Content recommendation method, device, equipment and storage medium

Abstract

This disclosure provides a content recommendation method, apparatus, device, and storage medium, relating to the fields of data processing technology, particularly big data and information flow. A specific implementation involves: obtaining point of interest information, including at least one of workplace information and behavioral information; determining a target industry based on the point of interest information; and recalling recommended content associated with the target industry. The techniques disclosed herein can improve the accuracy of the recall results.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, in particular to the field of big data and information flow, and specifically to a content recommendation method, apparatus, device and storage medium. Background Art

[0002] In related technologies, product recommendations for B2B (Business-to-Business) scenarios typically adopt the same recommendation strategies as those for B2C (Business-to-Consumer) scenarios. However, since B-end users and C-end users differ significantly in both decision-making costs and usage habits, the recommendation strategies for B2C scenarios are not well-suited to product recommendation scenarios in B2C scenarios, resulting in the recalled recommendation content failing to meet user needs. Summary of the Invention

[0003] The present disclosure provides a content recommendation method, apparatus, device, and storage medium.

[0004] According to one aspect of the present disclosure, a content recommendation method is provided, comprising:

[0005] Acquiring point of interest information, where the point of interest information includes at least one of work location information and behavior information;

[0006] Determine target industries based on point of interest information;

[0007] Recall recommendations related to your target industry.

[0008] According to another aspect of the present disclosure, there is provided a content recommendation device, comprising:

[0009] an acquisition module, configured to acquire information about points of interest, the information about points of interest including at least one of work location information and behavior information;

[0010] A target industry determination module is used to determine the target industry based on the point of interest information;

[0011] The recall module is used to recall recommended content related to the target industry.

[0012] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the method in any embodiment of the present disclosure.

[0016] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided. The computer instructions are used to cause a computer to execute the method in any embodiment of the present disclosure.

[0017] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, which implements the method in any embodiment of the present disclosure when executed by a processor.

[0018] According to the content recommendation method of the embodiment of the present disclosure, the accuracy of the recall result can be improved.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0021] Figure 1 A flowchart showing a content recommendation method according to an embodiment of the present disclosure is shown;

[0022] Figure 2 A specific flow chart showing the determination of target industries in the content recommendation method according to an embodiment of the present disclosure;

[0023] Figure 3 A specific flow chart showing the recall of recommended content in the content recommendation method according to an embodiment of the present disclosure;

[0024] Figure 4 A schematic diagram illustrating construction of an industry relationship map according to a content recommendation method according to an embodiment of the present disclosure;

[0025] Figure 5 A diagram showing an application scenario of the content recommendation method according to an embodiment of the present disclosure;

[0026] Figure 6 A block diagram showing a content recommendation device according to an embodiment of the present disclosure;

[0027] Figure 7 4 is a block diagram of an electronic device for implementing the content recommendation method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0029] B2B (Business-to-Business) refers to a business model in which businesses exchange and transfer data and information over dedicated networks or the internet to conduct transactions. A B2B platform is a comprehensive platform for businesses (buyers) and (sellers) to exchange products, services, and information over the internet. It not only aggregates information on high-quality sources for buyers, optimizing procurement efficiency, but also empowers sellers, providing them with a platform and diverse marketing methods, maximizing information matching between the two parties on the platform.

[0030] B2C (Business-to-Consumer) refers to a model of e-commerce, a retail model that sells products and services directly to consumers. Product recommendations are a very common and mature function in B2C e-commerce scenarios, and the traffic volume they drive even accounts for half of the platform's traffic. The significance of product recommendations lies in the following: First, for the platform, product recommendation traffic, as the platform's mobile traffic, is not affected by other factors such as relevance, and can fully leverage its equity value to become one of the platform's most flexible traffic resources; second, for buyers, product recommendations can improve sourcing efficiency and meet the needs of multiple parties for price comparison; third, product recommendations can provide sellers with more traffic support, thereby improving seller conversion efficiency.

[0031] Recommendation strategies are based on matching users, products, and marketplaces. User cold start is essential for recommendations. A newly registered user has no activity on the platform, so even with a wealth of product and scenario features, it's difficult to predict their true needs. Therefore, effective cold start recommendation strategies are crucial. Traditional cold start recommendation strategies focus on recommending popular products, low-priced items, or products with attractive images, catering to user preferences for practicality, aesthetics, novelty, and the desire to follow the crowd.

[0032] In related technologies, the recommendation strategies used for B2B scenarios are exactly the same as those used for B2C scenarios. However, B-end users and C-end users differ significantly in both decision-making costs and usage habits. Therefore, existing recommendation strategies for B2C scenarios are not well suited for product recommendation scenarios in B2C scenarios. Especially during the user cold start phase, B2C scenarios do not utilize user characteristics because users have no activity on the platform. Instead, they use global hot strategies or low-price strategies to meet the emotional consumption needs of most new users. However, B2B users are rational and purposeful, and reusing the recommendation strategies for B2C scenarios will bring the following problems:

[0033] (1) For users, the accuracy of recommendation services is low, and it is difficult for users to stimulate more demand through product recommendations;

[0034] (2) For the platform, wasting traffic reduces traffic flow efficiency and reduces users' trust in the platform.

[0035] In response to the content recommendation strategy in the B2B scenario in the related art, this briefing proposal proposes a content recommendation method to solve at least one of the above technical problems existing in the related art.

[0036] Refer to the following Figures 1 to 5 The content recommendation method according to the embodiment of the present disclosure is described. Figure 1 As shown, the content recommendation method of the embodiment of the present disclosure specifically includes the following steps:

[0037] S101: Acquire point of interest information, where the point of interest information includes at least one of work location information and behavior information;

[0038] S102: Determine the target industry based on the point of interest information;

[0039] S103: Recall recommended content related to the target industry.

[0040] The content recommendation method of the embodiment of the present disclosure can be used in a product recommendation scenario. More specifically, it can be used to recommend products to a target object in a B2B or B2C e-commerce scenario, where the target object can be a company or individual serving as a buyer.

[0041] For example, in step S101, the point of interest information may be information that has a strong correlation with the point of interest of the target object, or information that can reflect the real needs of the target object to a certain extent.

[0042] It should be noted that the acquisition of POI information in this embodiment is authorized by the target subject, that is, it is actively or passively acquired with the target subject's permission. For example, POI information can be actively acquired from the target subject's terminal after the user signs a privacy agreement or agrees to an inquiry request. In another example, POI information can be received from a device terminal in response to an operation command initiated by the user on the terminal, with the user actively filling in the relevant information.

[0043] Understandably, to effectively integrate people, goods, and venues in B2B scenarios, it's crucial to understand user needs that are closest to real-world production practices, thereby uncovering more authentic and effective data features. Traditional user cold-start strategies are inappropriate for B2B scenarios because they don't effectively leverage the behavioral data of the target audience. However, B2B scenarios are characterized by significant industry diversity and distinct industry characteristics. This necessitates that POI information isn't limited to single-point features of individuals. Instead, it aggregates common industry features into demographic features, leveraging these features to improve user cold-start efficiency and continuously enhance the accuracy of recommendation services.

[0044] The B2B industry has distinct demographics, with significant differentiation between individuals. This leads to distinct behavioral habits among individuals in different industries. Conversely, we can use these behavioral patterns to predict a user's industry and improve the accuracy of user cold start predictions using this industry information. Specifically, the target user's workplace and behavioral information can better reflect the differentiation between individuals in different industries, thereby improving the accuracy of target industry predictions.

[0045] For example, in step S102 , the industrial belt where the target object's workplace is located is determined based on the acquired workplace information, and then the target industry where the target object is located is predicted based on the industrial belt.

[0046] It's understandable that the formation of industrial belts is a prominent feature of regional economic development. In the early stages of an industrial belt's formation, enterprises' locational behavior, influenced by environmental conditions, tends to concentrate in advantageous locations, subsequently developing into several urban industrial clusters. As operations progress, enterprises diffuse outward from the center along the axis. These two spatial processes both promote and constrain each other, forming industrial belts. Thus, industrial belts can represent the high concentration of a particular industry within a given region, allowing for relatively accurate predictions of the target industry of a target enterprise.

[0047] Based on the obtained behavioral information, the target industry of the target object can be predicted based on the target object's operational behavior information on the terminal, such as the target object's website browsing history or APP loading history on the terminal.

[0048] Understandably, the B2B industry is highly specialized, and its market conditions are susceptible to multiple factors, including seasonality and policy. Market prices for raw materials (such as steel and cotton) fluctuate daily, and both traders and manufacturers are constantly monitoring these trends. Consequently, industry professionals frequently visit websites or apps for relevant information. Industrial products with a certain level of technical expertise (such as chips and precision instruments) often come with specialized instruction manuals or technical brochures. This technical information is often displayed on the brand's official website or specialized apps, leading industry professionals to frequently visit these websites or apps. Furthermore, there are specialized procurement platforms for specific verticals (such as Zhongsu Online and Zhubajie), which boast high penetration within their respective industries. In summary, B2B market participants frequently visit certain apps and websites to effectively access various information, including procurement, information, and technical skills. Therefore, based on the target user's website browsing history or app download history on their device, it is possible to effectively predict the user's target industry.

[0049] For example, in step S103, for a product recommendation scenario, the recommended content can be products. Based on the attributes or categories of the products, an industry tag corresponding to the selected industry can be pre-added to each product. After the target industry is determined from the multiple selected industries in step S102, at least one product under the industry tag corresponding to the target industry can be recalled as recommended content and recommended to the target user.

[0050] In an embodiment of the present disclosure, the determined target industry may be one or more, and in the case of multiple target industries, corresponding recommended content may be recalled for each target industry, sorted according to preset weights, and recommended to the target object.

[0051] According to the method of the embodiment of the present disclosure, by obtaining the point of interest information of the target object, such as the workplace information and / or behavior information of the target object, the target industry of the target object is predicted based on the workplace information and / or behavior information of the target object, and then the recommended content is recalled based on the target industry. Therefore, for product recommendation scenarios, especially product recommendation scenarios in the B2B industry, the target industry of the target object can be effectively predicted, thereby improving the accuracy of the recall results.

[0052] Furthermore, for cold-start recommendations in B2B scenarios, we can efficiently and accurately recommend content that is highly relevant to users' interests without having to rely on their historical behavior on the platform. This significantly improves the accuracy of recommendation services, thereby stimulating more user demands through recommended content. For B2B platforms, this also improves traffic flow efficiency and users' trust in the B2B platform.

[0053] like Figure 2 As shown, in one embodiment, step S102 includes:

[0054] S201: Determine, based on the workplace information, an industrial cluster associated with the workplace information;

[0055] S202: Determine the first target industry corresponding to the associated industrial clusters using a pre-configured first industry relationship map; the first industry relationship map is used to characterize the mapping relationship between each cluster and each industry.

[0056] It is understandable that the B2B scenario has obvious industrial agglomeration phenomenon. Industrial agglomeration is the product of industrial development to a certain stage, and the strong correlation between industrial agglomeration and industrial competitiveness will strengthen over time. Therefore, the industrial agglomeration phenomenon will become more and more obvious with the development of industrialization.

[0057] For example, as shown in Table 1, the first industry relationship map is used to represent the mapping relationship between region, industrial cluster, and candidate industry. Based on the target person's workplace information, it is determined whether the target person's workplace is within the industrial cluster of the corresponding region. If the target person's workplace is within the industrial cluster, the target person's industrial cluster is determined, and the candidate industry corresponding to the industrial cluster is determined as the target person's first target industry.

[0058]

[0059]

[0060] Table 1

[0061] In one specific example, the center of the product belt can be used as the center of a circle, and the radius can be set as the radius to determine the influence range of the industrial belt. The obtained work location information is then intersected with the product belt's influence range. If the work location information intersects with the product belt's influence range, the target object's industrial cluster is determined.

[0062] Through the above implementation, the first target industry of the target object can be quickly and efficiently determined based on the workplace information of the target object using the first industry relationship map.

[0063] In one embodiment, the behavior information includes at least one of website browsing history information and terminal APP loading information. Step S102 includes:

[0064] Based on the website browsing record information and / or terminal APP loading information, the second target industry is determined using a pre-configured second industry relationship map; the second industry relationship map is used to characterize the mapping relationship between the website and / or APP and each industry.

[0065] Website Name Keywords Selected industries Armored Net Category website: Excavator special website Mechanical industry, hardware,… ABB official website Brand website: Electronic component brand Electronics 3C, electrical engineering,... Global Plastics Network Industry website: Plastics industry vertical website Rubber and plastics, chemicals,…

[0066] Table 2

[0067] For example, as shown in Table 2, the second industry relationship map is used to characterize the mapping relationship between website name, keyword, and selected industry, where the keyword is used to characterize the type or attribute of the website. Website browsing record information may include the target object's website browsing record on the terminal. The website browsing record information is matched with the second industry relationship map. Based on the website name matched by the website browsing record in the second industry relationship map, the corresponding selected industry is obtained, and the corresponding selected industry is determined as the second target industry. For example, if the website browsing record information matches the website name "Tiejia.com" in the second industry relationship map, then based on the mapping relationship in the second industry relationship map, the selected industries "Mechanical Industry" and "Hardware" corresponding to "Tiejia.com" can be directly determined, and "Mechanical Industry" and "Hardware" are determined as the second target industry.

[0068]

[0069] Table 3

[0070] Exemplarily, as shown in Table 3, the second industry relationship map is used to characterize the mapping relationship between APP name-keyword-selected industry, wherein the keyword is used to characterize the type or attribute of the APP. The terminal APP loading information may include the APP record loaded by the target object on the terminal, and the terminal APP loading information is matched with the second industry relationship map. According to the APP name matched by the terminal APP loading information in the second industry relationship map, the corresponding selected industry is obtained, and the corresponding selected industry is determined as the second target industry. For example, the terminal APP loading information matches the APP name of "Zhongsu Online" in the second industry relationship map, then according to the mapping relationship in the second industry relationship map, the selected industries "rubber plastics" and "chemicals" corresponding to "Zhongsu Online" can be directly determined, and "rubber plastics" and "chemicals" are determined as the second target industries.

[0071] Through the above implementation, the second target industry of the target object can be quickly and efficiently determined based on the target object's website browsing record information and / or terminal APP loading information using the second industry relationship map.

[0072] In one embodiment, step S101 includes:

[0073] In response to an operation instruction of the terminal, at least one of the terminal's location information, browser browsing history information, and loaded APP information is obtained.

[0074] Specifically, the terminal can be a mobile phone, PC, tablet computer, or other electronic device. The target object's workplace information can be directly determined through the terminal's positioning information; the target object's website browsing history information can be directly determined through the browser's browsing history information; and the target object's terminal app installation information can be directly determined through the terminal's installed app information.

[0075] Exemplarily, the terminal's operation instruction can be an operation instruction initiated by the terminal, or an operation instruction initiated by the terminal after the target object performs a corresponding operation on the terminal. For example, after the target object agrees to the privacy agreement, the terminal actively generates an operation instruction and sends it to the server. In response to the operation instruction, the server obtains at least one of the terminal's location information, browser browsing history information, and loaded APP information. For another example, the target object can enter the above information in the terminal, and then send a reception request for the above information to the server through the terminal. In response to the acceptance request, the server receives the above information filled in by the target object from the terminal.

[0076] Through the above implementation, the point of interest information of the target object can be obtained based on the authorization permission of the target object, and the acquisition method is relatively fast and simple.

[0077] like Figure 3 As shown, in one embodiment, step S103 includes:

[0078] S301: Obtaining business model information;

[0079] S302: Recall recommended content related to the target industry based on the business model information.

[0080] Business model information is used to characterize the target entity's corporate nature or profit model. For example, business model information can specifically include manufacturers and traders. It is understood that manufacturers are businesses that earn profits from their products, excluding production materials and production-related expenses, while traders are businesses that purchase products from manufacturers and sell them to consumers, earning the difference.

[0081] For example, the business model information can be obtained based on the business card information that the target object actively fills out on the B2B platform.

[0082] According to the above embodiment, by recalling recommended content based on the target object's business model information, targeted recommendations can be made based on the user's business model, thereby improving the recall precision of recommended content and further meeting the real needs of the target object.

[0083] In one embodiment, the business model information includes manufacturers and traders, and the recommended content includes upstream product content and co-travel product content. Step S302 includes:

[0084] In the case where the business model information is a manufacturer, recall the upstream product content related to the target industry;

[0085] When the business model information is a trader, recall the same travel product content related to the target industry.

[0086] In one example, the target industry is small home appliances. If the target's business model information is manufacturer, then other upstream products related to small home appliances, such as molding machines, polypropylene, and wires, will be recalled. If the target's business model information is trader, then other related products, such as kettles and electric fans, will be recalled.

[0087] In addition, in other examples of the present disclosure, if the business model information of the target object is not obtained, popular product content associated with the target industry is recommended to the target object.

[0088] Through the above implementation method, based on the target object's business model information, the target object is recommended with related product content or upstream product content related to the target industry, which can make the recommended content more consistent with the target object's business model, thereby increasing the probability of hitting the target object's interest points, and further improving the target object's usage experience.

[0089] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved (such as work location information and behavior information contained in point of interest information) comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0090] In a specific example, Figure 4 As shown, the construction method of the first industry relationship map and the second industry relationship map is as follows:

[0091] Obtain sample information corresponding to a certain number of sample objects (i.e., a1, a2, a3, b2, and c2 in the diagram). Each sample information item includes information about apps with prominent industry characteristics installed on the phone, historically browsed websites, and information about the industry belt associated with the work location. Cluster this information contained in each sample information item to obtain multiple clustering results, namely, candidate industry A corresponding to sample objects a1, a2, and a3, candidate industry B corresponding to b2, and candidate industry C corresponding to c2. Based on the multiple sample information items and the resulting mapping relationships between the multiple candidate industries, construct a first industry relationship map and a second industry relationship map.

[0092] Refer to the following Figure 5 A specific example of the content recommendation method according to an embodiment of the present disclosure is described.

[0093] like Figure 5 As shown, the content recommendation method of the embodiment of the present disclosure specifically includes the following steps:

[0094] (1) The influence range of the industrial belt is determined by taking the POI (Point of Interest) at the center of the product belt as the center and the coverage radius as the radius;

[0095] (2) Obtain the intersection of the user's workplace information and the product belt's influence range. If there is an intersection between the user's workplace information and the product belt's influence range, predict the user's target industry based on the product belt;

[0096] (3) Recommend upstream products and related products to users based on their industry. If the user is a manufacturer, upstream products are recommended, such as molding machines, PP (polypropylene), and wires. If the user is a trader, related merchants are recommended, such as kettles, electric fans, and other related products. If the user does not have more information, popular products in the industry are recommended.

[0097] According to another aspect of the present disclosure, a content recommendation device is also provided.

[0098] like Figure 6 As shown, the content recommendation device includes:

[0099] An acquisition module 601 is configured to acquire information about points of interest, where the information about points of interest includes at least one of work location information and behavior information.

[0100] A target industry determination module 602 is configured to determine a target industry based on the point of interest information;

[0101] The recall module 603 is used to recall recommended content associated with the target industry.

[0102] In one embodiment, the target industry determination module 602 includes:

[0103] An industrial cluster determination submodule is used to determine the industrial cluster associated with the workplace information based on the workplace information;

[0104] The first target industry determination submodule is used to determine the first target industry corresponding to the associated industrial cluster using a pre-configured first industry relationship map; the first industry relationship map is used to characterize the mapping relationship between each cluster and each industry.

[0105] In one embodiment, the behavior information includes at least one of website browsing record information and terminal APP loading information;

[0106] The target industry determination module 602 includes:

[0107] The second target industry determination submodule is used to determine the second target industry based on the website browsing record information and / or terminal APP loading information using a pre-configured second industry relationship map; the second industry relationship map is used to characterize the mapping relationship between the website and / or APP and each industry.

[0108] In one embodiment, the acquisition module 601 is further configured to:

[0109] In response to an operation instruction of the terminal, at least one of the terminal's location information, browser browsing history information, and loaded APP information is obtained.

[0110] In one embodiment, the recall module 603 includes:

[0111] The business model information acquisition submodule is used to obtain business model information;

[0112] The recall submodule is used to recall recommended content related to the target industry based on the business model information.

[0113] In one embodiment, the business model information includes manufacturers and traders, and the recommended content includes upstream product content and co-travel product content;

[0114] The recall submodule is also used to:

[0115] In the case where the business model information is a manufacturer, recall the upstream product content related to the target industry;

[0116] When the business model information is a trader, recall the same travel product content related to the target industry.

[0117] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0118] Figure 7 A schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0119] like Figure 7As shown, the device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. Various programs and data required for the operation of the device 700 can also be stored in the RAM 703. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0120] Various components in device 700 are connected to I / O interface 705, including an input unit 706, such as a keyboard, mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a magnetic disk, optical disk, etc.; and a communication unit 709, such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows device 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0121] The computing unit 701 can be various general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as the content recommendation method. For example, in some embodiments, the content recommendation method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the content recommendation method described above can be performed. Alternatively, in other embodiments, the computing unit 701 can be configured to perform the content recommendation method by any other appropriate means (e.g., by means of firmware).

[0122] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0123] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0124] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0125] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0126] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0127] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0128] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0129] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A content recommendation method, comprising: Acquiring point of interest information, wherein the point of interest information includes at least one of work location information and behavior information; determining a target industry based on the point of interest information; Recall recommended content associated with the target industry; Determining the target industry based on the point of interest information includes: Determining, based on the work location information, an industrial cluster associated with the work location information; Determine the first target industry corresponding to the associated industrial cluster using a pre-configured first industry relationship map; the first industry relationship map is used to represent the mapping relationship between each cluster and each industry; Wherein, determining an industrial cluster associated with the work location information based on the work location information includes: Determine the industrial belt where the workplace is located based on the workplace information; Determine the influence range of the industrial belt with the center of the industrial belt as the center and the preset coverage radius as the radius; When the work location information intersects with the influence range of the industrial belt, the industrial cluster is determined.

2. The method according to claim 1, wherein The behavior information includes at least one of website browsing record information and terminal APP loading information; Determining the target industry based on the POI information includes: Determine the second target industry using a pre-configured second industry relationship map based on the website browsing history information and / or terminal APP loading information; The second industry relationship map is used to represent the mapping relationship between the website and / or APP and various industries.

3. The method according to claim 1, wherein Get information about points of interest, including: In response to an operation instruction of the terminal, at least one of the terminal's location information, browser browsing history information, and loaded APP information is obtained.

4. The method according to claim 1, wherein Recall recommended content associated with the target industry, including: Obtain business model information; According to the business model information, recommended content associated with the target industry is recalled.

5. The method according to claim 4, wherein The business model information includes manufacturers and traders, and the recommended content includes upstream product content and co-travel product content; Based on the business model information, recall recommended content associated with the target industry, including: In the case where the business model information is the manufacturer, recalling upstream product content associated with the target industry; When the business model information is the trader, the same-travel merchandise content associated with the target industry is recalled.

6. A content recommendation device, comprising: an acquisition module, configured to acquire information about points of interest, wherein the information about points of interest includes at least one of work location information and behavior information; A target industry determination module, configured to determine a target industry based on the point of interest information; A recall module, used to recall recommended content associated with the target industry; Wherein, the target industry determination module includes: An industrial cluster determination submodule, configured to determine an industrial cluster associated with the work location information based on the work location information; A first target industry determination submodule is configured to determine a first target industry corresponding to the associated industrial cluster using a pre-configured first industry relationship map; the first industry relationship map is configured to represent a mapping relationship between each cluster and each industry; Wherein, determining an industrial cluster associated with the work location information based on the work location information includes: Determine the industrial belt where the workplace is located based on the workplace information; Determine the influence range of the industrial belt with the center of the industrial belt as the center and the preset coverage radius as the radius; When the work location information intersects with the influence range of the industrial belt, the industrial cluster is determined.

7. The device according to claim 6, wherein The behavior information includes at least one of website browsing record information and terminal APP loading information; The target industry determination module includes: The second target industry determination submodule is used to determine the second target industry based on the website browsing record information and / or terminal APP loading information using a pre-configured second industry relationship map; the second industry relationship map is used to characterize the mapping relationship between the website and / or APP and each industry.

8. The device according to claim 6, wherein The acquisition module is further configured to: In response to an operation instruction of the terminal, at least one of the terminal's location information, browser browsing history information, and loaded APP information is obtained.

9. The device according to claim 6, wherein The recall module includes: The business model information acquisition submodule is used to obtain business model information; The recall submodule is used to recall recommended content associated with the target industry based on the business model information.

10. The device according to claim 9, wherein The business model information includes manufacturers and traders, and the recommended content includes upstream product content and co-travel product content; The recall submodule is also used for: In the case where the business model information is the manufacturer, recalling upstream product content associated with the target industry; When the business model information is the trader, the same-travel merchandise content associated with the target industry is recalled.

11. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

12. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 5.

13. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 5.

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