Product recommendation method and device based on video bullet screen

Abstract

The invention provides a product recommendation method and device based on a video bullet screen, relates to the technical field of artificial intelligence, and can be applied to the technical field of finance or other technical fields. The product recommendation method based on the video bullet screen comprises the following steps: acquiring video title label data, popularity bullet screen label data, customer bullet screen label data and comment operation label data; determining video tag data according to a tag coefficient, the video title tag data, the popularity bullet screen tag data, the customer bullet screen tag data and the comment operation tag data; and sorting the video tag data, and pushing a product corresponding to the video tag data according to a sorting result. The method can improve the overall operation benefit of the client.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a product recommendation method and device based on video barrage. Background Art

[0002] The bank has launched various mobile clients to serve farmers, which include two core functions: one is a video section that provides farmers with agricultural training, and the other is a matching section that matches farmers' planting and crop sales. However, the above two functions are relatively independent, that is, the video section can only provide video playback, and the matching section can only match production and sales. The two have not been organically integrated, resulting in a disconnect between the overall functions of the client. Each function remains at the public welfare service level, and a systematic process service cannot be formed, which cannot effectively create revenue for the bank. This further leads to a lack of source power for the creation of authors in the video section and the release of information in the matching section, and the client develops slowly. Summary of the invention

[0003] The main purpose of the embodiments of the present invention is to provide a product recommendation method and device based on video barrage to improve the overall operating efficiency of the client.

[0004] In order to achieve the above object, an embodiment of the present invention provides a product recommendation method based on video bullet screen, comprising:

[0005] Obtain video title tag data, popular barrage tag data, customer barrage tag data, and comment operation tag data;

[0006] Determine the video tag data according to the tag coefficient, the video title tag data, the hot barrage tag data, the customer barrage tag data and the comment operation tag data;

[0007] The video tag data is sorted, and products corresponding to the video tag data are pushed according to the sorting result.

[0008] In one embodiment, determining the video tag data according to the tag coefficient, the video title tag data, the hot barrage tag data, the customer barrage tag data, and the comment operation tag data includes:

[0009] Determine the label data of each label according to the label coefficient, the video title label data, the hot barrage label data, the customer barrage label data and the comment operation label data;

[0010] The video tag data is determined according to the tag data of each tag.

[0011] In one embodiment, determining the video tag data according to the tag data of each tag includes:

[0012] Determine the maximum value of the label data;

[0013] The video tag data is determined according to a maximum value of the tag data and tag data of each tag.

[0014] In one embodiment, it also includes:

[0015] The label coefficient is updated according to the transaction completion data.

[0016] In one embodiment, updating the label coefficient according to the transaction data includes:

[0017] Update the video tag data according to the transaction completion data;

[0018] The label coefficient is updated according to the updated video label data.

[0019] In one embodiment, updating the label coefficient according to the video label data includes:

[0020] Update the tag data of each tag according to the video tag data;

[0021] The label coefficients are updated according to the updated label data of each label.

[0022] In one embodiment, pushing the product corresponding to the video tag data according to the sorting result includes:

[0023] Obtaining a label corresponding to the video label data according to the sorting result;

[0024] Push the product corresponding to the tag.

[0025] The embodiment of the present invention further provides a product recommendation device based on video bullet screen, comprising:

[0026] An acquisition module is used to acquire video title tag data, hot barrage tag data, customer barrage tag data, and comment operation tag data;

[0027] A video tag data module, used to determine the video tag data according to the tag coefficient, the video title tag data, the hot barrage tag data, the customer barrage tag data and the comment operation tag data;

[0028] The sorting module is used to sort the video tag data and push the product corresponding to the video tag data according to the sorting result.

[0029] In one embodiment, the video tag data module includes:

[0030] A label data unit, used to determine the label data of each label according to the label coefficient, the video title label data, the hot barrage label data, the customer barrage label data and the comment operation label data;

[0031] The video tag data unit is used to determine the video tag data according to the tag data of each tag.

[0032] In one embodiment, the video tag data unit includes:

[0033] A maximum value determination subunit, used to determine the maximum value of the label data;

[0034] The video tag data subunit is used to determine the video tag data according to the maximum value of the tag data and the tag data of each tag.

[0035] In one embodiment, it also includes:

[0036] The label coefficient updating module is used to update the label coefficient according to the transaction completion data.

[0037] In one embodiment, the label coefficient updating module includes:

[0038] A video tag data updating unit, configured to update the video tag data according to the transaction completion data;

[0039] The label coefficient updating unit is used to update the label coefficient according to the updated video label data.

[0040] In one embodiment, the label coefficient updating unit includes:

[0041] A label data updating subunit, used for updating label data of each label according to the video label data;

[0042] The label coefficient updating subunit is used to update the label coefficient according to the updated label data of each label.

[0043] In one embodiment, the sorting module includes:

[0044] A label acquisition unit, used to acquire a label corresponding to the video label data according to the sorting result;

[0045] The push unit is used to push the product corresponding to the label.

[0046] An embodiment of the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the product recommendation method based on video barrage are implemented.

[0047] An embodiment of the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the product recommendation method based on video barrage are implemented.

[0048] An embodiment of the present invention also provides a computer program product, including a computer program / instruction, which implements the steps of the product recommendation method based on video barrage when the computer program / instruction is executed by a processor.

[0049] The product recommendation method and device based on video barrage in the embodiment of the present invention determines the video tag data for sorting according to the tag coefficient, video title tag data, hot barrage tag data, customer barrage tag data and comment operation tag data, and pushes the products corresponding to the video tag data according to the sorting results, which can improve the overall operating efficiency of the client. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 is a flow chart of a product recommendation method based on video barrage in an embodiment of the present invention;

[0052] Figure 2 is a flowchart of a product recommendation method based on video barrage in another embodiment of the present invention;

[0053] Figure 3 is a flow chart of S102 in an embodiment of the present invention;

[0054] Figure 4 is a flow chart of S202 in an embodiment of the present invention;

[0055] Figure 5 is a flow chart of S103 in an embodiment of the present invention;

[0056] Figure 6 is a flow chart of updating label coefficients in an embodiment of the present invention;

[0057] Figure 7 is a flow chart of S502 in an embodiment of the present invention;

[0058] Figure 8 is a structural block diagram of a product recommendation device based on video barrage in an embodiment of the present invention;

[0059] Fig. 9A schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, device, apparatus, method or computer program product. Therefore, the present disclosure may be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0062] The acquisition, storage, use, and processing of data in the technical solution of the present invention are in compliance with the relevant provisions of national laws and regulations. The user information in the embodiments of this application is obtained through legal and compliant channels, and the acquisition, storage, use, and processing of user information are authorized and agreed by the customer.

[0063] The purpose of the present invention is to connect the video section with the matching section in the background, and to capture the farmers' interests through the comments, bullet screens, active operations of the customers in the video section, so as to match the customers with suitable production and marketing matching products in the matching section through background calculation, thereby reducing the time for customers to learn and search by themselves and improving the operation efficiency of the client. The present invention is described in detail below with reference to the accompanying drawings.

[0064] Figure 1 It is a flowchart of a product recommendation method based on video barrage in an embodiment of the present invention. Figure 2 FIG. 1 is a flow chart of a product recommendation method based on video bullet screen in another embodiment of the present invention. Figure 1-Figure 2 As shown in the figure, the product recommendation method based on video barrage includes:

[0065] S101: Obtain video title tag data, hot barrage tag data, customer barrage tag data, and comment operation tag data.

[0066] In specific implementation, the video title tag data is obtained in the following way:

[0067] The customer opens the mobile client, enters the relevant section that provides agricultural learning or video viewing, browses the relevant information of each video, and clicks to select the content of interest to watch. After the customer clicks on the video, the system background identifies and obtains the title content of the video, uses the named entity recognition method based on the combination of text classification and part-of-speech tagging, and uses JieBa word segmentation technology to split the title content into several minimum unit phrases, recognizes content such as names of people, organization names, dates, locations, business segment names, etc. from the text, and matches them with the label phrases in the product label library. If the title contains a label phrase in the label library, the phrase is added as the annotation information of the video, and the video title label data is generated and stored in database A.

[0068] Get the hot barrage tag data in the following ways:

[0069] The system plays the video selected by the customer, and automatically obtains the text displayed in the form of bullet screen at the current time point every X seconds (the bank can set parameters according to its own needs). At the same time, it automatically obtains all the comment information under the video, uses the named entity recognition method based on text classification and part-of-speech tagging, and uses JieBa word segmentation technology to split the comment content into several minimum unit phrases, recognizes content such as names, organization names, dates, locations, business segment names, etc. from the text, and matches the minimum unit phrases obtained from the bullet screen content with the label phrases in the product label library. If the comment or bullet screen content contains a label phrase in the label library, the phrase is added as the annotation information of the video, and the N video annotation phrases obtained are pushed to the mobile client on the one hand, and the hot word bullet screen sending option such as "Do you want to send 'Like XXX (hot word)' bullet screen with one click" is automatically generated for the customer at the last X seconds that just ended; when the customer chooses to send, the hot bullet screen label data will be generated and stored in database B.

[0070] Obtain customer bullet comment tag data in the following ways:

[0071] Identify and obtain the barrage content sent by the customer, use a named entity recognition method based on a combination of text classification and part-of-speech tagging, and use JieBa word segmentation technology to split the title content into several smallest unit phrases. Identify content such as names of people, organization names, dates, locations, business segment names, etc. from the text, and match them with the label phrases in the product label library. If the barrage content sent by the customer contains label phrases in the label library, add the phrase as the annotation information of the video, generate customer barrage label data and store it in database C.

[0072] Get comment operation tag data in the following way:

[0073] When a customer finishes or quits watching a video, the background log is used to capture the customer's operations throughout the entire video watching process: on the one hand, the barrage and comment content other than the hot word barrage sent by the customer is identified; on the other hand, the barrage within the time range of the video that the customer repeatedly watches using operations such as fast rewind and navigation keys is identified. Both aspects use a named entity recognition method based on a combination of text classification and part-of-speech tagging, and use JieBa word segmentation technology to split the title content into several minimum unit phrases. Content such as names of people, organization names, dates, locations, and business segment names are identified from the text and matched with the label phrases in the product label library. If the barrage content during the period when the customer repeatedly watches the video contains a label phrase in the label library, the phrase is added as the annotation information of the video, and comment operation label data is generated and stored in database D.

[0074] S102: Determine video tag data according to the tag coefficient, the video title tag data, the hot barrage tag data, the customer barrage tag data and the comment operation tag data.

[0075] The tag storage format of the present invention is as follows:

[0076]

[0077] Among them: the first digit of the variable x represents the video number watched by the customer, the second digit of the variable x represents the phrase number matched with the tag library in the video, the third digit of the variable x represents the step number, and the variable x is the frequency of the phrase in the current state corresponding to the variable x. For example, x 11A The video title tag data (video title tag frequency) of the first phrase matched with the tag library in the first video watched by the customer, x 1jD is the comment operation tag data (comment operation tag frequency) of the jth phrase matched with the tag library in the first video watched by the customer, x ikA is the video title tag data (video title tag frequency) of the kth phrase matched with the tag library in the i-th video watched by the customer, x ikB The hot barrage tag data of the kth phrase matched with the tag library in the i-th video watched by the customer, x ikC The customer comment tag data of the kth phrase matched with the tag library in the i-th video watched by the customer, x ikD The comment operation label data for the k-th phrase matched with the label library in the i-th video watched by the customer.

[0078] Figure 3 is a flow chart of S102 in an embodiment of the present invention. Figure 3 As shown, S102 includes:

[0079] S201: Determine the label data of each label according to the label coefficient, the video title label data, the hot barrage label data, the customer barrage label data and the comment operation label data.

[0080] According to different customer attention levels, different initial label coefficients a0, b0, c0, d0 are assigned to the video title label data, the hot barrage label data, the customer barrage label data and the comment operation label data. The video title tag data is the content information obtained by customers through the video title. The title is short and has strong generalization characteristics. The phrases it contains belong to the core phrases of the video, and the frequency of phrases in the content such as barrage is often lower, so it should be given a higher coefficient; the hot barrage tag data is the phrases extracted from the video barrage and comments. The phrase frequency represents the focus of the general audience of the video on the video. Although it has reference value, it does not fully represent the current customer focus, so it should be given a lower coefficient; the customer barrage tag data is the customer actively sending barrage and comments. It is a subjective operation with a strong distinguishing value. The phrase frequency represents the current customer's focus on certain specific content of the video. It is an important basis for product recommendation, so it should be given the highest coefficient; the comment operation tag data is the barrage and comments in the content that customers watch repeatedly in the video. It also belongs to the customer supervisor operation. The phrase frequency reflects the customer's interest to a certain extent and should be given a higher coefficient. In summary, the initial label coefficient assignments are: a0=0.3, b0=0.1, c0=0.4, d0=0.2.

[0081] In actual applications, the business side is supported to change the initial value configuration based on factors such as the accuracy of video title naming, the quality of barrage comments, and the convenience of function operation (subjective operation cost).

[0082] In specific implementation, the label data of each label can be determined by the following formula:

[0083] f ik (x) = a0x ikA +b0x ikB +c0x ikC +d0x ikD ;

[0084] Among them, f ik (x) is the label data of the kth label in the i-th video watched by the customer, x ikA The video title tag data for the kth tag in the i-th video watched by the customer, x ikB is the hot comment tag data of the kth tag in the i-th video watched by the customer, x ikC is the customer comment label data of the kth label in the i-th video watched by the customer, x ikDIt is the comment operation label data of the kth label in the ith video watched by the customer, a0 is the first label coefficient, b0 is the second label coefficient, c0 is the third label coefficient, and d0 is the fourth label coefficient.

[0085] S202: Determine the video tag data according to the tag data of each tag.

[0086] The maximum value of each label word group value contained in the video can be used to obtain the core word group (the maximum value of the label data) and core word group data (video label data) of the video.

[0087] Figure 4 is a flow chart of S202 in an embodiment of the present invention. Figure 4 As shown, S202 includes:

[0088] S301: Determine the maximum value of the tag data.

[0089] S302: Determine the video tag data according to the maximum value of the tag data and the tag data of each tag.

[0090] In specific implementation, the video tag data can be determined by the following formula:

[0091]

[0092] Among them, f i (x) is the video label data of the i-th video, f im (x) is the maximum value of the label data of the i-th video, and n is the number of labels. Therefore, the higher the frequency of occurrence of the core word and the stronger its specificity in the video (there are no other high-frequency phrases in the video), the higher the core word data.

[0093] S103: Sort the video tag data, and push products corresponding to the video tag data according to the sorting result.

[0094] Figure 5 is a flow chart of S103 in an embodiment of the present invention. Figure 5 As shown, S103 includes:

[0095] S401: Obtain a label corresponding to the video label data according to the sorting result.

[0096] In the specific implementation, all the historical video viewing records of the customer are retrieved, the video tag data corresponding to each video is obtained and sorted, and the following customer preference table is obtained:

[0097]

[0098] Then, the customer preference table is matched with the system product tag library to obtain the recommended products corresponding to each video, and they are displayed to customers in descending order on the product recommendation page, as follows:

[0099]

[0100] S402: Push the product corresponding to the tag.

[0101] During the specific implementation, keywords of the product are extracted, default words are generated in the search bar of the mobile client, and the product is highlighted in the matching section through pop-up ads, product sorting, etc.

[0102] In one embodiment, after executing S103, the process further includes:

[0103] The label coefficient is updated according to the transaction completion data.

[0104] During the specific implementation, we continuously obtain the business operations of customers in the matching section. If they enter the process and complete the business through advertising pop-ups, product sorting, etc., we obtain the transaction data.

[0105] Figure 6 is a flow chart of updating label coefficients in an embodiment of the present invention. Figure 6 As shown, updating the label coefficient according to the transaction data includes:

[0106] S501: Update the video tag data according to the transaction completion data.

[0107] In specific implementation, the updated video tag data is (1+0.01w) p f i (x); where w is the weight parameter, which can be adjusted according to business needs; p is the number of business transactions or transaction volume, and f i (x) is the video label data of the i-th video before updating.

[0108] According to the actual business situation, if the target business / transaction is a key promotion business / transaction or has many operation steps and is difficult to complete, its corresponding weight should be appropriately increased. That is, the higher the cost for customers to complete this business / transaction and the greater the business benefits, the higher the corresponding weight.

[0109] According to the actual business situation, when p is the number of business transactions, the recommended adjustment range of the weight parameter w is (8-12); when p is the transaction volume, the recommended adjustment range of the weight parameter w is (0.8-1.2). For example, when p is the number of business transactions, w=10, and when p is the transaction volume, w=1.

[0110] S502: Update the label coefficient according to the updated video label data.

[0111] Figure 7 is a flow chart of S502 in an embodiment of the present invention. Figure 7 As shown, S502 includes:

[0112] S601: Update the tag data of each tag according to the video tag data.

[0113] S602: Update the label coefficient according to the updated label data of each label.

[0114] In specific implementation, if a customer completes a business transaction or a product transaction based on the product recommendation content, the overall video tag data ranking changes, and the original data can no longer well reflect the customer's needs, that is, the formula f should be updated. ik (x) = a0x ikA +b0x ikB +c0x ikC +d0x ikD a0, b0, c0, d0 in the equation are used to adjust the feedback coefficients for evaluating customer interest points as follows:

[0115]

[0116] Among them, f i '(x) is the updated video label data, f i ' m (x) is the maximum value of the label data of the i-th video after update, f i ' k (x) is the updated label data of the kth label in the ith video, a'0 is the updated first label coefficient, b'0 is the updated second label coefficient, c'0 is the updated third label coefficient, and d'0 is the updated fourth label coefficient.

[0117] Figure 1 The execution body of the product recommendation method based on video bullet comments is the matching section and the calculation module. Figure 1 As can be seen from the process shown, the product recommendation method based on video barrage in an embodiment of the present invention determines the video tag data for sorting according to the tag coefficient, video title tag data, popular barrage tag data, customer barrage tag data and comment operation tag data, and pushes the products corresponding to the video tag data according to the sorting results, thereby improving the overall operating efficiency of the client.

[0118] The specific embodiments of the present invention are as follows:

[0119] For example, fake customer A is a corn farmer. After logging into the mobile client, he enters the video section and watches a video titled "Methods to Increase Corn Yields". At this time, the system background matches the "corn" phrase to the system label vocabulary through the named entity recognition method and stores it as a keyword in database A.

[0120] At the same time, the system is set to extract hot words from the barrage every 20 seconds. During the viewing process, the customer talked about the breeding, care and harvesting methods of corn within a 20-second node. At this time, the system background matched the phrases "corn" (20 times), "seeds" (5 times), and "harvester" (3 times) in the barrage and comments to the system label vocabulary, and stored them as keywords in database B. The system also pushed the customer a message "Do you want to send a barrage of 'like corn' with one click?" The customer selected "Yes" and sent a hot word barrage in response. At this time, the system background stored "corn" in database B, and stored the label vocabulary (such as corn) in the barrage sent by the customer in database C.

[0121] After the video ended, the customer rewound the video to the time section of corn care methods and watched it repeatedly, and sent a comment "Finally learned how to grow corn". At this time, the system background extracted the time section and the customer's comments "corn" (7 times) and "seeds" (1 time) and stored them in database D.

[0122] After the customer exits the video section, the system extracts and matches three tag phrases: "corn", "seeds", and "harvester". The system automatically calculates the corresponding scores for each phrase as follows:

[0123] Corn: f 11 (x) = a0 + 20b0 + c0 + 7d0;

[0124] Seed:f 12 (x) = 5b0 + d0;

[0125] Harvester: f 13 (x)=3b0.

[0126] The comparison shows that "maize" is the core phrase of this video, and the core phrase value is calculated to be

[0127]

[0128] Based on this calculation, the core word group scores corresponding to all video records of the customer are obtained and sorted. The "corn" word group has the highest score, and it is determined that the customer is a corn grower. Then it is matched with the matching information, and the purchase information of corn published in the matching section is filtered out. This information is pushed in the search bar and advertising page to help customers quickly find a market for corn.

[0129] In summary, the product recommendation method based on video barrage provided by the embodiment of the present invention has the following beneficial effects:

[0130] (1) Help customers quickly participate in video interaction by sending bullet comments with one click;

[0131] (2) Capture customers’ interests more accurately based on their video viewing habits;

[0132] (3) Linking video playback with product push for business marketing can reduce customers’ self-learning and search costs and improve the overall operating efficiency of the client.

[0133] Based on the same inventive concept, an embodiment of the present invention also provides a product recommendation device based on video barrage. Since the principle of solving the problem by the device is similar to that of the product recommendation method based on video barrage, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0134] Figure 8 is a structural block diagram of a device for recommending products based on video bullet screens in an embodiment of the present invention. Figure 8 As shown, the product recommendation device based on video barrage includes:

[0135] An acquisition module is used to acquire video title tag data, hot barrage tag data, customer barrage tag data, and comment operation tag data;

[0136] A video tag data module, used to determine the video tag data according to the tag coefficient, the video title tag data, the hot barrage tag data, the customer barrage tag data and the comment operation tag data;

[0137] The sorting module is used to sort the video tag data and push the product corresponding to the video tag data according to the sorting result.

[0138] In one embodiment, the video tag data module includes:

[0139] A label data unit, used to determine the label data of each label according to the label coefficient, the video title label data, the hot barrage label data, the customer barrage label data and the comment operation label data;

[0140] The video tag data unit is used to determine the video tag data according to the tag data of each tag.

[0141] In one embodiment, the video tag data unit includes:

[0142] A maximum value determination subunit, used to determine the maximum value of the label data;

[0143] The video tag data subunit is used to determine the video tag data according to the maximum value of the tag data and the tag data of each tag.

[0144] In one embodiment, it also includes:

[0145] The label coefficient updating module is used to update the label coefficient according to the transaction completion data.

[0146] In one embodiment, the label coefficient updating module includes:

[0147] A video tag data updating unit, configured to update the video tag data according to the transaction completion data;

[0148] The label coefficient updating unit is used to update the label coefficient according to the updated video label data.

[0149] In one embodiment, the label coefficient updating unit includes:

[0150] A label data updating subunit, used for updating label data of each label according to the video label data;

[0151] The label coefficient updating subunit is used to update the label coefficient according to the updated label data of each label.

[0152] In one embodiment, the sorting module includes:

[0153] A label acquisition unit, used to acquire a label corresponding to the video label data according to the sorting result;

[0154] The push unit is used to push the product corresponding to the label.

[0155] In practical applications, product recommendation devices based on video bullet screens include:

[0156] The video section obtains relevant information about the customer's video viewing in the background based on the client's existing basic operations such as video play and pause, and functions such as video title, comment, and barrage, as well as barrage, comments and other information actively operated by the customer.

[0157] The matching section, including the acquisition module and the sorting module, obtains the relevant information of the matching products in the background based on the client's existing matching production and sales information release functions.

[0158] The calculation module, including the video tag data module and the tag coefficient update module, adds calculation logic that the client does not have. By introducing relevant information about the video watched by the customer and performing background calculations, it matches the customer with suitable production and marketing matching products, which are then pushed in search boxes, matching sections, and other locations.

[0159] In summary, the product recommendation device based on video barrage in an embodiment of the present invention determines the video tag data for sorting according to the tag coefficient, video title tag data, popular barrage tag data, customer barrage tag data and comment operation tag data, and pushes the products corresponding to the video tag data according to the sorting results, which can improve the overall operating efficiency of the client.

[0160] Fig. 9 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Fig. 9 As shown, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that Fig. 9 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0161] In one embodiment, the product recommendation method based on video bullet comments may be integrated into the central processing unit 9100. The central processing unit 9100 may be configured to perform the following control:

[0162] Obtain video title tag data, popular barrage tag data, customer barrage tag data, and comment operation tag data;

[0163] Determine the video tag data according to the tag coefficient, the video title tag data, the hot barrage tag data, the customer barrage tag data and the comment operation tag data;

[0164] The video tag data is sorted, and products corresponding to the video tag data are pushed according to the sorting result.

[0165] From the above description, it can be seen that the product recommendation method based on video barrage provided in this application determines the video tag data for sorting according to the tag coefficient, video title tag data, popular barrage tag data, customer barrage tag data and comment operation tag data, and pushes the products corresponding to the video tag data according to the sorting results, which can improve the overall operating efficiency of the client.

[0166] In another embodiment, the product recommendation device based on video barrage can be configured separately from the central processing unit 9100. For example, the product recommendation device based on video barrage can be configured as a chip connected to the central processing unit 9100, and the functions of the product recommendation method based on video barrage can be implemented through the control of the central processing unit.

[0167] like Fig. 9As shown, the electronic device 9600 may also include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Fig. 9 In addition, the electronic device 9600 may also include Fig. 9 For components not shown, reference may be made to the prior art.

[0168] like Fig. 9 As shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0169] The memory 9140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 9100 may execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0170] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0171] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer 9141 (sometimes referred to as a buffer memory). The memory 9140 may include an application / function storage unit 9142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 9600 through the central processor 9100.

[0172] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0173] The communication module 9110 is a transmitter / receiver 9110 that sends and receives signals via an antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0174] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module, etc. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.

[0175] The embodiment of the present invention also provides a computer-readable storage medium capable of implementing all the steps of the product recommendation method based on video barrage in the above embodiment, where the execution subject is a server or a client. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, all the steps of the product recommendation method based on video barrage in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0176] Obtain video title tag data, popular barrage tag data, customer barrage tag data, and comment operation tag data;

[0177] Determine the video tag data according to the tag coefficient, the video title tag data, the hot barrage tag data, the customer barrage tag data and the comment operation tag data;

[0178] The video tag data is sorted, and products corresponding to the video tag data are pushed according to the sorting result.

[0179] In summary, the computer-readable storage medium of an embodiment of the present invention determines the video tag data for sorting according to the tag coefficient, video title tag data, hot barrage tag data, customer barrage tag data and comment operation tag data, and pushes the product corresponding to the video tag data according to the sorting result, which can improve the overall operating efficiency of the client.

[0180] The embodiment of the present invention also provides a computer program product capable of implementing all the steps of the product recommendation method based on video barrage in the above embodiment, where the execution subject is a server or a client. The computer program product includes a computer program / instruction. When the computer program / instruction is executed by a processor, all the steps of the product recommendation method based on video barrage in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0181] Obtain video title tag data, popular barrage tag data, customer barrage tag data, and comment operation tag data;

[0182] Determine the video tag data according to the tag coefficient, the video title tag data, the hot barrage tag data, the customer barrage tag data and the comment operation tag data;

[0183] The video tag data is sorted, and products corresponding to the video tag data are pushed according to the sorting result.

[0184] In summary, the computer program product of the embodiment of the present invention determines the video tag data for sorting according to the tag coefficient, video title tag data, hot barrage tag data, customer barrage tag data and comment operation tag data, and pushes the product corresponding to the video tag data according to the sorting result, which can improve the overall operating efficiency of the client.

[0185] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0186] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0187] Although the present application provides method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-creative labor. The order of steps listed in the embodiments is only one way of executing the order of many steps and does not represent the only execution order. When the actual device or client product is executed, it can be executed in the order of the method shown in the embodiments or the drawings or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0188] Although the present specification embodiment provides the method operation steps as described in the embodiment or flow chart, more or less operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiment is only one way in the order of execution of many steps, and does not represent a unique execution order. When the device or terminal product in practice is executed, it can be executed in sequence or in parallel (such as a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment) according to the method shown in the embodiment or the accompanying drawings. The term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, product or equipment including a series of elements not only includes those elements, but also includes other elements not clearly listed, or also includes elements inherent to such process, method, product or equipment. In the absence of more restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or equipment including the elements.

[0189] For the convenience of description, the above devices are described in various modules according to their functions. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same or more software and / or hardware, or the module implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, 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, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0190] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules for implementing the method and structures within the hardware component.

[0191] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0192] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0193] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0194] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0195] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0196] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0197] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, the embodiments of this specification may take the form of complete hardware embodiments, complete software embodiments or embodiments combining software and hardware. Moreover, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0198] The various embodiments in this specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The embodiments of this specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0199] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. In the description of this specification, the description of the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of this specification. In this specification, the schematic representation of the above terms does not necessarily target the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0200] The above is only an example of the embodiment of the present specification and is not intended to limit the embodiment of the present specification. For those skilled in the art, the embodiment of the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiment of the present specification shall be included in the scope of the claims of the embodiment of the present specification.

Claims

1. A product recommendation method based on video bullet comments, characterized in that: include: Obtain video title tag data, popular barrage tag data, customer barrage tag data, and comment operation tag data; Determine the video tag data according to the tag coefficient, the video title tag data, the hot barrage tag data, the customer barrage tag data and the comment operation tag data; The video tag data is sorted, and products corresponding to the video tag data are pushed according to the sorting result.

2. The product recommendation method based on video barrage according to claim 1 is characterized in that: Determining the video tag data according to the tag coefficient, the video title tag data, the hot barrage tag data, the customer barrage tag data and the comment operation tag data includes: Determine the label data of each label according to the label coefficient, the video title label data, the hot barrage label data, the customer barrage label data and the comment operation label data; The video tag data is determined according to the tag data of each tag.

3. The product recommendation method based on video barrage according to claim 2 is characterized in that: Determining the video tag data according to the tag data of each tag includes: Determine the maximum value of the label data; The video tag data is determined according to a maximum value of the tag data and tag data of each tag.

4. The product recommendation method based on video barrage according to claim 1 is characterized in that: Also includes: The label coefficient is updated according to the transaction completion data.

5. The product recommendation method based on video barrage according to claim 4 is characterized in that: Updating the label coefficient according to the transaction data includes: Update the video tag data according to the transaction completion data; The label coefficient is updated according to the updated video label data.

6. The product recommendation method based on video barrage according to claim 5 is characterized in that: Updating the label coefficient according to the video label data includes: Update the tag data of each tag according to the video tag data; The label coefficients are updated according to the updated label data of each label.

7. The product recommendation method based on video barrage according to claim 1, characterized in that: The products corresponding to the video tag data pushed according to the sorting results include: Obtaining a label corresponding to the video label data according to the sorting result; Push the product corresponding to the tag.

8. A product recommendation device based on video bullet screen, characterized in that: include: An acquisition module is used to acquire video title tag data, hot barrage tag data, customer barrage tag data, and comment operation tag data; A video tag data module, used to determine the video tag data according to the tag coefficient, the video title tag data, the hot barrage tag data, the customer barrage tag data and the comment operation tag data; The sorting module is used to sort the video tag data and push the product corresponding to the video tag data according to the sorting result.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the product recommendation method based on video barrage are implemented as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the product recommendation method based on video barrage are implemented as described in any one of claims 1 to 7.

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