DEDUPLICATION OF QUERY TO ASSORTMENT PAGES

A method including generating, using a semantic embedding generation machine learning model, one or more respective shelf embedding vector representations for each of one or more browse shelves based on a respective shelf name for the each of the one or more browse shelves. The method also can include obtaining a keyword. The method additionally can include generating, using the semantic embedding generation machine learning model, a keyword embedding vector representation based on the keyword. The method further can include determining a respective similarity score between the keyword embedding vector representation and each of the one or more respective shelf embedding vector representations for each of the one or more browse shelves. The method additionally can include determining whether any of the respective similarity scores for the one or more respective shelf embedding vector representations across the one or more browse shelves exceed a predetermined threshold value. When any of the respective similarity scores for the one or more respective shelf embedding vector representations across the one or more browse shelves exceeds a predetermined threshold value, the method further can include filtering out the keyword. When none of the respective similarity scores for the one or more respective shelf embedding vector representations across the one or more browse shelves exceed a predetermined threshold value, the method additionally can include generating a new topic page using the keyword. Other embodiments are described.

TECHNICAL FIELD

This disclosure relates generally to deduplication of query to assortment pages.

BACKGROUND

Websites that offer items online can present those items through various types of pages, such as browse pages based on categorical taxonomy of the items, search result pages generated based on keyword searches from users, and/or topic pages based on keywords that are pre-generated. Third-party search engines can crawl various types of pages on such websites but not others. For example, search result pages are typically generated based on user searches, and are not crawled by third-party search engines. Generating new topic pages can result in overlaps with existing browse pages.

DESCRIPTION OF EXAMPLES OF EMBODIMENTS

Turning to the drawings,FIG.1illustrates an exemplary embodiment of a computer system100, all of which or a portion of which can be suitable for (i) implementing part or all of one or more embodiments of the techniques, methods, and systems and/or (ii) implementing and/or operating part or all of one or more embodiments of the non-transitory computer readable media described herein. As an example, a different or separate one of computer system100(and its internal components, or one or more elements of computer system100) can be suitable for implementing part or all of the techniques described herein. Computer system100can comprise chassis102containing one or more circuit boards (not shown), a Universal Serial Bus (USB) port112, a Compact Disc Read-Only Memory (CD-ROM) and/or Digital Video Disc (DVD) drive116, and a hard drive114. A representative block diagram of the elements included on the circuit boards inside chassis102is shown inFIG.2. A central processing unit (CPU)210inFIG.2is coupled to a system bus214inFIG.2. In various embodiments, the architecture of CPU210can be compliant with any of a variety of commercially distributed architecture families.

Continuing withFIG.2, system bus214also is coupled to memory storage unit208that includes both read only memory (ROM) and random access memory (RAM). Non-volatile portions of memory storage unit208or the ROM can be encoded with a boot code sequence suitable for restoring computer system100(FIG.1) to a functional state after a system reset. In addition, memory storage unit208can include microcode such as a Basic Input-Output System (BIOS). In some examples, the one or more memory storage units of the various embodiments disclosed herein can include memory storage unit208, a USB-equipped electronic device (e.g., an external memory storage unit (not shown) coupled to universal serial bus (USB) port112(FIGS.1-2)), hard drive114(FIGS.1-2), and/or CD-ROM, DVD, Blu-Ray, or other suitable media, such as media configured to be used in CD-ROM and/or DVD drive116(FIGS.1-2). Non-volatile or non-transitory memory storage unit(s) refer to the portions of the memory storage units(s) that are non-volatile memory and not a transitory signal. In the same or different examples, the one or more memory storage units of the various embodiments disclosed herein can include an operating system, which can be a software program that manages the hardware and software resources of a computer and/or a computer network. The operating system can perform basic tasks such as, for example, controlling and allocating memory, prioritizing the processing of instructions, controlling input and output devices, facilitating networking, and managing files. Exemplary operating systems can include one or more of the following: (i) Microsoft® Windows® operating system (OS) by Microsoft Corp. of Redmond, Wash., United States of America, (ii) Mac® OS X by Apple Inc. of Cupertino, Calif., United States of America, (iii) UNIX® OS, and (iv) Linux® OS. Further exemplary operating systems can comprise one of the following: (i) the iOS® operating system by Apple Inc. of Cupertino, Calif., United States of America, (ii) the WebOS operating system by LG Electronics of Seoul, South Korea, (iii) the Android™ operating system developed by Google, of Mountain View, Calif., United States of America, or (iv) the Windows Mobile™ operating system by Microsoft Corp. of Redmond, Wash., United States of America.

In the depicted embodiment ofFIG.2, various I/O devices such as a disk controller204, a graphics adapter224, a video controller202, a keyboard adapter226, a mouse adapter206, a network adapter220, and other I/O devices222can be coupled to system bus214. Keyboard adapter226and mouse adapter206are coupled to a keyboard104(FIGS.1-2) and a mouse110(FIGS.1-2), respectively, of computer system100(FIG.1). While graphics adapter224and video controller202are indicated as distinct units inFIG.2, video controller202can be integrated into graphics adapter224, or vice versa in other embodiments. Video controller202is suitable for refreshing a monitor106(FIGS.1-2) to display images on a screen108(FIG.1) of computer system100(FIG.1). Disk controller204can control hard drive114(FIGS.1-2), USB port112(FIGS.1-2), and CD-ROM and/or DVD drive116(FIGS.1-2). In other embodiments, distinct units can be used to control each of these devices separately.

In some embodiments, network adapter220can comprise and/or be implemented as a WNIC (wireless network interface controller) card (not shown) plugged or coupled to an expansion port (not shown) in computer system100(FIG.1). In other embodiments, the WNIC card can be a wireless network card built into computer system100(FIG.1). A wireless network adapter can be built into computer system100(FIG.1) by having wireless communication capabilities integrated into the motherboard chipset (not shown), or implemented via one or more dedicated wireless communication chips (not shown), connected through a PCI (peripheral component interconnector) or a PCI express bus of computer system100(FIG.1) or USB port112(FIG.1). In other embodiments, network adapter220can comprise and/or be implemented as a wired network interface controller card (not shown).

Although many other components of computer system100(FIG.1) are not shown, such components and their interconnection are well known to those of ordinary skill in the art. Accordingly, further details concerning the construction and composition of computer system100(FIG.1) and the circuit boards inside chassis102(FIG.1) are not discussed herein.

When computer system100inFIG.1is running, program instructions stored on a USB drive in USB port112, on a CD-ROM or DVD in CD-ROM and/or DVD drive116, on hard drive114, or in memory storage unit208(FIG.2) are executed by CPU210(FIG.2). A portion of the program instructions, stored on these devices, can be suitable for carrying out all or at least part of the techniques described herein. In various embodiments, computer system100can be reprogrammed with one or more modules, system, applications, and/or databases, such as those described herein, to convert a general purpose computer to a special purpose computer. For purposes of illustration, programs and other executable program components are shown herein as discrete systems, although it is understood that such programs and components may reside at various times in different storage components of computing device100, and can be executed by CPU210. Alternatively, or in addition to, the systems and procedures described herein can be implemented in hardware, or a combination of hardware, software, and/or firmware. For example, one or more application specific integrated circuits (ASICs) can be programmed to carry out one or more of the systems and procedures described herein. For example, one or more of the programs and/or executable program components described herein can be implemented in one or more ASICs.

Turning ahead in the drawings,FIG.3illustrates a block diagram of a system300that can be employed for deduplication of query to assortment pages, according to an embodiment. System300is merely exemplary and embodiments of the system are not limited to the embodiments presented herein. The system can be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, certain elements, modules, or systems of system300can perform various procedures, processes, and/or activities. In other embodiments, the procedures, processes, and/or activities can be performed by other suitable elements, modules, or systems of system300. In some embodiments, system300can include a page generation deduplication system310and/or web server320.

Page generation deduplication system310and/or web server320can each be a computer system, such as computer system100(FIG.1), as described above, and can each be a single computer, a single server, or a cluster or collection of computers or servers, or a cloud of computers or servers. In another embodiment, a single computer system can host page generation deduplication system310and/or web server320. Additional details regarding page generation deduplication system310and/or web server320are described herein.

In some embodiments, web server320can be in data communication through a network330with one or more user devices, such as a user device340. User device340can be part of system300or external to system300. Network330can be the Internet or another suitable network. In some embodiments, user device340can be used by users, such as a user350. In many embodiments, web server320can host one or more websites and/or mobile application servers. For example, web server320can host a web site, or provide a server that interfaces with an application (e.g., a mobile application), on user device340, which can allow users (e.g.,350) to browse and/or search for items (e.g., products, grocery items), to add items to an electronic cart, and/or to purchase items, in addition to other suitable activities. In a number of embodiments, web server320can interface with page generation deduplication system310to obtain new topic pages.

In some embodiments, an internal network that is not open to the public can be used for communications between page generation deduplication system310and web server320within system300. Accordingly, in some embodiments, page generation deduplication system310(and/or the software used by such systems) can refer to a back end of system300operated by an operator and/or administrator of system300, and web server320(and/or the software used by such systems) can refer to a front end of system300, as is can be accessed and/or used by one or more users, such as user350, using user device340. In these or other embodiments, the operator and/or administrator of system300can manage system300, the processor(s) of system300, and/or the memory storage unit(s) of system300using the input device(s) and/or display device(s) of system300.

In certain embodiments, the user devices (e.g., user device340) can be desktop computers, laptop computers, mobile devices, and/or other endpoint devices used by one or more users (e.g., user350). A mobile device can refer to a portable electronic device (e.g., an electronic device easily conveyable by hand by a person of average size) with the capability to present audio and/or visual data (e.g., text, images, videos, music, etc.). For example, a mobile device can include at least one of a digital media player, a cellular telephone (e.g., a smartphone), a personal digital assistant, a handheld digital computer device (e.g., a tablet personal computer device), a laptop computer device (e.g., a notebook computer device, a netbook computer device), a wearable user computer device, or another portable computer device with the capability to present audio and/or visual data (e.g., images, videos, music, etc.). Thus, in many examples, a mobile device can include a volume and/or weight sufficiently small as to permit the mobile device to be easily conveyable by hand. For examples, in some embodiments, a mobile device can occupy a volume of less than or equal to approximately 1790 cubic centimeters, 2434 cubic centimeters, 2876 cubic centimeters, 4056 cubic centimeters, and/or 5752 cubic centimeters. Further, in these embodiments, a mobile device can weigh less than or equal to 15.6 Newtons, 17.8 Newtons, 22.3 Newtons, 31.2 Newtons, and/or 44.5 Newtons.

Exemplary mobile devices can include (i) an iPod®, iPhone®, iTouch®, iPad®, MacBook® or similar product by Apple Inc. of Cupertino, Calif., United States of America, (ii) a Lumia® or similar product by the Nokia Corporation of Keilaniemi, Espoo, Finland, and/or (iii) a Galaxy™ or similar product by the Samsung Group of Samsung Town, Seoul, South Korea. Further, in the same or different embodiments, a mobile device can include an electronic device configured to implement one or more of (i) the iPhone® operating system by Apple Inc. of Cupertino, Calif., United States of America, (ii) the Android™ operating system developed by the Open Handset Alliance, or (iii) the Windows Mobile™ operating system by Microsoft Corp. of Redmond, Wash., United States of America.

In many embodiments, page generation deduplication system310and/or web server320can each include one or more input devices (e.g., one or more keyboards, one or more keypads, one or more pointing devices such as a computer mouse or computer mice, one or more touchscreen displays, a microphone, etc.), and/or can each comprise one or more display devices (e.g., one or more monitors, one or more touch screen displays, projectors, etc.). In these or other embodiments, one or more of the input device(s) can be similar or identical to keyboard104(FIG.1) and/or a mouse110(FIG.1). Further, one or more of the display device(s) can be similar or identical to monitor106(FIG.1) and/or screen108(FIG.1). The input device(s) and the display device(s) can be coupled to page generation deduplication system310and/or web server320in a wired manner and/or a wireless manner, and the coupling can be direct and/or indirect, as well as locally and/or remotely. As an example of an indirect manner (which may or may not also be a remote manner), a keyboard-video-mouse (KVM) switch can be used to couple the input device(s) and the display device(s) to the processor(s) and/or the memory storage unit(s). In some embodiments, the KVM switch also can be part of page generation deduplication system310and/or web server320. In a similar manner, the processors and/or the non-transitory computer-readable media can be local and/or remote to each other.

Meanwhile, in many embodiments, page generation deduplication system310and/or web server320also can be configured to communicate with one or more databases, such as a database system315. The one or more databases can include a product database that contains information about products, items, or SKUs (stock keeping units), for example, among other information, such as browse shelves and vector representations of such browse shelves, topic pages, and/or other suitable information, as described below in further detail. The one or more databases can be stored on one or more memory storage units (e.g., non-transitory computer readable media), which can be similar or identical to the one or more memory storage units (e.g., non-transitory computer readable media) described above with respect to computer system100(FIG.1). Also, in some embodiments, for any particular database of the one or more databases, that particular database can be stored on a single memory storage unit or the contents of that particular database can be spread across multiple ones of the memory storage units storing the one or more databases, depending on the size of the particular database and/or the storage capacity of the memory storage units.

In many embodiments, page generation deduplication system310can include a communication system311, a machine learning system312, a scoring system313, a page generation system314, and/or database system315. In many embodiments, the systems of page generation deduplication system310can be modules of computing instructions (e.g., software modules) stored at non-transitory computer readable media that operate on one or more processors. In other embodiments, the systems of page generation deduplication system310can be implemented in hardware. Page generation deduplication system310and/or web server320each can be a computer system, such as computer system100(FIG.1), as described above, and can be a single computer, a single server, or a cluster or collection of computers or servers, or a cloud of computers or servers. In another embodiment, a single computer system can host page generation deduplication system310and/or web server320. Additional details regarding page generation deduplication system310and the components thereof are described herein.

In many embodiments, system300can generate new topic pages for a website of an ecommerce retailer. For example, the topic pages can be assortment pages or category pages that include items that capture trending purchasing intents of uses. Topic pages can be based on keywords, such as queries. For example, a topic page can be generated based on the query “shoes adidas girls,” such that the topic page includes a listing of items are based on that query. Unlike a search result page, which are generated typically in real-time in response to search queries from users and which are not crawled by third-party search engines, topic pages can be pre-generated and stored to allowed third-party search engines to crawl the topic pages. The third-party search engines can be Google, Bing, Yahoo, or another suitable third-party search engine. When the topic pages have been crawled by third-party search engines, these topic pages can be listed as results of search queries to the third-party search engines, which can drive additional traffic to the website of the ecommerce retailer.

A website for an e-commerce retailer often includes browse pages. These browse pages can be pages that list items according to the categorical taxonomy of the products. For example, a browse shelf of “Outdoor Griddle Tools & Accessories,” which can have a primary category path within the product taxonomy of “Patio & Garden/Grills & Outdoor Cooking/Outdoor Cooking Tools & Accessories/Outdoor Griddle Tools & Accessories.” The browse page for the browse shelf “Outdoor Griddle Tools & Accessories” can list items that are categorized into that particular category of the product taxonomy. Many browse pages for browse shelves can exist. For example, in some examples, there can be 40,000 different browse shelf pages on the website.

Generating new topic pages based on keyword queries can result in topic pages that overlap with browse pages. Jumping ahead in the drawings,FIG.5illustrates various different scenarios501-505showing differences in scope between topic pages based on keyword queries and browse shelf pages. Scenario501represents cases in which the scope of the keyword query is almost the same as the scope of the browse shelf page. For example, scenario501can apply when the keyword query is “nike shoes for kids” and the browse shelf page is for “kids' nike shoes.” Most of the items on the two pages would be the same.

Scenario502represents cases in which the scope of the keyword query is within (i.e. a subset of) the scope of the browse shelf page. For example, scenario502can apply when the keyword query is “red nike shoes for girls” and the browse shelf page is for “kids' nike shoes.” The items on the topic page would generally be included on the browse shelf page, but the browse shelf page would include other items that are not included on the topic page.

Scenario503represents cases in which the scope of the browse shelf page is within (i.e. a subset of) the scope of the keyword query. For example, scenario503can apply when the keyword query is “nike shoes” and the browse shelf page is for “kids' nike shoes.” The items on the browse shelf page would generally be included on the topic page, but the topic page would include other items that are not included on the browse shelf page.

Scenario504represents cases in which the scope of the keyword query overlaps somewhat with the scope of the browse shelf page. For example, scenario504can apply when the keyword query is “girls' shoe” and the browse shelf page is for “kids' nike shoes.” Some of the items on the browse shelf page would also be included on the topic page, but the topic page would include other items that are not included in the browse shelf page, and the browse shelf page also would include other items that are not included in the topic page.

Scenario505represents cases in which the scope of the keyword query is far from the scope of the browse shelf page. For example, scenario505can apply when the keyword query is “chocolate milk” and the browse shelf page is for “kids' nike shoes.” There is generally no overlap between the items on the two pages.

It can be beneficial to generate the topic pages in scenarios502-505, as there is a different scope of coverage, but it can be beneficial to not generate the topic page in scenario501, as the topic page would be nearly a duplicate of the browse page. Conventional models for keyword classification are sensitive to differences in scenarios504and505, but are generally not sensitive to differences between the scenarios in group510, which includes scenarios501-503, as keyword classification is generally not sensitive to differences in semantic scope. Semantic scope can refer to the specificity of a keyword or category. For example, “shoes” has a larger scope than “red nike shoes.” In many embodiments, system300(FIG.3) can capture differences within group510, in order to distinguish scenario501its own group511as different from the rest of group510. System300(FIG.3) can deduplicate keywords that fall within group511, such that topic pages are not generated for those keywords, while keeping the keywords in scenarios502-505to generate topic pages for those keywords. In many embodiments, system300(FIG.3) can build text semantic similarity analysis models to perform keyword (or query) deduplication, which can effectively locate similar existing browse shelf pages given a keyword.

Turning back in the drawings,FIG.4illustrates a flow chart for a method400of providing deduplication of query to assortment pages, according to an embodiment. Method400is merely exemplary and is not limited to the embodiments presented herein. Method400can be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, the procedures, the processes, and/or the activities of method400can be performed in the order presented. In other embodiments, the procedures, the processes, and/or the activities of method400can be performed in any suitable order. In still other embodiments, one or more of the procedures, the processes, and/or the activities of method400can be combined or skipped.

In many embodiments, system300(FIG.3), page generation deduplication system310(FIG.3), and/or web server320(FIG.3) can be suitable to perform method400and/or one or more of the activities of method400. In these or other embodiments, one or more of the activities of method400can be implemented as one or more computing instructions configured to run at one or more processors and configured to be stored at one or more non-transitory computer readable media. Such non-transitory computer readable media can be part of system300(FIG.3). The processor(s) can be similar or identical to the processor(s) described above with respect to computer system100(FIG.1).

In some embodiments, method400and other activities in method400can include using a distributed network including distributed memory architecture to perform the associated activity. This distributed architecture can reduce the impact on the network and system resources to reduce congestion in bottlenecks while still allowing data to be accessible from a central location.

Referring toFIG.4, method400can include an activity405of generating, using a semantic embedding generation machine learning model, one or more respective shelf embedding vector representations for each of one or more browse shelves based on a respective shelf name for the each of the one or more browse shelves. The semantic embedding generation machine learning model can be similar or identical to semantic embedding generation machine learning model720(FIG.7, described below), which can be a portion of query classification machine learning model620(FIG.6, described below).

Turning ahead in the drawings,FIG.6shows a flow chart of a method600of using a query classification machine learning model620. Method600is merely exemplary and not limited to the embodiments presented herein. Method600can be employed in many different embodiments or examples not specifically depicted or described herein. Method600can include inputting an input610, such as a keyword, into query classification machine learning model620, which can generate an output630, such as browse shelf classification labels.

In some embodiments, query classification machine learning model620can include a BERT (Bidirectional Encoder Representations from Transformers) layer621, a first feed forward layer622, a second feed forward layer623, and a third feed forward layer624. In many embodiments, the BERT layer can output a vector output based on a neural network-based technique for natural language processing to understand the intent of the keyword. In some embodiments, the output of the BERT layer can be a 786-dimension vector, or another suitable dimension. First feed forward layer622can output a 2048-dimension vector, or another suitable dimension. Second feed forward layer623can a 3072-dimension output vector, or another suitable dimension. For example, the dimension of second feed forward layer623can be 128 dimensions to more than 3072 dimensions. Third feed forward layer624can output a 40,000-dimension vector, or another suitable dimension, which can provide sigmoid functions for calculating the multilabel probabilities for 40,000 browse shelves. Each element of the 40,000-dimension vector can be on a 0-1 scale representing the confidence of the keyword to each of the 40,000 browse shelves. As explained above, a keyword can be categorized to multiple browse shelves and can have a high confidence score in subset and/or superset scenarios (e.g., scenarios502-503).

In many embodiments, the semantic embedding generation machine learning model can be a portion of query classification machine learning model620, as query classification machine learning model620can include third feed forward layer624configured to output a plurality of sigmoid functions for calculating multilabel probabilities across the one or more browse shelves. Instead the semantic embedding generation machine learning model can be similar or identical to a semantic embedding generation machine learning model720, as described inFIG.7. The semantic embedding generation machine learning model can distinguish between scenarios501-503(FIG.5).

Turning ahead in the drawings,FIG.7illustrates a flow chart of a method700of using semantic embedding generation machine learning model720. Method700is merely exemplary and not limited to the embodiments presented herein. Method700can be employed in many different embodiments or examples not specifically depicted or described herein. Method700can include inputting a keyword710, into semantic embedding generation machine learning model720, which can generate an output of a keyword embedding730that corresponds to keyword710. In another example, method700can include inputting a shelf name711into semantic embedding generation machine learning model720, which can generate an output of a shelf embedding731that corresponds to shelf name711

In some embodiments, semantic embedding generation machine learning model720can include BERT (Bidirectional Encoder Representations from Transformers) layer621, first feed forward layer622, and second feed forward layer623, but not include or not use third feed forward layer624(FIG.6). The output of second feed forward layer623(e.g., keyword embedding730or shelf embedding731) can be a learned embedding vector representation corresponding to the input (e.g., keyword710or shelf name711).

Returning toFIG.4, in a number of embodiments, activity405can include an activity410of generating a first respective shelf embedding vector representation of the one or more respective shelf embedding vector representations based on a first respective input to the semantic embedding generation machine learning model for the each of the one or more browse shelves. In some embodiments, the first respective input can be the respective shelf name for the each of the one or more browse shelves. For example, the shelf name “Therapy Hand Exercisers” for a browse shelf can be input into the semantic embedding generation machine learning model (e.g.,720(FIG.7)) to generate a shelf embedding vector representation for that shelf name. Similarly, the shelf names for other browse shelves can be input into the semantic embedding generation machine learning model (e.g.,720(FIG.7)) to generate a shelf embedding vector representation for each of those shelf names.

In some embodiments, additional inputs can be used for each of the browse shelves to generate additional shelf embedding vector representations that represent those browse shelves to more fully capture the intent, scope, and/or coverage of each of those browse shelves. The shelf name can thus be augmented with additional shelf representations to generate multiple embeddings for each browse shelf. In many embodiments, these augmented shelf representations can be concise, specific, and/or semantically understandable. Additional such inputs are described below in connection with activities415and420.

In several embodiments, activity405also can include an activity415of generating a second respective shelf embedding vector representation of the one or more respective shelf embedding vector representations based on a second respective input to the semantic embedding generation machine learning model for the each of the one or more browse shelves. In some embodiments, the second respective input can include a first taxonomy level of a respective primary category path of the respective shelf name for the each of the one or more browse shelves prepended to the respective shelf name for the each of the one or more browse shelves. For example, the primary category path for the browse shelf having the shelf name “Therapy Hand Exercisers” can be “Health/Home Health Care/Physical Therapy/Hand Exercisers/Therapy Hand Exercisers.” The first level of the taxonomy is “Health,” which can be prepended to the shelf name “Therapy Hand Exercisers,” so the second input can be “Health Therapy Hand Exercisers.” For example, “Health Therapy Hand Exercisers” can be input into semantic embedding generation machine learning model720to generate a shelf embedding vector representation.

In a number of embodiments, activity405additionally can include an activity420of generating a third respective shelf embedding vector representation of the one or more respective shelf embedding vector representations based on a third respective input to the semantic embedding generation machine learning model for the each of the one or more browse shelves. In some embodiments, the third respective input can include a shortened version of the respective primary category path of the respective shelf name for the each of the one or more browse shelves. In various embodiments, the shortened version of the respective primary category path of the respective shelf name for the each of the one or more browse shelves can be generated based on the respective primary category path of the respective shelf name for the each of the one or more browse shelves using suffix mapping and keyword filtering.

In some embodiments, suffix mapping can remove words that are duplicate in the primary category path by removing words in a higher level that are also found in a lower level. For example, for the primary category path “Health/Home Health Care/Physical Therapy/Hand Exercisers/Therapy Hand Exercisers,” the term “Health” in the first (highest) taxonomy level is also found in the second taxonomy level, so it is removed from the first taxonomy level. Similarly, the term “Hand Exercisers” is found in both the fourth and fifth levels, so it is removed from the fourth level. The third input thus can be “Home Health Care Physical Therapy Therapy Hand Exercisers.” For example, “Home Health Care Physical Therapy Therapy Hand Exercisers” can be input into semantic embedding generation machine learning model720to generate a shelf embedding vector representation.

In various embodiments, keyword filtering can remove terms from the primary category path that do not contain information about purchasing intent. For example, for a browse shelf with the primary category path “Electronics/Computers/Laptops/Shop Laptops By Brand/Alienware Laptops/All Alienware Laptops,” the terms “Shop,” “By Brand” and “All” can be filtered out. And with additional suffix mapping, the third input can be “Electronics Computers Alienware Laptops.” For example, “Electronics Computers Alienware Laptops” can be input into semantic embedding generation machine learning model720to generate a shelf embedding vector representation.

In some embodiments, after the BERT layer (e.g.,621(FIGS.6-7)) has been trained, the BERT layer can be fine-tuned using back-propagated training with augmented training data comprising augmented shelf representations for the one or more browse shelves. The original training data can be true pairs of query and shelf labels. The augmented training data can additionally include pairs of shelf representations and shelf labels, where the shelf representations are the additional representations for the shelf, such as the second input and third input described above. Blended weights can be considered for the shelf representations. Training the output against the true shelf labels can use back-propagated training to fine-tune the BERT layer and feed forward layers.

In several embodiments, method400also can include an activity425of obtaining a keyword. For example, the keyword can be a keyword that is being considered for generating a topic page, but can be tested to see if any of the existing browse shelf pages already existing would closely match the scope of the topic page. The keyword can be the same as the query that would be used to generate the topic page. In many embodiments, the one or more respective shelf embedding vector representations for the each of the one or more browse shelves can be pre-generated before obtaining the keyword.

In a number of embodiments, method400additionally can include an activity430of generating, using the semantic embedding generation machine learning model, a keyword embedding vector representation based on the keyword. For example, as shown inFIG.7, keyword710can be input into semantic embedding generation machine learning model720to generate keyword embedding730.

Returning toFIG.4, in several embodiments, method400further can include an activity435of determining a respective similarity score between the keyword embedding vector representation and each of the one or more respective shelf embedding vector representations for each of the one or more browse shelves. In many embodiments, activity435can include using a cosine similarity measure to generate the respective similarity score. For example, as shown inFIG.7, keyword embedding730and shelf embedding731can be input into cosine similarity function740to generate similarity score750. Similarly, augmented shelf representations for a browse shelf, as described above, can be used to generate additional shelf embeddings (e.g.,731), which each can be input along with keyword embedding730to generate additional similarity scores for the browse shelf. These similarity scores can be generated across the browse shelves (e.g., 40,000 browse shelves), to see if any of the browse shelves are very close in scope to the keyword.

In a number of embodiments, method400additionally can include an activity440of determining whether any of the respective similarity scores for the one or more respective shelf embedding vector representations across the one or more browse shelves exceed a predetermined threshold value. In some embodiments, the predetermined threshold value can be approximately 0.88 or another suitable threshold value. For example, for a keyword “Tools for Outdoor Griddle,” activity can determine that shelf embedding vector representations for two of the browse shelves exceed the threshold value of 0.88, namely the “Outdoor Griddle Tools & Accessories” browse shelf, and the “Blackstone Tools & Accessories” browse shelf. By contrast, for a keyword “alexa smart home,” it can be determined that none of the shelf embedding vector representations for any existing browse pages exceed the threshold.

In several embodiments, when any of the respective similarity scores for the one or more respective shelf embedding vector representations across the one or more browse shelves exceeds a predetermined threshold value, method400can include an activity445of filtering out the keyword. The keyword “Tools for Outdoor Griddle” can be filtered out for the example provided above, such that this keyword can be “deduplicated,” so that a topic page for this keyword is not generated.

In a number of embodiments, when none of the respective similarity scores for the one or more respective shelf embedding vector representations across the one or more browse shelves exceed a predetermined threshold value, method400can include an activity445of generating a new topic page using the keyword. In many embodiments, the new topic page can include an assortment page that is able to be crawled by a search engine. For example, the keyword can be used as a query in a search engine on the web site hosted by web server320(FIG.3) to generate a topic page for that keyword, and the topic page can be stored and indexed to be crawled by third-party search engines.

In many embodiments, activities425-450can be repeated for each keyword of a list of keywords. In some embodiments, the browse shelves utilized above can include existing browse shelf pages, existing topic pages, existing branded browse pages, existing assortment pages. For topic pages and/or branded browse pages (e.g., “Nike girls' shoes”), the page titles for these pages can be in place of the shelf name.

Returning toFIG.3, in several embodiments, communication system311can at least partially perform activity425(FIG.4) of obtaining a keyword.

In several embodiments, machine learning system312can at least partially perform activity405(FIG.4) of generating, using a semantic embedding generation machine learning model, one or more respective shelf embedding vector representations for each of one or more browse shelves based on a respective shelf name for the each of the one or more browse shelves, activity410(FIG.4) of generating a first respective shelf embedding vector representation of the one or more respective shelf embedding vector representations based on a first respective input to the semantic embedding generation machine learning model for the each of the one or more browse shelves, activity415(FIG.4) of generating a second respective shelf embedding vector representation of the one or more respective shelf embedding vector representations based on a second respective input to the semantic embedding generation machine learning model for the each of the one or more browse shelves, activity420(FIG.4) of generating a third respective shelf embedding vector representation of the one or more respective shelf embedding vector representations based on a third respective input to the semantic embedding generation machine learning model for the each of the one or more browse shelves, and/or activity430(FIG.4) of generating, using the semantic embedding generation machine learning model, a keyword embedding vector representation based on the keyword.

In a number of embodiments, scoring system313can at least partially perform activity435(FIG.4) of determining a respective similarity score between the keyword embedding vector representation and each of the one or more respective shelf embedding vector representations for each of the one or more browse shelves, activity440(FIG.4) of determining whether any of the respective similarity scores for the one or more respective shelf embedding vector representations across the one or more browse shelves exceed a predetermined threshold value, and/or activity445(FIG.4) of filtering out the keyword.

In several embodiments, page generation system314can at least partially perform activity445(FIG.4) of generating a new topic page using the keyword. In many embodiments, the new topic page can include an assortment page that is able to be crawled by a search engine.

In many embodiments, the techniques described herein can provide a practical application and several technological improvements. In some embodiments, the techniques described herein can provide for deduplication of query to assortment pages. The techniques described herein can provide a significant improvement over conventional approaches that fail to account for the semantic scope of keyword queries. In many embodiments, the techniques described herein can support multiple different page types, such as topic pages, browse shelf pages, branded browse pages, or other suitable page types. In several embodiments, the techniques described herein can support multi-page and/or single-page semantic similarity mapping. In some embodiments, the techniques described herein can support automatic filtering with a similarity threshold that can be predetermine and/or configurable. In several embodiments, the techniques described herein can support similarity analysis for arbitrary keyword pairs.

In a number of embodiments, the techniques described herein can solve a technical problem that arises only within the realm of computer networks, as online ordering is a concept that do not exist outside the realm of computer networks. Moreover, the techniques described herein can solve a technical problem that cannot be solved outside the context of computer networks. Specifically, the techniques described herein cannot be used outside the context of computer networks, in view of a lack of data, the lack of browse shelf pages, topic pages, and search pages outside computer networks, and the inability to train the machine-learning recommendation models without a computer.

Various embodiments can include a system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processor to perform certain acts. The acts can include generating, using a semantic embedding generation machine learning model, one or more respective shelf embedding vector representations for each of one or more browse shelves based on a respective shelf name for the each of the one or more browse shelves. The acts also can include obtaining a keyword. The acts additionally can include generating, using the semantic embedding generation machine learning model, a keyword embedding vector representation based on the keyword. The acts further can include determining a respective similarity score between the keyword embedding vector representation and each of the one or more respective shelf embedding vector representations for each of the one or more browse shelves. The acts additionally can include determining whether any of the respective similarity scores for the one or more respective shelf embedding vector representations across the one or more browse shelves exceed a predetermined threshold value. When any of the respective similarity scores for the one or more respective shelf embedding vector representations across the one or more browse shelves exceeds a predetermined threshold value, the acts further can include filtering out the keyword. When none of the respective similarity scores for the one or more respective shelf embedding vector representations across the one or more browse shelves exceed a predetermined threshold value, the acts additionally can include generating a new topic page using the keyword.

A number of embodiments can include a method being implemented via execution of computing instructions configured to run at one or more processors. The method can include generating, using a semantic embedding generation machine learning model, one or more respective shelf embedding vector representations for each of one or more browse shelves based on a respective shelf name for the each of the one or more browse shelves. The method also can include obtaining a keyword. The method additionally can include generating, using the semantic embedding generation machine learning model, a keyword embedding vector representation based on the keyword. The method further can include determining a respective similarity score between the keyword embedding vector representation and each of the one or more respective shelf embedding vector representations for each of the one or more browse shelves. The method additionally can include determining whether any of the respective similarity scores for the one or more respective shelf embedding vector representations across the one or more browse shelves exceed a predetermined threshold value. When any of the respective similarity scores for the one or more respective shelf embedding vector representations across the one or more browse shelves exceeds a predetermined threshold value, the method further can include filtering out the keyword. When none of the respective similarity scores for the one or more respective shelf embedding vector representations across the one or more browse shelves exceed a predetermined threshold value, the method additionally can include generating a new topic page using the keyword.

Although deduplication of query to assortment pages has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes may be made without departing from the spirit or scope of the disclosure. Accordingly, the disclosure of embodiments is intended to be illustrative of the scope of the disclosure and is not intended to be limiting. It is intended that the scope of the disclosure shall be limited only to the extent required by the appended claims. For example, to one of ordinary skill in the art, it will be readily apparent that any element ofFIGS.1-7may be modified, and that the foregoing discussion of certain of these embodiments does not necessarily represent a complete description of all possible embodiments. For example, one or more of the procedures, processes, or activities ofFIG.4may include different procedures, processes, and/or activities and be performed by many different modules, in many different orders. As another example, the systems within system300(FIG.3) can be interchanged or otherwise modified.