Patent Application
20240428295
The present teaching generally relates to computers. More specifically, the present teaching relates to data analytics and application thereof.
With the advancement of the Internet, most people in the society now conduct their daily affairs online, including consuming different types of content (articles or videos), checking out different products, making purchases of just about everything, enjoying entertainment, receiving/providing education, or even taking virtual vacations. Such a shift in social behavior has motivated most entities, including individuals, companies, organizations, universities, or interest groups, to place a tremendous amount of information on the Internet to share, to motivate discussions, and to monetize. In the meantime, online service providers such as content portals, advertisers, or social media platform operators have attempted to improve services by providing more relevant content to each user via personalization based on, e.g., user profiles. This framework 100 is illustrated in
Traditionally, a profile related to a user may include different types of information, as illustrated in
Such commonly used representation (a list with scores) for user interests does not capture complex relationships that often exist among different interests, making it impossible to leveraging such relationships in personalization to improve online services.
Thus, there is a need for a solution that addresses the issues discussed above.
The teachings disclosed herein relate to methods, systems, and programming for information management. More particularly, the present teaching relates to methods, systems, and programming related to hash table and storage management using the same.
In one example, a method, implemented on a machine having at least one processor, storage, and a communication platform capable of connecting to a network for personalized content service. Information related to a user is first obtained with a user profile indicative of multiple interests of the user. User embeddings are computed with respect to some interests of the user based on interest embeddings of such interests to capture semantics of such interests as well as additional interests temporally related to the interests. Personalized content is identified based on the user embeddings and is provided to the user.
In a different example, a system is disclosed for personalized content service. The disclosed system includes a user profile retriever, a user embedding generator, a content search/recommendation engine, and a user interface. The user profile retriever is provided for obtaining information related to a user, including a user profile indicative of multiple interests of the user. The user embedding generator is provided for computing user embeddings with respect to the user based on at least some interest selected from the multiple interests as well as interest embeddings associated with the at least some interest. The user embeddings capture both semantics of the at least some interest and additional interests temporally related to the at least some interest. The content search/recommendation engine is provided for identifying content relating to the at least some interest via personalization based on the user embeddings. Such personalized content is then provided to the user via the user interface.
Other concepts relate to software for implementing the present teaching. A software product, in accordance with this concept, includes at least one machine-readable non-transitory medium and information carried by the medium. The information carried by the medium may be executable program code data, parameters in association with the executable program code, and/or information related to a user, a request, content, or other additional information.
Another example is a machine-readable, non-transitory and tangible medium having information recorded thereon for personalized content service. The information, when read by the machine, causes the machine to perform various steps. Information related to a user is first obtained with a user profile indicative of multiple interests of the user. User embeddings are computed with respect to some interests of the user based on interest embeddings of such interests to capture semantics of such interests as well as additional interests temporally related to the interests. Personalized content is identified based on the user embeddings and is provided to the user.
Additional advantages and novel features will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and the accompanying drawings or may be learned by production or operation of the examples. The advantages of the present teachings may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations set forth in the detailed examples discussed below.
The methods, systems and/or programming described herein are further described in terms of exemplary embodiments. These exemplary embodiments are described in detail with reference to the drawings. These embodiments are non-limiting exemplary embodiments, in which like reference numerals represent similar structures throughout the several views of the drawings, and wherein:
In the following detailed description, numerous specific details are set forth by way of examples in order to facilitate a thorough understanding of the relevant teachings. However, it should be apparent to those skilled in the art that the present teachings may be practiced without such details. In other instances, well known methods, procedures, components, and/or system have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present teachings.
The present teaching discloses an exemplary framework for improved online services with enhanced personalization based on user embeddings that characterize a user's interest in terms of both semantics and temporal implicated interests relevant to the user. This is shown in
According to the present teaching, a user's interests may be represented by both the semantics of estimated interests as well as those interests captured via temporal relationships. In some embodiments, semantic embeddings for an interest (e.g., on one or more topics) may be derived via machine learning based on textual information defining the interest (obtained from, e.g., a commonly recognized platform such as Wikipedia) and optionally also online content related to the topics. By capturing the semantics of each interest via embeddings via learning (from training data), the learned semantic embeddings also capture the relationships among different interests. For example, when embeddings for different interests are projected into the embedding feature space, as illustrated as different projected points 200-1, 200-2, 200-3, and 200-4, etc. in
In addition to capturing the semantics of interests via embeddings, the present teaching also discloses to capture additional interests that, although are not explicitly listed as user's interests, are temporally connected to the user when they co-occur in content under some temporal conditions. Temporal embeddings are used to capture such co-occurring interests and may be derived via machine learning based on online content published within a specified temporal period. This s illustrated in
In some embodiments, as each interest may be represented by the semantic embeddings and temporal embeddings, according to the present teaching, semantic embeddings and temporal embeddings associated with relevant interests of a user may be integrated to generate user embeddings to represent the user's interests and such created user embeddings may be utilized to perform personalization. Such user embeddings capture semantics as well as temporal aspects of user's interests and the relationships thereof. Content identified based on such integrated user embeddings capture knowledge about the user and temporal environment in a way that the traditional user profiles 140 are not capable of providing.
As discussed herein, user embeddings, as illustrated in
According to the present teaching, interest embeddings include both semantic embeddings 380 and temporal embeddings 390, as shown in
Both semantic embeddings 380 for different interests as well as temporal embeddings 390 may be updated in time. Because of different natures of the two, the temporal embeddings 390 may have more of a transient nature and thus may need to be updated accordingly (e.g., more frequently) while the semantic embeddings 380 may be updated with a different schedule (e.g., less frequently). As such, the first part of the content service engine 210 may operate independently from other part in a continuous manner to maintain an updated storage of interest embeddings 310, which are used by the second part of the content service engine 210 as the basis to construct user embeddings 230 for personalization with respect to individual users according to their dynamically changing interests in providing services.
The second part of the content service engine 210 may be directed to providing personalized services via interest embeddings 310. This part includes a user interface 340, a user profile retriever 330, a user embedding generator 320, optionally a user demographics analyzer 350, and a content search/recommendation engine 370. The user interface 340 is provided to interact with users 110 such as taking a query from a user and provide personalized content to the user according to the query. In a different setting, personalized content may be recommended to users even in absence of users' queries. The user profile retriever 330 may be provided to access user profiles 220 to identify interests of each user to utilize the known interests of a user to dynamically construct user embeddings 230 based on interest embeddings 310 according to the known interests of each user. Such dynamically constructed user embeddings comprehensively represent the interests of the user and may then be used by the content search/recommendation engine 370 to search online content consistent with the interests of the user. In some embodiments, user's demographic information may also be used, either jointly or separately, to identify via search, online content of user's interests according to some configuration stored in 360 that specifies the relationship between demographics and topics of interest in content. Such searched online content is then delivered to the user via the user interface 340.
In this illustrated embodiment, the second part is disclosed for either searching or recommending content in a personalized manner based on user embeddings. The embeddings (including semantic embeddings, temporal embeddings, interest embeddings, and user embeddings) as disclosed herein may be used or leveraged in other applications as well. As discussed herein with reference to
This may be leveraged in different use cases. For instance, the semantic affinity in different interests may be used to, e.g., explore additional user interests by finding similar interests. If a user is interested in a particular topic (e.g., natural healing) represented by corresponding semantic embeddings as a projected point, e.g., 200-1 in
To generate temporal embeddings for each interest, the interest embedding generator 300 may also obtain, at 410, online content related to the interest within a temporal period specified by a sliding window as training data for learning the temporal embeddings for the interest. Based on such collected training data, the interest embedding generator 300 may then apply machine learning to train, at 420, the semantic embeddings 380 and temporal embeddings 390 to create the embeddings for each of the interests at 430. Once interest embeddings for different interests are established, the interest embedding generator 300 may regularly update the interest embeddings according to appropriate schedules. For instance, depending on the temporal period, the temporal embeddings 390 may need to be retrained with a higher frequency than that for semantic embeddings 380. The interest embedding generator 300 may check, at 440, whether an update is needed. If so, the processing proceeds to step 400 to carry out the needed re-training based on newly collected training data. Otherwise, the interest embedding generator 300 continues to check the need for re-training any part of the interest embeddings.
In some embodiments, the top K interests may also be selected based on other criteria appropriate for an application. For instance, a user interacting with the user interface 340 may enter a query specifically related to an interest category, e.g., physical therapy. In this case, the top K interests selected from the user profile may correspond to those that fall into a category related to physical therapy, even though the user may have strong interests in electric vehicles and cooking (i.e., having high scores in these interests) and a minor interest in health (i.e., having lower scores in health-related topics). In this case, interest “health” may be selected even though it has a lower score and interests associated with electric vehicle and cooking may not be selected despite that they have higher scores. That is, how top K interests are selected may be specified to be situation dependent and different selection conditions may be applied with respect to different situations.
Based on each of the selected top K interests, the interest embeddings (including both semantic embeddings 380 and temporal embeddings 390) for each of the K interests may be retrieved, at 470, from the interest embedding storage 310. The retrieved semantic and temporal embeddings associated with the K interests of the user may then be used for constructing, at 480, the user embeddings 230 (or individualized interest embeddings) for the user. With the constructed user embeddings for the user, the content search/recommendation engine 370 identifies, at 490, personalized content for the user that matches the user's interests captured by the user embeddings. There may be different ways to construct user embeddings based on semantic/temporal embeddings (380 & 390) corresponding to top K interests of the user. In some embodiments, the embeddings for the selected interests may be aggregated to generate the user embeddings for the user. For example, the following formulation may be used to aggregate interest embeddings of different interests to generate user embeddings for the user:
where X is an aggregated attribute value of the user embeddings, n is the number of selected interests, and xi is the embedding attribute value of the interest embeddings associated with interest i. In some embodiments, the embedding attribute values xi may correspond to scaled attribute values, obtained by scaling the attribute values using, e.g., the score associated with the corresponding interest. Other means to aggregate interest embeddings of different interests to generate user embeddings may also be employed.
To build semantic embeddings for different interests, the semantic entity determiner 500 is provided for identifying entities (or interests) and their textual definitions from, e.g., Wikipedia. Such information on each interest may be used to train the semantic embeddings and may be stored in semantics training data storage 520 as training data. In some embodiments, articles that elaborate on the meaning of an interest, e.g., links to online articles provided by Wikipedia, may be used by the interest-based content retriever 510 to obtain additional text information associated with each entity/interest and save such additional information as additional semantics training data 520 related to the same interest. The collected textual information or training data associated with different interests in 520 may then be used by the semantic embedding learning unit 530 to conduct machine learning to derive the semantic embeddings 380 for different entities/interests.
To build temporal embeddings, a temporal window 550 is configured and stored in 550. For example, the temporal window 550 may be set to be, e.g., 48 hours. In operation, with the specified temporal window, the temporal article data retriever 540 may obtain articles falling within the configured temporal window so that they may be used to derive temporal embeddings. In some embodiments, based on the retrieved temporal articles, the article entity determiner 560 may process each of such articles to identify entities appearing in each of the articles. Such entities may include co-occurring interests. For instance, in articles on 2020 election, entity “Joe Biden” and entity “Presidential Election” may frequently occur in the same articles. In addition, in articles on environment, entities “global warming” and “environment pollution” may also co-occurring therein. The ranked entity list generator 570 may be provided to rank the entities identified from different temporal articles according to, e.g., their relevance to the articles. The ranked lists of entities 580 of different articles may then be provided to the temporal embedding learning unit 590 as training data to derive temporal embeddings 390.
To implement various modules, units, and their functionalities described in the present disclosure, computer hardware platforms may be used as the hardware platform(s) for one or more of the elements described herein. The hardware elements, operating systems and programming languages of such computers are conventional in nature, and it is presumed that those skilled in the art are adequately familiar with to adapt those technologies to appropriate settings as described herein. A computer with user interface elements may be used to implement a personal computer (PC) or other type of workstation or terminal device, although a computer may also act as a server if appropriately programmed. It is believed that those skilled in the art are familiar with the structure, programming, and general operation of such computer equipment and as a result the drawings should be self-explanatory.
Computer 700, for example, includes COM ports 750 connected to and from a network connected thereto to facilitate data communications. Computer 700 also includes a central processing unit (CPU) 720, in the form of one or more processors, for executing program instructions. The exemplary computer platform includes an internal communication bus 710, program storage and data storage of different forms (e.g., disk 770, read only memory (ROM) 730, or random-access memory (RAM) 740), for various data files to be processed and/or communicated by computer 700, as well as possibly program instructions to be executed by CPU 720. Computer 700 also includes an I/O component 760, supporting input/output flows between the computer and other components therein such as user interface elements 780. Computer 700 may also receive programming and data via network communications.
Hence, aspects of the methods of information analytics and management and/or other processes, as outlined above, may be embodied in programming. Program aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of executable code and/or associated data that is carried on or embodied in a type of machine readable medium. Tangible non-transitory “storage” type media include any or all of the memory or other storage for the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide storage at any time for the software programming.
All or portions of the software may at times be communicated through a network such as the Internet or various other telecommunication networks. Such communications, for example, may enable loading of the software from one computer or processor into another, for example, in connection with information analytics and management. Thus, another type of media that may bear the software elements includes optical, electrical, and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links, or the like, also may be considered as media bearing the software. As used herein, unless restricted to tangible “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.
Hence, a machine-readable medium may take many forms, including but not limited to, a tangible storage medium, a carrier wave medium or physical transmission medium. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, which may be used to implement the system or any of its components as shown in the drawings. Volatile storage media include dynamic memory, such as a main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that form a bus within a computer system. Carrier-wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer may read programming code and/or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a physical processor for execution.
Those skilled in the art will recognize that the present teachings are amenable to a variety of modifications and/or enhancements. For example, although the implementation of various components described above may be embodied in a hardware device, it may also be implemented as a software only solution, e.g., an installation on an existing server. In addition, the techniques as disclosed herein may be implemented as a firmware, firmware/software combination, firmware/hardware combination, or a hardware/firmware/software combination.
While the foregoing has described what are considered to constitute the present teachings and/or other examples, it is understood that various modifications may be made thereto and that the subject matter disclosed herein may be implemented in various forms and examples, and that the teachings may be applied in numerous applications, only some of which have been described herein. It is intended by the following claims to claim any and all applications, modifications and variations that fall within the true scope of the present teachings.
