WO2024066253A1 - Procédé de recommandation de produit basée sur une fiction interactive et appareil associé - Google Patents

Procédé de recommandation de produit basée sur une fiction interactive et appareil associé Download PDF

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Publication number
WO2024066253A1
WO2024066253A1 PCT/CN2023/084205 CN2023084205W WO2024066253A1 WO 2024066253 A1 WO2024066253 A1 WO 2024066253A1 CN 2023084205 W CN2023084205 W CN 2023084205W WO 2024066253 A1 WO2024066253 A1 WO 2024066253A1
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target
product
product type
type
recommendation
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PCT/CN2023/084205
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English (en)
Chinese (zh)
Inventor
王一
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深圳市人马互动科技有限公司
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Publication of WO2024066253A1 publication Critical patent/WO2024066253A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G06F16/3329—Natural language query formulation or dialogue systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/334—Query execution
    • G06F16/3343—Query execution using phonetics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/253—Grammatical analysis; Style critique
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis

Definitions

  • the present application belongs to the general data processing technology field of the Internet industry, and specifically relates to a product recommendation method and related devices based on interactive novels.
  • the embodiment of the present application provides a product recommendation method and related devices based on interactive novels, so as to recommend products to users in a targeted manner during human-computer dialogue, thereby improving the user's adoption rate of the recommended products and the user's usage experience.
  • an embodiment of the present application provides a product recommendation method based on interactive novels, which is applied to an electronic device in an integrated service system, wherein the integrated service system includes the electronic device and a server, the electronic device is communicatively connected with the server, and the electronic device is provided with a voice interactive service engine; the method includes:
  • the target service type being a service type of at least one of the following service types supported by the server of the integrated service system: film and television recommendation service, food recommendation service, and scenic spot recommendation service;
  • an interaction record of the user in the interactive novel wherein the interaction record includes interactive voice information input by the user and plot content of the interactive novel corresponding to the interactive voice information;
  • the target product type being at least one product type among a plurality of product types corresponding to the target service type, the product type being used to indicate a classification of products included in the service corresponding to the target service type;
  • an embodiment of the present application provides a product recommendation device based on interactive novels, which is applied to an electronic device in an integrated service system, wherein the integrated service system includes the electronic device and a server, the electronic device is communicatively connected with the server, and the electronic device is provided with a voice interactive service engine; the device includes:
  • a first acquisition unit is used to call the voice interaction service engine to acquire the user's target voice information, perform intent recognition on the target voice information, and obtain the user's intent recognition result, wherein the intent recognition result is used to characterize the user's demand intention;
  • a first determining unit is used to determine a target service type adapted to the user according to the intention recognition result, wherein the target service type is a service type of at least one of the following service types supported by the server of the integrated service system: a film and television recommendation service, a food recommendation service, and a scenic spot recommendation service;
  • a second acquisition unit is used to acquire the interaction record of the user in the interactive novel, wherein the interaction record includes the interactive voice information input by the user and the plot content of the interactive novel corresponding to the interactive voice information;
  • a second determining unit configured to determine a target interaction record associated with the target service type from the interaction record according to the plot content
  • a third determining unit is used to determine a target product type according to the target interaction record, wherein the target product type is at least one product type among a plurality of product types corresponding to the target service type, and the product type is used to indicate a classification of products included in the service corresponding to the target service type;
  • a fourth determining unit configured to determine a target product according to the target product type, wherein the target product is a product belonging to the target product type;
  • a third acquiring unit configured to acquire product information about the target product from the server
  • An output unit is used to generate recommendation information based on the product information and call the voice interaction service engine to output the recommendation information.
  • an embodiment of the present application provides an electronic device, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.
  • an embodiment of the present application provides a computer storage medium, characterized in that it stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute some or all of the steps described in the first aspect of this embodiment.
  • an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application.
  • the computer program product may be a software installation package.
  • the electronic device first calls the voice interaction service engine to obtain the user's target voice information, performs intent recognition on the target voice information, and obtains the user's intent recognition result, the intent recognition result is used to characterize the user's demand intention, and then determines the target service type adapted to the user according to the intent recognition result, the target service type is a service type of at least one of the following service types supported by the server of the comprehensive service system: film and television recommendation service, food recommendation service, scenic spot recommendation service, and then obtains the user's interactive novel in the interactive novel.
  • the target service type is determined from the target interaction record, wherein the interaction record includes the interactive voice information input by the user and the plot content of the interactive novel corresponding to the interactive voice information.
  • the target interaction record associated with the target service type is determined from the interaction record.
  • the target product type is determined.
  • the target product type is at least one product type among the multiple product types corresponding to the target service type.
  • the product type is used to indicate the classification of the products included in the service corresponding to the target service type.
  • the target product is determined.
  • the target product is a product belonging to the target product type.
  • product information about the target product is obtained from the server.
  • recommendation information is generated according to the product information, and the voice interaction service engine is called to output the recommendation information.
  • the user's preference for the product type included in the service type can be obtained, and then the user can be targeted to recommend products based on this, which can improve the user's satisfaction with the product recommendation function and improve the user's adoption rate of the recommended products.
  • this solution is only based on the user's interaction records in the interactive novel, without obtaining a large amount of user privacy data in daily life. Therefore, the user will not feel that they are being monitored by electronic devices, thereby improving the user's usage experience and peace of mind.
  • FIG1 is a schematic diagram of the composition of an integrated service system provided in an embodiment of the present application.
  • FIG2 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.
  • FIG3 is a flow chart of a product recommendation method based on interactive novels provided in an embodiment of the present application.
  • FIG4 is a schematic diagram of a product recommendation interface provided in an embodiment of the present application.
  • FIG5 is a block diagram of functional units of a product recommendation device based on interactive novels provided in an embodiment of the present application
  • FIG6 is a block diagram of the functional units of another product recommendation device based on interactive novels provided in an embodiment of the present application.
  • an embodiment of the present application provides a product recommendation method and related devices based on interactive novels.
  • the embodiment of the present application is described in detail below in conjunction with the accompanying drawings.
  • the comprehensive service system 10 includes an electronic device 101 and a server 102, the electronic device 101 is communicatively connected to the server 102, and the electronic device 101 is provided with a voice interaction service engine 111.
  • the voice interaction service engine 111 can perform intent recognition on the voice information input by the user, so that the electronic device 101 can determine the product that meets the user's needs.
  • the server 102 can support multiple services, so that when the service required by the user is a service type supported by the server 102, the electronic device 101 can recommend products to the user in combination with the product information in the server 102.
  • the electronic device 101 includes a processor 120, a memory 130, a communication interface 140, and one or more programs 131, wherein the one or more programs 131 are stored in the above-mentioned memory 130 and are configured to be executed by the above-mentioned processor 120, and the one or more programs 131 include instructions for executing any step in the following method embodiment.
  • the processor 120 is used to execute any step performed by the electronic device in the following method embodiment, and when performing data transmission such as sending, the communication interface 140 can be selectively called to complete the corresponding operation.
  • the electronic device involved in the embodiments of the present application may be an electronic device with communication capabilities, which may include various handheld devices with wireless communication functions, vehicle-mounted devices, wearable devices, computing devices or other processing devices connected to a wireless modem, as well as various forms of user equipment (UE), mobile stations (MS), terminal devices, etc.
  • UE user equipment
  • MS mobile stations
  • Figure 3 is a flow chart of a product recommendation method based on interactive novels provided in an embodiment of the present application.
  • the product recommendation method based on interactive novels is applied to an electronic device in an integrated service system, the integrated service system includes the electronic device and a server, the electronic device is connected to the server in communication, and the electronic device is provided with a voice interactive service engine.
  • the method includes the following steps:
  • the intention recognition result is used to characterize the user's demand intention.
  • the intention recognition of the target voice information includes: converting the target voice information into target text information, generating a parsing graph according to the target text information, the parsing graph includes multiple parsing nodes and parsing node relationship lines, the parsing node relationship line is used to indicate the grammatical or semantic relationship between two parsing nodes, the two parsing nodes can be two adjacent parsing nodes, or two non-adjacent parsing nodes, the same parsing node can correspond to multiple parsing node relationship lines, and the parsing node is a word in the target file information.
  • a matching knowledge subgraph is provided, wherein a plurality of knowledge subgraphs constitute the knowledge graph, each knowledge subgraph includes a plurality of knowledge nodes and a relationship line between each knowledge node, a knowledge node is a word set, and a knowledge node relationship line is used to indicate a grammatical or semantic relationship between two knowledge nodes, wherein the two knowledge nodes may be two adjacent knowledge nodes or two non-adjacent knowledge nodes, and the same knowledge node may correspond to multiple knowledge node relationship lines.
  • the user intent is determined based on the triples included in the knowledge subgraph, and the included triples may be used to characterize the intent recognition result, and each triple includes two entities and an association relationship between the two entities, and the two entities may correspond to a word respectively, and the association relationship may be a semantic relationship and/or a grammatical relationship.
  • the target service type is at least one of the following service types supported by the server of the integrated service system: film and television recommendation service, food recommendation service, and scenic spot recommendation service.
  • the film and television recommendation service can be used to recommend TV series, movies, animations and other film and television content to users
  • the food recommendation service can recommend restaurants, takeaway shops, etc. to users
  • the scenic spot recommendation service can be used to recommend scenic spots, routes, travel methods, etc. to users
  • the travel method recommendation can also include ticket booking services.
  • the interactive record includes the interactive voice information input by the user and the plot content of the interactive novel corresponding to the interactive voice information.
  • the plot content can be used to indicate the plot of the novel in which the interactive record is currently located, for example, the current interactive record corresponds to the plot of the protagonist having a dinner party, or the plot of the male and female protagonists watching a movie, etc.
  • the interactive voice information is the voice information about the plot content input by the user according to the interactive plot when using the interactive novel.
  • S204 Determine a target interaction record associated with the target service type from the interaction records according to the plot content.
  • the determining of the interaction record associated with the target service type includes: performing text analysis on the interactive voice information input by the user included in the acquired interactive record to obtain interactive text information; determining the keyword corresponding to the target service type, and determining the interactive text information including the keyword from the acquired interactive text information as the target interactive text information; and determining the interactive record corresponding to the interactive voice information corresponding to the target interactive text information as the target interactive record.
  • the machine output sentence in the plot content included in the interactive record can also be obtained for text analysis to determine whether the machine output sentence includes the keyword; if it does, then determine the user input voice information corresponding to the machine output sentence; perform intent recognition on the user input voice information corresponding to the machine output sentence to obtain a recognition result, and determine whether the interactive record corresponding to the machine output sentence is the target interactive record according to the recognition result.
  • the recognition result can indicate whether the user has a positive intention or a negative intention. If the user has a positive intention, then it is the target interactive record. For example, the machine output sentence is "Shall we go eat barbecue?" It can be seen that the machine output sentence includes the word "barbecue" as the keyword corresponding to the food recommendation service. Therefore, if the user has a positive intention, that is, the voice information input by the user in the interactive novel is "OK", then this interaction record can be determined as the target interaction record.
  • the target product type is at least one of the multiple product types corresponding to the target service type, and the product type is used to indicate the classification of products included in the service corresponding to the target service type.
  • the product types corresponding to the film and television recommendation service include: Xianxia, suspense, campus, etc.
  • the food recommendation service corresponds to barbecue, hot pot, Japanese food, etc.
  • the scenic spot recommendation service corresponds to travel, humanities, and scenery, etc.
  • the target product is a product belonging to the target product type.
  • the target product may include multiple products.
  • the target product may be multiple hot pot restaurants such as hot pot restaurant 1 and hot pot restaurant 2.
  • S207 Acquire product information about the target product from the server.
  • the product information includes link information, and the user can jump to the corresponding product interface according to the link information to obtain more complete product information.
  • the electronic device determines the target product, it can send request information for the product information corresponding to the target product to the server, and the server sends response information to the electronic device according to the request information, and the response information includes product information about the target product.
  • the server first sends the product information of each product to the electronic device, and the electronic device stores the product information of each product in a hierarchical manner according to the product type and service type.
  • the electronic device Before the electronic device determines the target product, it can synchronously determine the storage path step by step according to the determined service type and product type, and then after the electronic device determines the target product, it can directly find the product information related to the target product through the storage path corresponding to the target product.
  • the recommendation information includes a machine sentence and the corresponding product information of the target product.
  • the recommendation sentence recommends the target product to the user, and the product information is used for the user to understand the specific content of the target product.
  • the electronic device first calls the voice interaction service engine to obtain the user's target voice information, performs intent recognition on the target voice information, and obtains the user's intention recognition result, which is used to characterize the user's demand intention, and then determines the target service type adapted to the user based on the intention recognition result, and the target service type is a service type of at least one of the following service types supported by the server of the comprehensive service system: film and television recommendation service, food recommendation service, and scenic spot recommendation service, and then obtains the user's interaction record in the interactive novel, and the interaction record includes the interactive voice information input by the user and the plot content of the interactive novel corresponding to the interactive voice information.
  • the target interaction record associated with the target service type is determined from the interaction record, and then the target product type is determined according to the target interaction record, the target product type is at least one product type among the multiple product types corresponding to the target service type, and the product type is used to indicate the classification of the products included in the service corresponding to the target service type, and then the target product is determined according to the target product type, and the target product is a product belonging to the target product type, and then the product information about the target product from the server is obtained, and finally the recommendation information is generated according to the product information, and the voice interaction service engine is called to output the recommendation information.
  • the user's preference for the product type included in the service type can be obtained, and then the product recommendation can be made to the user in a targeted manner, which can improve the user's satisfaction with the product recommendation function and improve the user's adoption rate of the recommended product.
  • this solution is only determined based on the user's interaction record in the interactive novel, and does not need to obtain a large amount of privacy data of the user in daily life, so it will not make the user feel monitored by the electronic device, and improve the user's use experience and peace of mind.
  • the target interaction record associated with a type includes: determining the target plot corresponding to the target service type; determining the interaction record whose plot content in the interaction record conforms to the target plot as the target interaction record; in the case where the target interaction record includes multiple, determining the target product type based on the target interaction record, including: determining the product type included in each target interaction record based on the interactive voice information included in the target interaction record; determining the product type corresponding to the majority of all product types included in multiple target interaction records as the target product type.
  • the product type corresponding to the target service type in each target interaction record can be determined according to the target interaction record.
  • the determination of the product type can be determined according to the voice information input by the user in the target interaction record, or it can be determined by first determining the candidate product type according to the machine output sentence corresponding to the plot content included in the target interaction record, and then determining the final product type from the candidate product type according to the user's reply voice information to the machine output sentence.
  • the machine output sentence includes "Which do you prefer, hot pot or barbecue?", so the obtained candidate product types include two product types, "hot pot” and "barbecue". If the user answers "the second one", it can be known that the product type included in the target interaction record is "barbecue".
  • the corresponding target interaction record can be deleted.
  • the target interaction records are first screened out from the acquired interaction records according to the target plot corresponding to the target service type, and then the product type with the largest number of occurrences among the product types corresponding to the target interaction records is determined as the target product type. Since the product types included in the target interaction records are all product types actively selected by users in the interactive novel, the user's preference for each product type in the target service type can be determined based on the interaction record, and the product type corresponding to the majority is determined as the target product type, which can best fit the user's preferences and ensure that the final recommended product is adopted by the user.
  • determining that the product type corresponding to the majority of all product types included in a plurality of target interaction records is the target product type includes: determining the interaction type corresponding to each target interaction record, the interaction type including selection-type interaction and question-and-answer-type interaction; determining that the product type included in the target interaction record corresponding to the selection-type interaction is the first product type, and the product type included in the target interaction record corresponding to the question-and-answer-type interaction is the second product type, to obtain a first product type set and a second product type set; determining the total number of the first product type included in the first product type set, and the total number of the second product type included in the second product type set; determining the number of the first reference product type in the first product type set, and the number of the second reference product type in the second product type set the number of second reference product types in the set, the first reference product type is the product type corresponding to the majority in the set of the first product types, and the second reference product type is the product type corresponding to
  • the voice information input by the user included in the selection type interaction record is the selection of the product type included in the machine output sentence
  • the voice information input by the user included in the question-and-answer type interaction record is the answer to the machine output sentence
  • the machine output sentence does not include the product type.
  • the machine output sentence included in the interaction record is "Do you like hot pot or barbecue?"
  • the voice information input by the user is "barbecue”
  • the interaction record is a selection type interaction record. If the machine output sentence included in the interaction record is "What shall we eat for dinner?", and the voice information input by the user is "barbecue", then the interaction record is a question-and-answer type interaction record.
  • the corresponding weight of the question-and-answer type interaction record is higher than that of the selection type interaction record.
  • different product types can be classified according to the interaction type to obtain two product type sets.
  • the first product type set and the second product type set can include the same product types. Then determine the most product types included in each product type in the two product type sets, as well as the number of the most product types.
  • the product types included in the first product type set are "hot pot", “barbecue” and “Japanese food”, among which "hot pot” appears 3 times, “barbecue” appears 5 times, and "Japanese food” appears 2 times. Therefore, the first reference product type corresponding to the majority in the first product type set is "barbecue", with a quantity of 5.
  • the parameters in the second product type set are determined in the same way. If the second reference type in the determined second product type set is "grilled fish" and the second quantity accounts for 0.4, then since the weight of the selection interaction is greater than the weight of the question-and-answer interaction, if the weight ratio of the selection interaction to the question-and-answer interaction is 2:1, then it can be seen that the score of the first reference product type is 0.5 and the score of the second reference product type is 0.8. Therefore, it can be determined that the target product type is the second reference product type, i.e., "grilled fish".
  • the user's preferences are comprehensively determined based on the interaction type corresponding to the target interaction record to obtain the target product type. This can be closer to the user's actual product type preferences, reduce the restrictions of the interactive novel framework on the user's product type preferences, and increase the adoption rate of the products ultimately recommended to the user.
  • determining the product type with a higher score between the first reference product type and the second reference product type as the target product type includes: determining the product type with a higher score as the alternative product type; obtaining historical recommendation records, the historical recommendation records being recommendation records for products corresponding to the target plot; obtaining the recommendation adoption status corresponding to each historical recommendation record; determining whether the alternative product type is included in the historical recommendation records; if so, determining the recommendation adoption rate of the alternative product type based on the recommendation adoption status; and determining the alternative product type as the target product type when the recommendation adoption rate is higher than a preset adoption rate.
  • Determining whether the user adopts the recommended product type may include: after outputting the product corresponding to the alternative product type, determining whether the user performs a confirmation operation on the product within a preset time period.
  • the confirmation operation includes the user replying to a voice message with confirmation intention.
  • the adoption rate is the ratio of the number of times an alternative product type is adopted to the number of times the alternative product type is recommended.
  • the method further includes: when the recommendation adoption rate is not higher than the preset adoption rate, determining whether the product type included in the most recent target interaction record is the same as the alternative product type; if they are the same, determining the product type with the highest recommendation adoption rate based on the recommendation adoption situation, and determining the alternative product type and the product type with the highest recommendation adoption rate as the target product type; if they are not the same, determining the alternative product type and the product type included in the most recent target interaction record as the target product type.
  • the most recent target interaction record is the interaction record in the target interaction record whose interaction time is closest to the current time. If the product type included in the most recent interaction record is the candidate product type, it means that the user's current preference for the candidate product type is still high, but because the user's adoption rate for the candidate product type is low, the product type with a higher user adoption rate can also be recommended to the user. If the product type included in the most recent interaction record is not the candidate product type, when making product recommendations, products in the product type included in the most recent interaction record can also be recommended at the same time.
  • generating recommendation information based on the product information includes: determining, from the target interaction record, that the interaction record corresponding to the target product type is the final interaction record; obtaining final plot information included in the final interaction record, wherein the final plot information includes character information and time information; generating a first recommendation statement based on the character information, the time information and the target product type; generating a second recommendation statement and recommended content based on the target product; generating a final recommendation statement based on the first recommendation statement and the second recommendation statement; and generating the recommendation information based on the final recommendation statement and the product information.
  • the target interaction record generated last is determined as the final interaction record.
  • the character information is the character information associated with the target service type that appears in the final plot information.
  • the character information includes multiple characters, the character that is most closely related to the role played by the user in the interactive novel among the multiple characters is determined as the character information included in the final plot information.
  • the method for determining the time information may include determining the time when the target interaction record is generated. If the time interval between the time and the current time is less than a preset interval, the time information is determined to be the time when the target interaction record is generated.
  • Figure 4 is a schematic diagram of a product recommendation interface provided by an embodiment of the present application.
  • user A sends a target voice message to the voice interaction server engine (named "Xiaowu” in the figure), and the target voice message is "What should I eat for dinner today?" Then the voice interaction service engine can perform intent recognition on the target voice message to determine that the target service type is a food recommendation service.
  • the recommendation sentence can be generated based on the character information and time information in the final interaction record. As shown in Figure 4, since the time interval is only one day, which is less than the preset The time information at this time is represented by the generation time of the final interaction record.
  • the final recommendation sentence recommends products to users and evokes users' memories of interactive novels. The product information is used for users to gain a deeper understanding of the recommended products.
  • the recommendation sentences are generated by combining the character information related to the novel content in the interaction record, which can not only evoke users' memories of interactive novels and increase the adoption rate of recommended products, but also enable users to use interactive novels again, improve the reading rate of interactive novels, and make users feel more immersed when reading interactive novels again.
  • determining the target product according to the target product type includes: determining user information according to the target service type; determining alternative products according to the target product type and the user information; determining whether the target service type is suitable for repeated recommendations for the same product; if so, determining the target product from the alternative products; if not, deleting the recommended products from the alternative products, and determining the target product from the remaining alternative products.
  • the user information corresponding to different service types may not be exactly the same.
  • the user information corresponding to the film and television recommendation service may include the user's age information. Since some film and television works have viewing age restrictions, such products that are not suitable for users to watch need to be screened out when determining the alternative products.
  • the user information corresponding to the food recommendation service may include the user's current location, so when determining the alternative products, restaurants that are greater than the preset distance from the user's current location can be screened out.
  • the user information corresponding to the scenic spot recommendation service may be the user's disposable time and current location, and the play time of the corresponding scenic spot in the alternative product needs to be within the disposable time.
  • target services are not suitable for repeated recommendations for the same product, while others are suitable, for example, for the film and television recommendation service, it is not suitable to repeatedly recommend the same film and television work. If a film and television work has been recommended, it needs to be deleted from the alternative products. For the food recommendation service, multiple recommendations can be made for the same restaurant, and there is no need to delete the recommended product from the alternative products.
  • the target product is comprehensively determined based on the user information and whether the target service type is suitable for repeated recommendation, so that the recommended target product meets the user's needs and at the same time improves the user's satisfaction and usage experience.
  • Figure 5 is a functional unit composition block diagram of a product recommendation device based on interactive novels provided in an embodiment of the present application, which is applied to an electronic device in an integrated service system, wherein the integrated service system includes the electronic device and a server, the electronic device is communicatively connected to the server, and the electronic device is provided with a voice interaction service engine;
  • the product recommendation device 40 based on interactive novels includes: a first acquisition unit 401, which is used to call the voice interaction service engine to obtain the user's target voice information, perform intent recognition on the target voice information, and obtain the user's intent recognition result, wherein the intent recognition result is used to characterize the user's demand intention; a first determination unit 402, which is used to determine a target service type adapted to the user according to the intent recognition result, wherein the target service type is a service type of at least one of the following service types supported by the server of the integrated service system: film and television recommendation service , food recommendation service, scenic spot recommendation service;
  • the third determination unit 405 is specifically used to: determine the target plot corresponding to the target service type; determine the interaction record whose plot content in the interaction record conforms to the target plot as the target interaction record; in the case where the target interaction record includes multiple target interaction records, in determining the target product type according to the target interaction record, the fourth determination unit 406 is specifically used to: determine the product type included in each target interaction record according to the interactive voice information included in the target interaction record; determine the product type corresponding to the majority of all product types included in multiple target interaction records as the target product type.
  • the fourth determination unit 406 is specifically used to: determine the interaction type corresponding to each target interaction record, the interaction type including selection-type interaction and question-and-answer-type interaction; determine that the product type included in the target interaction record corresponding to the selection-type interaction is the first product type, and the product type included in the target interaction record corresponding to the question-and-answer-type interaction is the second product type, to obtain a first product type set and a second product type set; determine the total number of the first product types included in the first product type set, and the total number of the second product types included in the second product type set; determine the number of the first reference product type in the first product type set, and the number of the second reference product type in the second product type set.
  • the number of second reference product types the first reference product type being the product type corresponding to the majority in the first product type set, and the second reference product type being the product type corresponding to the majority in the second product type set; determining a first quantity ratio according to the total number of the first product type and the number of the first reference product type; determining a second quantity ratio according to the total number of the second product type and the number of the second reference product type; determining a weight ratio corresponding to the selection interaction and the question-and-answer interaction, the weight of the question-and-answer interaction being greater than that of the selection interaction; calculating scores of the first reference product type and the second reference product type according to the first quantity ratio, the second quantity ratio and the weight ratio; determining the product type with a higher score between the first reference product type and the second reference product type as the target product type.
  • the fourth determination unit 406 is specifically used to: determine that the product type with a high score is the alternative product type; obtain historical recommendation records, which are recommendation records for products corresponding to the target plot; obtain the recommendation adoption status corresponding to each historical recommendation record; determine whether the historical recommendation records include the alternative product type; if so, determine the recommendation adoption rate of the alternative product type based on the recommendation adoption status; and when the recommendation adoption rate is higher than a preset adoption rate, determine that the alternative product type is the target product type.
  • the fourth determination unit 406 is specifically used to: when the recommendation adoption rate is not higher than the preset adoption rate, determine whether the product type included in the most recent target interaction record is the same as the alternative product type; if they are the same, determine the product type with the highest recommendation adoption rate based on the recommendation adoption situation, and determine the alternative product type and the product type with the highest recommendation adoption rate as the target product type; if they are not the same, determine the alternative product type and the product type included in the most recent target interaction record as the target product type.
  • the output unit 408 Specifically used for: determining from the target interaction record that the interaction record corresponding to the target product type is the final interaction record; obtaining the final plot information included in the final interaction record, the final plot information including character information and time information; generating a first recommendation statement based on the character information, the time information and the target product type; generating a second recommendation statement and recommended content based on the target product; generating a final recommendation statement based on the first recommendation statement and the second recommendation statement; generating the recommendation information based on the final recommendation statement and the product information.
  • the output unit 408 is specifically used to: determine user information according to the target service type; determine alternative products according to the target product type and the user information; determine whether the target service type is suitable for repeated recommendations for the same product; if so, determine the target product from the alternative products; if not, delete the recommended products from the alternative products, and determine the target product from the remaining alternative products.
  • the product recommendation device 500 based on interactive novels includes: a processing module 512 and a communication module 511.
  • the processing module 512 is used to control and manage the actions of the product recommendation device based on patch advertisements, for example, to execute the steps of the first acquisition unit 401, the first determination unit 402, the second acquisition unit 403, the second determination unit 404, the third determination unit 405, the fourth determination unit 406, the third acquisition unit 407 and the output unit 408, and/or to perform other processes of the technology described herein.
  • the communication module 511 is used for interaction between the product recommendation device based on interactive novels and other devices.
  • the product recommendation device based on interactive novels may also include a storage module 513, which is used to store program code and data of the product recommendation device based on interactive novels.
  • the processing module 412 can be a processor or a controller, for example, it can be a central processing unit (CPU), a general processor, a digital signal processor (DSP), an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It can implement or execute various exemplary logic boxes, modules and circuits described in conjunction with the disclosure of this application.
  • the processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of DSP and microprocessor, etc.
  • the communication module 411 can be a transceiver, an RF circuit or a communication interface, etc.
  • the storage module 413 can be a memory.
  • the above interactive novel-based product recommendation device 500 can execute the interactive novel-based product recommendation method shown in FIG. 3 .
  • the electronic device includes a hardware structure and a software module corresponding to the execution of each function.
  • the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.
  • the embodiment of the present application can divide the functional units of the electronic device according to the above method example.
  • each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.
  • An embodiment of the present application also provides a chip, wherein the chip includes a processor for calling and running a computer program from a memory, so that a device equipped with the chip executes some or all of the steps described for the electronic device in the above method embodiment.
  • An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method recorded in the above method embodiments, and the above computer includes an electronic device.
  • the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment.
  • the computer program product may be a software installation package, and the computer includes an electronic device.
  • the disclosed devices can be implemented in other ways.
  • the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.
  • Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.
  • the units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
  • the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory.
  • the technical solution of the present application is essentially or part of the contribution to the prior art or all or part of the technical solution can be embodied in the form of a software product.
  • the computer software product is stored in a memory, including several instructions for enabling a computer device (which may be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the above methods in various embodiments of the present application.
  • the aforementioned memory includes: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

Abstract

La présente demande concerne un procédé de recommandation de produit basée sur une fiction interactive et un appareil associé, qui sont appliqués à un dispositif électronique dans un système de service complet. Le dispositif électronique est pourvu d'un moteur de service d'interaction vocale; et le procédé consiste à : appeler le moteur de service d'interaction vocale pour obtenir des informations vocales cibles d'un utilisateur, et effectuer une reconnaissance d'intention sur les informations vocales cibles pour obtenir un résultat de reconnaissance d'intention de l'utilisateur; en fonction du résultat de reconnaissance d'intention, déterminer un type de service cible adapté à l'utilisateur; obtenir des enregistrements d'interaction de l'utilisateur dans une fiction interactive; selon un contenu d'intrigue, déterminer, à partir des enregistrements d'interaction, un enregistrement d'interaction cible associé au type de service cible; déterminer un type de produit cible selon l'enregistrement d'interaction cible; déterminer un produit cible selon le type de produit cible; obtenir des informations de produit concernant le produit cible à partir d'un serveur; et générer des informations de recommandation selon les informations de produit et appeler le moteur de service d'interaction vocale pour délivrer les informations de recommandation. De cette manière, des produits peuvent être recommandés à des utilisateurs d'une manière ciblée, et les taux d'adoption, par les utilisateurs, des produits recommandés sont améliorés.
PCT/CN2023/084205 2022-09-29 2023-03-27 Procédé de recommandation de produit basée sur une fiction interactive et appareil associé WO2024066253A1 (fr)

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Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115271891B (zh) * 2022-09-29 2022-12-30 深圳市人马互动科技有限公司 基于互动小说的产品推荐方法及相关装置
CN115599900B (zh) * 2022-12-12 2023-03-21 深圳市人马互动科技有限公司 具有歧义的用户输入的分发处理方法及相关装置
CN115689810B (zh) * 2023-01-04 2023-04-04 深圳市人马互动科技有限公司 基于人机对话的数据处理方法及相关装置
CN116089728B (zh) * 2023-03-23 2023-06-20 深圳市人马互动科技有限公司 针对儿童的语音互动小说的生成方法及相关装置

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109522488A (zh) * 2018-12-27 2019-03-26 上海连尚网络科技有限公司 一种信息推荐方法及设备
US10380208B1 (en) * 2015-12-28 2019-08-13 Amazon Technologies, Inc. Methods and systems for providing context-based recommendations
CN110956511A (zh) * 2019-12-30 2020-04-03 深圳前海微众银行股份有限公司 商品推荐方法、装置、设备与计算机可读存储介质
CN110990625A (zh) * 2019-11-27 2020-04-10 南京创维信息技术研究院有限公司 影视推荐方法及装置
CN115101047A (zh) * 2022-08-24 2022-09-23 深圳市人马互动科技有限公司 语音交互方法、装置、系统、交互设备和存储介质
CN115271891A (zh) * 2022-09-29 2022-11-01 深圳市人马互动科技有限公司 基于互动小说的产品推荐方法及相关装置

Family Cites Families (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110459214B (zh) * 2014-01-20 2022-05-13 华为技术有限公司 语音交互方法及装置
CN104463640B (zh) * 2014-12-26 2018-07-06 小米科技有限责任公司 智能硬件商品的推荐显示方法及装置
CN106874308B (zh) * 2015-12-14 2021-03-26 北京搜狗科技发展有限公司 一种推荐方法和装置、一种用于推荐的装置
CN106250553A (zh) * 2016-08-15 2016-12-21 珠海市魅族科技有限公司 一种服务推荐方法及终端
CN110708607B (zh) * 2016-12-22 2022-03-01 广州方硅信息技术有限公司 直播互动方法、装置、电子设备以及存储介质
CN107146610B (zh) * 2017-04-10 2021-06-15 易视星空科技无锡有限公司 一种用户意图的确定方法及装置
CN109391834B (zh) * 2017-08-03 2021-08-31 阿里巴巴集团控股有限公司 一种播放处理方法、装置、设备和存储介质
CN108174247A (zh) * 2017-12-27 2018-06-15 优酷网络技术（北京）有限公司 视频互动方法及装置
CN110149558B (zh) * 2018-08-02 2023-03-28 腾讯科技（深圳）有限公司 一种基于内容识别的视频播放实时推荐方法及系统
CN110889006A (zh) * 2018-09-07 2020-03-17 阿里巴巴集团控股有限公司 推荐方法及装置
CN109600633B (zh) * 2018-12-17 2021-08-03 象山兑鹏电子科技有限公司 商品信息的网络推广互动系统及方法
CN111026969B (zh) * 2019-12-18 2023-10-03 腾讯科技（深圳）有限公司 一种内容推荐方法、装置及存储介质和服务器
CN113297399A (zh) * 2020-03-27 2021-08-24 阿里巴巴集团控股有限公司 个性化推荐方法、个性化推荐装置及电子设备
CN114783421A (zh) * 2022-03-22 2022-07-22 中国平安人寿保险股份有限公司 智能推荐方法和装置、设备、介质

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10380208B1 (en) * 2015-12-28 2019-08-13 Amazon Technologies, Inc. Methods and systems for providing context-based recommendations
CN109522488A (zh) * 2018-12-27 2019-03-26 上海连尚网络科技有限公司 一种信息推荐方法及设备
CN110990625A (zh) * 2019-11-27 2020-04-10 南京创维信息技术研究院有限公司 影视推荐方法及装置
CN110956511A (zh) * 2019-12-30 2020-04-03 深圳前海微众银行股份有限公司 商品推荐方法、装置、设备与计算机可读存储介质
CN115101047A (zh) * 2022-08-24 2022-09-23 深圳市人马互动科技有限公司 语音交互方法、装置、系统、交互设备和存储介质
CN115271891A (zh) * 2022-09-29 2022-11-01 深圳市人马互动科技有限公司 基于互动小说的产品推荐方法及相关装置

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