devices302and/or the content providers306. In response thereto, although for other networks, an alternative device serving a similar purpose as any one of the content server316could be used, as would be apparent to one of ordinary skill in the art upon reading this disclosure.

In a further example, the content providers306may include a content console308for communicating with the content server316. The content console may be an internet-enabled application (e.g., browser/stand-alone application) that is configured to execute on the content provider306and is configured to communicate with the content server316in the manner described herein. The communications between the content server316and the servers/devices ofFIG. 3are via an interface or networking component318, such as a network interface card or a wireless interface. In alternate embodiments, the system architecture300is maintained internal and confidential between the content providers306and the content server316during the configuration stages. Accordingly, one or more components or modules in the system architecture300are isolated from external influence by any known security methods, including firewalls, during configuration stages. Alternatively, sections of the one or more components or modules in the system architecture300are available within one or more content providers306. In yet another alternative implementation, sections of the one or more components or modules in the system architecture300may be secure, while other sections of the one or more components or modules may be available in the public domain to interface with client devices302. In accordance with such alternate implementations, one or more components or modules in system architecture300may also be virtual machines or operate in a virtual environment for performing one or more of the features disclosed herein.

Further, in the example architecture300ofFIG. 3, real-time or dynamic search results are provided for queries from the client devices302. For example, queries are addressed in real-time or dynamically via the server modules324, which may include machine learning capabilities, a display content generator338, and a new item modifier336. Modules324and336may cooperatively function under two or more modes. In one of the modes, the machine learning features of select modules324(e.g., crawler/webpage analyzer332and query reviewer330) may constantly improve upon itself using each new query and newly modified content as feedback to their respective machine learning algorithms to ensure a robust functionality. Machine learning aspects are provided in the examples ofFIGS. 6A-B, and7. Such a mode may be referred to as a training mode. In an application of neural networks for the machine learning operations, the machine learning aspects of these modules may train one or more neural networks with each new query and newly modified content while running active operations with a copy of an active previously-trained neural network. Once the new query and newly modified content has been trained to one or more inactive neural networks, the one or more inactive neural networks may be activated and the previously-trained neural networks may be inactivated for training. Alternatively, bulk queries and modified content is also available to train one or more neural networks during an assigned downtime.

In content server316, the queries from client devices302may be received via module326. Results webpage(s) or display content is generated by module338. In an example, the display content generator338is a module that includes HTML® and various dynamic scripts that are formatted for rendering on the client device302. The dynamic scripts enable additional requests for information to populate an HTML® formatted page upon rendering on the client device, for instance. In addition, content server316includes a query monitor328for determining if there is a spike in the received queries. In an example, such a spike may be a general increase in search traffic or an increase in specific search queries. Query reviewer330is a module that may be separate or part of the query monitor328, and that reviews the queries themselves to determine if any of the queries (e.g., from a detected spike) have common textual features.

In an example, the common textual features are determined by a machine learning algorithm of the module which determines similarity and/or semantic relationships between the query terms. Semantic relationship may pertain to implementations where one or more queries during the spike include a typographical error or a word sounding similar to an actual new item name. The reliance on semantic relationships in the machine learning process may still indicate such a query as indicative of a possible new item release. An example implementation is provided inFIGS. 6A-Band it supporting discussion. For example, two or more words in a query form a basis for identifying similarity and/or semantic relationships with similar groupings in other queries during the spike. With two or more words take in different combinations from a query for comparison with other queries, a reliance on a single word pair may be eliminated for further robustness in the similarity matching process.

Furthermore, noun identifiers are weighed preferably over articles and other grammar portions from the queries. The two or more words are analyzed against individual words or similar groupings across multiple queries since the spike occurred. In a process to eliminate false positives, the two or more words identified as similar and/or semantically similar may be compared against prior queries or content stored in the content database320to ensure that the queries do not relate to existing items or products forming part of the content in content database320. Indeed, if no content is seen as matching, then it is likely that the queries relate to a new item. Each word in a query may be taken with another word of the query to find correlation to the word pair. In an alternate aspect, from one query detected during the spike, two words are taken as a single word and then combined with one or more words to find correlation between the grouping and other groupings from other queries during the spike. The semantic relationships and/or similarities are comprised of measures that are then applicable to provide measurable representations for semantic relationships and/or the similarity.

In an implementation, terms identified as semantically similar and/or plainly similar across queries are then used to search the third party content webpages310(e.g., news webpages or social media) for content indicative of new item releases (i.e., product or service electronic releases or publications). For example, instead of merely crawling for new webpages, crawler and webpage analyzer332may be used to analyze newly published webpages to determine if the common textual features within the queries, during the spike, are also found in one or more of the newly published webpages. When the common textual features are found, then the one or more of the newly published webpages are further analyzed for embedded hyperlinks. Identifiers may be hyperlinks, Uniform Resource Identifiers (URIs), or Uniform Resource Locators (URLs). As newly retrieved webpages are news and social media webpages, it is likely to be the case that these webpages include a link to the product description or domain of the manufacturer, retailer, or distributor of the newly released item. The hyperlinks determined as embedded in the news and social media webpages310are extracted and provided as part of modified content display that include publicity for new items, thereby resolving, in part, an issue of electronic content discovery related to cold start for new products and services.

The identifiers determined as embedded in the news and social media content310are retrieved and indexed in the index database322by the hyperlink retriever and indexer module334. The hyperlink retriever and indexer module334may retrieve and generate a ranking of search results for the display content. The ranking of the search results may be by previous interactions received from prior searches within the website. The new item modifier336may then retrieve the indexed hyperlink and at least a portion of the content (including information from webpages referenced by the hyperlink, such as text, image, video, or audio portions) and provide this to modify the display content from the display content generator338. Further, the new item modifier336may, alternatively or concurrently, modify the ranking of the search results to provide the indexed hyperlink and at least the portion of the content in an intervening position in the ranking for the display content. In addition, the new item modifier336may be part of the hyperlink retriever and indexer module334, and may perform the modification of the ranking of the search results along with (e.g., concurrently with) the generation of the search results for the display content.

For example, when the ranking is based at least in part on prior searches within the website, the search results for the display content are provided with rankings—e.g., 1, 2, 3 . . . etc.—representing a maximum number of results for one page of the display content. The maximum number of results is configurable by the user to display more ranked results per page of the display content. When the modification is performed, content for the new item, including an identifier and a portion of an underlying specification or other information, may be provided in the intervening position of the above example rankings. In one aspect, content for the new item is provided in the “1” ranking, while the existing item previously ranked at the “1” ranking is moved down to 2 or subsequent ranks. Such a modification may occur dynamically, as new items are released and/or as required for the search results. Alternatively, the display content includes an area for the new content under a special ranking—e.g, a ranking representation of “0” implying a default status at the top of one or more pages in the display content. As such, the intervening ranking may correspond to modification of the existing rank or insertion of a ranking or placement that would position the content for the new item with more visibility than the remaining search results.

In another aspect, the display content is modified after being provided to the client device302. When the display content includes dynamic script that requests for updates to the search results—to maintain updated search results—the new item modifier may provide the portion of the content corresponding to the new item and the identifier retrieved by the module334to modify the display content. Alternatively, modifier of module336modifies the results webpage as it renders on the client device302. In such an alternative implementation, a slot or allocated area is designed in the display content for providing updated information or for providing most relevant new item information to the client device302. The slot or allocated area is a dynamic area to incorporate the new item information and the hyperlink in a dynamic manner, and representing an updated look and feel for the results webpage. Moreover, the use of the display content provides a graphical user interface that is dynamic—offering accurate and up-to-date content that is distinct from static content of a search results page. The use of such dynamic display content ensures that real estate exists to present existing content as well as to modify the display content for accurate and new information. Retrieved content from the index database322is subject to the new item modifier336for formatting and presentation in the display content from display content generator338. In an implementation, the new item information and related hyperlink is sent from the modifier with the display content and modifies the display content during rendering at the client device302.

FIG. 4illustrates an example400of a window of modified display content304that can be presented in accordance with various embodiments. This example, like the example ofFIG. 1utilizes an electronic marketplace as the content at issue. In the example400, like in example100ofFIG. 1, the window may be a web browser or a stand-alone application for interacting with display content402of the electronic marketplace. A user may search or interact with the display content via a search field408. A query418is provided in the search field408in one implementation of the searching or the interaction with display content. When the submit option is selected, the search may be initiated and processed on the computer or on a server as discussed in the implementations ofFIGS. 1-3. In example400ofFIG. 4, a query418for ACME MODEL ONE is entered into the search field408, and the search results are provided in the results section404of the display content402. As illustrated, there are four search results410-416on display, with an indication for more results (e.g., down arrow with text “MORE” at the bottom of the page402or the grey scroll indicator on the right of the page302) if the display content was scrolled down. Result416, while illustrated in the search results may load after the existing results—i.e., results410-414, are loaded. Categories within the search results are presented on one side406A-B and406D of the search results, while sponsored content may be displayed on other available area406C. Category406D may be a new category based on a learning of the new item type—DRONE and the commonly used terms associated with DRONE—e.g., TOY. As a result, the display content may be modified to include a new category406D and the corresponding new item result416.

The search results410-416are illustrated as search results that are modified to be responsive to the user's query that is determined as a new item query. As a result, a new item416(ACME MODEL ONE drone) is provided in a slot at the top of the display content. The new item may be indicated by special bordering or markings, e.g., marking418A stating NEW as to the new search result responsive to the query. In an alternate implementation, a highlight or marker of any sort may be applied to the new item portion of the content to reflect that dynamic modification has occurred. The example marked highlight418A is illustrated over the slot418B to reflect this. In addition, a decay function may be associated with the marked highlight418A or slot418B. The decay function, in one example, may cause the highlight to draw the customer's attention to it and then fade over the decay time set in the decay function. Such an implementation further improves the user interface displayed in the example electronic marketplace as discussed above. For example, the decay function improves the ability of the computer to display information and interact with the user through the use of content modification that dynamically indicates relevant information to the user's query.

In aspects of this disclosure, other locations for the slot418B may be provided in the display content402. Further, the title or the slot may include an embedded hyperlink to the product information page that was retrieved from the news or social media content and indexed in the index database that is illustrated inFIG. 3. In addition, summary specifications the new item may be provided under the item title, as extracted from the product information page of the underlying hyperlink. In another aspect, along with or separate from the marked highlight418A, the modified portion of the display content402might be subject to the decay function as well. For example, the modified portion—the slot418B—may fade to the original terms in the content and may fade back to the modified portion. Alternatively, a hover-over event may be implemented for a cursor action. Here, a timing function determines the hover-over time of a cursor over the modified portion (e.g., marked highlight boundary of slot418B). As such, when the cursor hovers over the ACME MODEL ONE for the time specified in the timing function, the modification may revert back to the original term that existed prior to the modification.

In another example aspect, the summary specifications under the title in slot418B may be displayed as an image from the retrieved content. For example, certain products provide stickers or labels providing critical or relevant information. Such information may be retrieved as part of the search process herein. In an example, the system of the disclosure herein is available to modify any content type than a displayed results webpage. For example, in audio-based or image-based searching, a similar manner as described above is applicable. Semantically similar or same audio or images to the query audio or images are generated based on a search of such content in news or social media content. Linked audio or images in the news or social media content is extracted and indexed along with an identifier for the source of the linked audio or images. Such a process is initiated after based at least in part on determining that a spike in queries has occurred. Video searching is similarly possible using frames within the video as portions of the video may be indexed based on searching of video-type social media content and video news, for instance. In an example, audio-based searching may be based of text-to-audio read-back, where the text results are read-back with a modification announced as the new item is identified as responsive to an audio-based query.

FIG. 5illustrates an example of semantic processing for machine learning in accordance with various embodiments that may be applied to achieve the above modification of content. The machine learning ofFIG. 5may be implemented in server module330to find common textual features in a query among multiple queries identified as part of a spike in received queries. When the common textual features are found as identified by a machine learning algorithm, then the common textual feature forms a basis for crawling and analyzing of news and/or social media webpages. For the machine learning part, as illustrated in section500the query from the search is provided as individual words to the machine learning algorithm. In this example, each entry represents a query identified as part of a query spike. In an implementation, an individual word502is taken and combined with each other word504from a query to create a robust trained network for query recognition. Further, groups of words506that are limited by a predefined number of words may be taken from a query and combined with other groups of the same query (e.g., MODEL1, NEWLY RELEASED) for training a network. Such training may be supervised or unsupervised. From the example inFIG. 5, a machine learning trained dataset would be able to distinguish incoming queries as belong to the spike or outside the spike.

InFIG. 5, a select root term may be chosen (e.g., reference numerals502/506) around which the training is performed. For example, in the input queries500, the first two queries include a respective root terms502. The first two queries also include associated terms504used for training with the respective root terms502. The associated terms504may provide context to the root term and helps with the machine learning process. The remaining queries in reference numeral400similarly follow the first two queries, but illustrate instances of using phrases506and associated groups or individual words for training a dataset to recognize a word or phrase associated with a trained phrase. In the machine learning process, term samples508are extracted from the queries500. For example, the term samples include the root term502and each associated term504—such as (MODEL, ACME); (MODEL, ONE); (DRONE, ACME); and (DRONE, NEW)—for the first two queries in reference numeral500.

The machine learning process trains a neural network (NN) to recognize that MODEL and ACME or DRONE AND ACME share a contextual relationship such as a semantic relationship (e.g., used together in a search). Similarly, DRONE RELEASED and NEW share a semantic relationship for being used with each other. While the query for DRONE RELEASE is newly received, a trained dataset of the NN may recognize the query as for the new item. There are large numbers of such queries that are utilized in the machine learning process and that provide similar contextual input. A spike in queries, therefore, enables a robust dataset to provide up-to-date new item information for new queries as they are received since the spike. For example, if ACME and MODEL are often used together then a large number of the queries would include these two terms. When these large numbers of queries comprising these terms are used to train a neural network, using the terms interchangeably as root and associated terms, then such a trained neural network would be able to predict one of these terms when provided with the other term as an input. For example, an NN trained to recognize these two terms as semantically related will provide an indication that the new item is a predicted search result when ACME or MODEL or ONE (or any variation of these words) is provided as an input.

Further, while the extracted samples508are illustrated as a root word phrase and associated word or phrases, a window of three or more terms may be used to provide additional context. Then neural networks may be trained to recognize that each root word may be semantically related with two or more associated terms. While providing training vectors510for each word, it may be prudent to use a more robust training vector. The training vectors510may represent a single word with 0s in all rows, but a 1 in a single row to represent a word—e.g., ACME. This is also referred to as a one-dimensional representation of a word. The training vectors510may be used as provided, but an option to use distributed training vectors600, as illustrated inFIG. 6A, is also available. The distributed training vectors may be a transformation of one-dimensional training vectors to form a multi-dimensional representation of words. As such, this transformation may be seen as a process of determining vector of semantic terms (e.g., a sentence) by averaging individual terms in the individual vectors.

In an implementation, the one-dimensional representation of a word may be prepared for the transformation to a multi-dimensional representation of words based on a matrix multiplication with a feature matrix. The feature matrix may provide the word's relationship to other words forming the multi-dimensional representation. For example, using a single word's one-dimensional training vector, a neural network with a hidden layer and multiple related words may be trained to recognize the single word from multiple related words. In such an implementation, the back propagation of error and the feed-forward process may correct initial random values in the hidden layer to an accurate representation of a multi-dimensional vector for the word's relation to other words. InFIG. 6A, the words in the query ofFIGS. 1 and 4are provided in comparison with other words generally available in a database of terms through a multi-dimension vector.

In another example aspect, word to word comparison may be performed, and may be extended to multiple words in a window. A training vector for a word, as illustrated in reference number510, may be first converted into a feature representation using a feature matrix. A feature matrix is composed of various values associated with unique features in text. For example, words with known relationships to other words may be a feature predefined in the feature matrix. The distributed representation vectors600may represent one such implementation. The training vector for a word may be multiplied with the feature vector to provide the feature representation. As the training vector for each word is a single row of multiple 0s and a single 1 entry, the resulting feature representation will be a single row of multiple columns of values. To find a relationship between words, two respective feature representations—one for each word—is multiple and fed to a classifier. In an example the classifier may be a softmax classifier which is a normalized function that marginalizes the largest value from the lower values in a provided input. As a result, highly related words are classified in a manner to distinguish a lesser relationship word-pair.

For example, the multiplied values from the feature representations of word A with a word B may provide a singular value. A similar evaluation of word A may be performed with multiple words. Following this the classifier may be applied to the collection of singular values to provide a vector distribution of the words associated with word A in different levels of closeness. The closeness demonstrates a sematic relationship from prior use of the words associated with word A. The vector distribution may represent a trained network which will be able to predict a word to output when the input word is Word A, following from the above example. Considering the above application in a multi-dimensional plane, with multiple words and relationships, a trained neural network will be able to identify relationships based on the numerical values associated with words. As such, an input of two or three words allows the trained neural network to predict a word that is bears a relationship to both words. Such a trained neural network may also be designed to provide a level of closeness (semantic relationship or similarity) between words in a query.

FIG. 6Billustrates another intermediate processing feature for modifying content based in part on a query. When a trained neural network indicates that semantic relationships exists between terms in the query and terms in a database of terms, then the trained neural network may be configured to provide such information. InFIG. 6B, the information as to semantic relationships is provided in the form of a graph602. The query words ACME and MODEL ONE are determined as related with respect to certain other commonly used words—e.g., DRONE, NEW, RELEASED, and TOY, as demonstrated in the feature matrix600ofFIG. 6A. While the graph is provided to visualize distances between words as defined in their determined semantic relationships, it is understood that a configured system may not graph the relationships, but merely provide the outputs. As a result, the closest determined terms, by semantic relationships, may have the least distance as calculated by a cosine distance measure or a Euclidean distance measure—once normalized.

In an application of the above trained neural network, once a correlation of queries to find common textual features is completed, a further analysis is performed to determine semantic relationships between words in the retrieved content and the query terms. Finally, the example process herein utilizes one or more of the correlated words or phrases from the queries to represent common textual features and to find if these common textual features exist in news and social media webpages. For example, the words ACME and RELEASED, having found to be semantically similar to other query terms, such as DRONE and TOY, it is most likely the case that a search of news or social media webpages would find a new item described and including these words. As a result, news and/or social media webpages that have these words are likely to include hyperlinks to a product description of the new item underlying the discussion in the news or social media webpages.

In an implementation, the above machine learning is performed in a continuous manner, but at least after a spike in search queries is indicated. Such an implementation represents pre-commutation to improve search results and to make the computing process more efficient. The pre-computation may occur in anticipation of further searches to a new item based on the spike in interest for the underlying common textual features of the query—e.g., for products suddenly in demand (new products, information from new press releases, items on sale, accessories related to new products, etc.). In an example, the pre-computation process monitors for the sudden surge in a specific search terms and stores semantically similar terms to the search terms, but also indexes search results for subsequent searches to a first time search. This improves latency of the search process from users' perspective, but also enables efficient computing as it reduces the burden on the content server (including its related computing devices and modules) to calculate similarity on-the-fly. The combination of pre-computed modification at near real-time with dynamic modification may also be implemented depending on the work-load at the content server. At high traffic times, the work-load may be higher and the demand for resources may need to be balanced with respect to the in-demand content. In such instances, pre-computing may be triggered to benefit the dynamic modification for the search results.

As a result, the use of the present content searching process additionally provides a solution to a network and computer related issue of latency and traffic management for high traffic networks. A user able to secure their specific match may not browse through multiple pages of search results or select to open multiple pages of product information from the search results. This reduces traffic to the content server (and related computing devices). This also reduces the work-load to these devices or allows the devices to be used to perform other tasks—e.g., improving the dataset—than providing pages of search results. In addition, this also removes from any requirement to store search results for anticipated access when the system provides the query-specific search results in the very first page. This alleviates storage issues as the content server processes query and retrieves data for numerous queries every second.

Another technical benefit realized in the use of the present disclosure is the ability to efficiently use display space in the user interface of the electronic marketplace or content display. The specific search results—those with modified content—may be provided the first area of the display content as illustrated inFIG. 4, with other search results in the remaining area previously allocated for the purpose. The user interface of the display content may also be dynamically modified in certain areas executing the appropriate dynamic script to indicate the specific search results separately from the general search results that are based on prior searches. Such areas can include, in addition, sponsored search results (e.g., paid content), including advertisements as to accessories for the new product. In an example, when a user is determined as from a mobile device or using a connection deemed as limiting (e.g., cost prohibitive or high loss/latency connections), it may be most efficient to provide the specific new content results alone. This may require dynamic modification of the display content to display the specific new content, which represents an exact match for a user's query in accordance with the latest developments of the day or the week—e.g., a new product launched is immediately provided when the product is searched instead of the typical search results of a previously highest selling product based on previous user behavior.

FIG. 7Aillustrates examples of semantic processing for machine learning to determine that common textual features from the queries are found in news websites or webpages700in accordance with various embodiments that may be applied to achieve the verification of existence of new items from an monitored query spike. The machine learning ofFIG. 7Amay be implemented in server module332to find that common textual features from queries, during the spike, are found in news websites or webpages700. Further, a person reading the present disclosure will recognize that the social media webpages are also available in the verification ofFIG. 7A. The process inFIG. 7Ais similar to the word grouping, sample extraction, and training vector generation fromFIG. 5for common textual features in the queries. The common textual features from the queries are found in news webpages700(e.g., reference numeral706for NEW DRONE MODEL being similar to words with high commonality from queries500). When found, the specific webpage702may be indexed first. Then the content of the webpage702is further crawled to find embedded hyperlinks—e.g., hyperlink708. The hyperlink is accessed and the underlying webpage from the hyperlink is also accessed and indexed.

Verification may be performed to determine if the hyperlink is related to the article in a substantial manner. For example,FIG. 7Billustrates examples of cross-verification that textual features in the news content fromFIG. 7Aare authentic using release publication content in content from a hyperlinked website708in the news content in accordance with various embodiments.FIG. 7Bdemonstrates that, when the webpage720of the hyperlink708is a release publication722from the manufacturer, distributor, or retailer of the new item, then many words or phrases in the hyperlinked webpage (e.g., words or phrases724,726) would be common (same or semantically similar) to the words or phrases in the news or social media website702(e.g., words or phrases704,706—HAS HIT and INTRODUCED may be semantically similar for a trained NN dataset). In addition, words or phrases728, e.g., describing specifics of the new item, from the hyperlinked webpage720, may be extracted for use in the results webpage with the new item insertion.

An additional verification may be performed based on category specific information for the queries and the new item. For example, a determination is made for whether the textual features are associated with an item category for the item based on a classification of the textual features in a classified dataset providing categories and related terms. For example, as demonstrated inFIGS. 5 and 7A-7B, words commonly used with the textual features may define a category that suits the new item. For example, the item type of DRONE or related use of the word TOY—as per a trained dataset illustrated inFIG. 6A, would result in categorization of the new item as a DRONE or, in a more common category, a TOY. This is illustrated inFIG. 4. The categorization process may include determining category-specific news webpages that are in the item category and accessing the category-specific news webpages to identify the news webpages. In an implementation, if the news webpages is for new toys or new smartphones, then this specific category for the news website may be applied to the new item as well. The categorization also helps differentiate new items by their categories to avoid improper content discovery. For example, marketing of a new movie may include a new video game. However, the new item for discovery may be the new movie. As a result, category-based searching for the first content—news websites and social media—may provide indication that the new item under the spike is a movie than a video game. In an implementation, the number of first content providing information may determine the appropriate classification or categorization. When a number of news webpages describing movies associated with the new item release is higher than a number of news webpages describing video games or other marketing items, the new item is a movie and appropriate movie related results may be provided in a subsequent search involving the movie name as a query.

In yet another verification process, the content server may be configured to parse the hyperlink to determine a domain and a landing page in the domain—such as, of a manufacturer's website. Then a determination is made that the domain belongs to an entity associated with the item based at least in part on a comparison of the domain with a database of entities' domains. For example, if a release publication cites to the manufacturer as ACME and the domain is ACME.com or a variation of the word ACME—e.g., ACMEPRODUCTS.com—then a credibility score may be associated with the entity based at least in part on timing of a release publication within the domain. For example, the creditability score may be based in part on the timing being within a predetermined range of a timing of publication of at least one of the news webpages, which indicates that the hyperlinked website is authentic as the news website is timed based on a publication released from the hyperlinked website for the news website. When the credibility score exceeds a threshold value, then the hyperlink is used an identifier for the item webpage. For example, the timing of the release publication may be determined from a printed time on the release publication or from a time of indexing provided by a web crawler that indexes for new webpages. The printed time or the time of indexing is then provided to adjust the predetermined range.

In another part of the verification process, either concurrent with the above or differently from the above verification, the news webpages is parsed to select sets of contiguous words. This is illustrated inFIG. 7A. A search is performed in the release publication for individual ones of the sets of contiguous words. When a match is found, a count is incremented to indicate matching individual ones of the sets of contiguous words. The credibility score may then be weighed favorably to the domain when the count is more than a predefined number. In another feature, a determination is made as to whether an existing item in the website exists for the queries. When no purchase occurred for the existing item in each instance of a search using the queries received during the spike, then a determination is made that the queries are to a new item.

FIG. 8Aillustrates an example process flow800to configure a computing system of at least a processor for providing display content with new items in a cold start in accordance with various embodiments. Sub-process802determines that a rate of receipt of queries at a website is higher than a threshold rate. Such a determination may be indicative of spike in interest in a new item from users aware of the new item. The present method provides configuration via sub-process804, to a computer system to access first content (e.g., news and social media content) to determine that textual features common to the queries are available within the first content. While sub-process804illustrates the use of textual features and a search of text, the use of image features, frame features, and audio samples are appropriate to match from query image, video, or audio data to similar media in the first content.

Access to the first content may be by crawling and analyzing news and social media content to determine that the textual features common to the queries are available in text within such news or social media content. Specific news or social media content, when found, are likely to describe a new item that is subject of the received queries. The specific news or social media webpages are then additionally crawled or parsed for identifiers, such as hyperlinks. Such a determination of identifiers is performed via sub-process806. For example, sub-process806determines that an identifier exists within one of the first content. The identifier is a reference (e.g., hyperlink) to a second webpage that includes details associated with the textual features—e.g., new product information or specification.

The hyperlinks within the individual one of the news or social media webpages may provide access to webpages that are specifications and/or product release information webpages (e.g., hyperlinks to open or securely held manufacturer's or retailer's product release webpages). The specifications and/or product release information webpages provide item details for the new item intended as responsive to the queries. The computer system may be configured to index the hyperlink and the related webpages or a summary of the information contained therein. In sub-process808, a determination is made as to whether a further content query comprising the textual features is received in the website. This further content query may be to the newly released item in the electronic marketplace or auction website, and may be received subsequent or concurrent with the spike of queries. When the further content query is not for the newly released content—e.g., does not include the textual features, then the process800performs sub-process802for monitoring the rate of receipt of queries for spikes. When the further content query is for the newly release content, then the website, via sub-process810, generates the display content with areas for responsive information to the content query. The responsive information includes area for previous content that may be responsive to the content query from prior users' behaviors and an allocated area from new content. For example, instead of a static results webpage, the display content is dynamic and includes areas or slots for existing content and for at least one additional slot or area for dynamic modification. Sub-process812provides dynamic modification to the display content to comprise information—e.g., a portion of the new content, and an identifier of the source of the new content. The portion of the new content may be provided in a manner indicative as borrowed from the source—e.g., in quotes or as an image excerpt or screenshot—thereby providing a look and feel improvement over a static results webpage.

FIG. 8Billustrates an example process flow850to configure a computing system of at least one processor for determining to provide display content with new content in a cold start, in accordance with various embodiments. Sub-process852determines that a rate of receipt of queries at a website is higher than a threshold rate. Such a determination may be indicative of spike in interest in a new item from users aware of the new item. The present method provides configuration via sub-process854, to a computer system to access first content (e.g., news and social media content) to determine that textual features common to the queries are available within the first content. While sub-process854illustrates the use of textual features and a search of text, the use of image features, frame features, and audio samples are appropriate to match from query image, video, or audio data to similar media in the first content. Access to the first content may be by crawling and analyzing news and social media content to determine that the textual features common to the queries are available in text within such news or social media content. Specific news or social media content, when found, are likely to describe a new item that is a subject of the queries. The specific news or social media webpages are then additionally crawled or parsed for identifiers, such as hyperlinks. Sub-process856performs a determination for an identifier within the first content. The identifier referencing second content associated with the textual features. For example, sub-process856determines that an identifier exists within one of the first content. The identifier is a reference (e.g., hyperlink) to a second webpage that includes details associated with the textual features—e.g., new product information or specification.

The hyperlinks within the individual one of the news or social media webpages may provide access to webpages that are specifications and/or product release information webpages (e.g., hyperlinks to open or securely held manufacturer's or retailer's product release webpages). The specifications and/or product release information webpages provide item details for the new item intended as responsive to the queries. The computer system may be configured to index the hyperlink and the related webpages or a summary of the information contained therein. In sub-process858, a determination is made as to whether a further content query comprising the textual features is received in the website. When the further content query is not for the newly released content—e.g., does not include the textual features, then the process850performs sub-process852for monitoring the rate of receipt of queries for spikes. When the further content query is for the newly release content, then the website generates, via sub-process860, the display content with areas for responsive information to the content query.

The further content query may be directed to the newly released item in the electronic marketplace or auction website, and may be received subsequent or concurrent with the spike of queries. In response to a content query received in the website, sub-process860generates display content comprising a ranking of matching content indexed from prior user searches in the website. Sub-process862modifies the ranking when the content query includes at least one portion of the textual features. For example, the modification provides the identifier and at least one portion of the second content in an intervening position in the ranking for the display content. As described previously, the modification may change a rank of an existing matching content indexed from the prior user searches by a ranking to the new item to intervene in the ranking. In one aspect, such a ranking may incorporate a new item in the place of the existing matching content. Alternatively, the ranking to the new item places it above the ranking of the existing matching content by a rank value that indicates a top page listing—e.g., ranking of “0.”

FIG. 9illustrates a logical arrangement of a set of general components of an example computing device900that can be used to implement aspects of the various embodiments. In this example, the device includes a processor902for executing instructions that can be stored in a memory device or element904. As would be apparent to one of ordinary skill in the art, the device900can include many types of memory, data storage, or non-transitory computer-readable storage media, such as a first data storage for program instructions for execution by the processor902, a separate storage for images or data, a removable memory for sharing information with other devices, etc. The device may include a position element912to provide positioning for updated results based on geographic position of the device900. The device900will include some type of display element906, such as a touch screen or liquid crystal display (LCD), although devices such as portable media players might convey information via other means, such as through audio speakers. As discussed, the device in many embodiments will include at least one input element918that is able to receive conventional input from a user. This conventional input can include, for example, a push button, touch pad, touch screen, wheel, joystick, keyboard, mouse, keypad, or any other such device or element whereby a user can input a command to the device. In some embodiments, however, such a device might not include any buttons at all, and might be controlled only through a combination of visual and audio commands using imaging element908and audio element910, such that a user can control the device without having to be in contact with the device. In some embodiments, the computing device900ofFIG. 9can include one or more network interface elements908for communicating over various networks, such as a Wi-Fi, Bluetooth, RF, wired, or wireless communication systems. The device in many embodiments can communicate with a network, such as the Internet using communication component914, and may be able to communicate with other such devices using this same or a similar component. Components902-914and918may be powered by power component916using internal or, in combination with, an external power supply.

Example environments discussed herein for implementing aspects in accordance with various embodiments are primarily Web-based, as relate to Web services and cloud computing, but it should be appreciated that, although a Web-based environment is used for purposes of explanation, different environments may be used, as appropriate, to implement various embodiments. Client devices used to interact with various embodiments can include any appropriate device operable to send and receive requests, messages, or information over an appropriate network and convey information back to a user of the device. Examples of such client devices include personal computers, smart phones, handheld messaging devices, laptop computers, set-top boxes, personal data assistants, electronic book readers, and the like. The network can include any appropriate network, including an intranet, the Internet, a cellular network, a local area network, or any other such network or combination thereof. Components used for such a system can depend at least in part upon the type of network and/or environment selected. Protocols and components for communicating via such a network are well known and will not be discussed herein in detail. Communication over the network can be enabled by wired or wireless connections, and combinations thereof using communication component914.

A data store can include several separate data tables, databases, or other data storage mechanisms and media for storing data relating to a particular aspect. The data store is operable, through logic associated therewith, to receive instructions from a server, and obtain, update, or otherwise process data in response thereto. In one example, a user might submit a search request for a certain type of item. In this case, the data store might access the user information to verify the identity of the user, and can access the catalog detail information to obtain information about items of that type. The information then can be returned to the user, such as in a results listing on a Web page that the user is able to view via a browser on the user device. Information for a particular item of interest can be viewed in a dedicated page or window of the browser.

The environment in one embodiment is a distributed computing environment utilizing several computer systems and components that are interconnected via communication links, using one or more computer networks or direct connections. However, it will be appreciated by those of ordinary skill in the art that such a system could operate equally well in a system having fewer or a greater number of components than are described. Thus, the depictions of various systems and services herein should be taken as being illustrative in nature, and not limiting to the scope of the disclosure.