Data recommendation method, device, equipment and computer-readable storage medium

Abstract

The present application discloses a data recommendation method, device, equipment and computer-readable storage medium, which belongs to the field of data processing technology. The method includes: obtaining the search data of the target user; determining the first recommended data corresponding to the search data; obtaining multiple second recommended data related to the first recommended data based on the recommended reference data, and the recommended reference data is obtained based on the historical self-driving data of the target user; and recommending the second recommended data that meets the recommendation conditions among the first recommended data and the multiple second recommended data. The method recommends more recommended data that meet the user's needs to the user based on the user's historical self-driving data, so that the results of data recommendation are more diversified, and through the screening of the recommended conditions, the recommended data is made more accurate, which saves a lot of redundant time, improves the recommendation efficiency, and facilitates the formulation of self-driving routes based on the recommended data.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data recommendation method, device, equipment and computer-readable storage medium. Background Art

[0002] Self-driving tours are becoming more and more popular. For self-driving tour users, they need to search a lot of information to plan their self-driving routes. However, the substantial increase in the amount of information makes it difficult for users to accurately obtain the information they need from a large amount of information. Therefore, it is necessary to recommend data for users to plan self-driving routes.

[0003] In the related art, the most popular local attractions are recommended as recommendation data. Since the above data recommendation method only considers the popularity of local attractions, the result of data recommendation is relatively simple. Summary of the invention

[0004] The present application provides a data recommendation method, apparatus, device and computer-readable storage medium, which can solve the problems in the related art.

[0005] In a first aspect, a data recommendation method is provided, the method comprising:

[0006] Obtain search data of target users;

[0007] Determining first recommended data corresponding to the search data;

[0008] Acquire a plurality of second recommended data related to the first recommended data based on recommended reference data, wherein the recommended reference data is acquired based on historical self-driving data of the target user;

[0009] The second recommended data that meets the recommendation condition among the first recommended data and the plurality of second recommended data is recommended.

[0010] In a possible implementation manner, the first recommendation data includes a plurality of destinations; and the acquiring a plurality of second recommendation data related to the first recommendation data based on the recommendation reference data includes:

[0011] acquiring a plurality of candidate recommendation data based on the recommendation reference data, wherein the candidate recommendation data and the first recommendation data include the same number of destinations;

[0012] Obtaining support degrees of the plurality of candidate recommendation data in the recommendation reference data respectively, wherein the support degrees are used to indicate the relevance degree between the candidate recommendation data and the recommendation reference data;

[0013] The candidate recommendation data whose support degree is greater than the support degree threshold among the multiple candidate recommendation data is used as the second recommendation data.

[0014] In a possible implementation, each destination includes a corresponding recommendation index, where the recommendation index is used to indicate a probability of the destination being recommended; and obtaining the support levels of the plurality of candidate recommendation data in the recommendation reference data respectively includes:

[0015] Serializing the recommended reference data based on the recommendation index corresponding to the destination included in the recommended reference data to obtain a reference sequence corresponding to the recommended reference data;

[0016] For any candidate recommendation data among the plurality of candidate recommendation data, serializing the any candidate recommendation data based on the recommendation index of the destination included in the any candidate recommendation data to obtain a candidate sequence corresponding to the any candidate recommendation data;

[0017] The number of occurrences of the sequence pattern of the candidate sequence in the reference sequence is obtained, and the number of occurrences is used as the support degree of any candidate recommended data in the recommended reference data, wherein the sequence pattern is used to indicate a combination pattern of the arrangement relationship of the candidate sequences.

[0018] In a possible implementation, the recommendation condition includes that the similarity with the first recommended data is greater than a similarity threshold; and recommending the first recommended data and the second recommended data that meets the recommendation condition among the plurality of second recommended data includes:

[0019] Obtaining a similarity between each second recommended data in the plurality of second recommended data and the first recommended data;

[0020] The second recommended data among the first recommended data and the plurality of second recommended data, the second recommended data having a similarity with the first recommended data greater than the similarity threshold, is recommended.

[0021] In a possible implementation, each destination includes a corresponding recommendation index, where the recommendation index is used to indicate a probability of the destination being recommended; and obtaining a similarity between each second recommendation data in the plurality of second recommendation data and the first recommendation data includes:

[0022] performing serialization processing on the first recommendation data based on the recommendation index of the destination included in the first recommendation data, and acquiring a first recommendation sequence corresponding to the first recommendation data;

[0023] For any second recommendation data among the plurality of second recommendation data, serializing the any second recommendation data based on the recommendation index of the destination included in the any second recommendation data, and acquiring a second recommendation sequence corresponding to the any second recommendation data;

[0024] A distance value between the first recommended sequence and the second recommended sequence is obtained based on a dynamic time warping algorithm, and the distance value is used as a similarity between any second recommended data and the first recommended data.

[0025] In a possible implementation manner, before acquiring a plurality of second recommended data related to the first recommended data based on the recommended reference data, the method further includes:

[0026] Acquire a popularity index of the destination included in the recommendation reference data, where the popularity index is used to indicate the number of tourists received by the destination;

[0027] The recommendation index corresponding to the destination included in the recommendation reference data is obtained by combining the travel frequency of the destination included in the historical self-driving data and the popularity index of the destination included in the recommendation reference data.

[0028] In a second aspect, a data recommendation device is provided, the device comprising:

[0029] A first acquisition module is used to acquire the search data of the target user;

[0030] A determination module, used to determine first recommended data corresponding to the search data;

[0031] A second acquisition module, configured to acquire a plurality of second recommended data related to the first recommended data based on recommended reference data, wherein the recommended reference data is acquired based on historical self-driving data of the target user;

[0032] The recommendation module is used to recommend the first recommendation data and the second recommendation data that meets the recommendation condition among the plurality of second recommendation data.

[0033] In one possible implementation, the first recommendation data includes multiple destinations; the second acquisition module is used to acquire multiple candidate recommendation data based on the recommendation reference data, and the number of the candidate recommendation data and the destinations included in the first recommendation data is the same; acquire the support of the multiple candidate recommendation data in the recommendation reference data, respectively, and the support is used to indicate the degree of relevance between the candidate recommendation data and the recommendation reference data; and use the candidate recommendation data whose support is greater than a support threshold among the multiple candidate recommendation data as the second recommendation data.

[0034] In a possible implementation, each destination includes a corresponding recommendation index, and the recommendation index is used to indicate the probability of the destination being recommended; the second acquisition module is used to serialize the recommendation reference data based on the recommendation index corresponding to the destination included in the recommendation reference data, and obtain a reference sequence corresponding to the recommendation reference data; for any candidate recommendation data among the multiple candidate recommendation data, serialize the any candidate recommendation data based on the recommendation index of the destination included in the any candidate recommendation data, and obtain a candidate sequence corresponding to the any candidate recommendation data; obtain the number of times a sequence pattern of the candidate sequence appears in the reference sequence, and use the number of occurrences as the support of the any candidate recommendation data in the recommendation reference data, and the sequence pattern is used to indicate a combination pattern of the arrangement relationship of the candidate sequence.

[0035] In a possible implementation, the recommendation condition includes that the similarity with the first recommended data is greater than a similarity threshold; the recommendation module is used to obtain the similarity between each second recommended data among the multiple second recommended data and the first recommended data; and recommend the first recommended data and the second recommended data among the multiple second recommended data whose similarity with the first recommended data is greater than the similarity threshold.

[0036] In a possible implementation, each destination includes a corresponding recommendation index, and the recommendation index is used to indicate the probability of the destination being recommended; the recommendation module is used to serialize the first recommendation data based on the recommendation index of the destination included in the first recommendation data, and obtain a first recommendation sequence corresponding to the first recommendation data; for any second recommendation data among the multiple second recommendation data, serialize the any second recommendation data based on the recommendation index of the destination included in the any second recommendation data, and obtain a second recommendation sequence corresponding to the any second recommendation data; obtain the distance value between the first recommendation sequence and the second recommendation sequence based on a dynamic time warping algorithm, and use the distance value as the similarity between the any second recommendation data and the first recommendation data.

[0037] In a possible implementation, the second acquisition module is also used to obtain a popularity index of the destination included in the recommended reference data, where the popularity index is used to indicate the number of tourists received by the destination; combining the travel frequency of the destination included in the historical self-driving data and the popularity index of the destination included in the recommended reference data, to obtain the recommendation index corresponding to the destination included in the recommended reference data.

[0038] In a third aspect, a computer device is also provided, comprising a processor and a memory, wherein the memory stores at least one program code, and the at least one program code is loaded and executed by the processor so that the computer device implements any of the above-mentioned data recommendation methods.

[0039] In a fourth aspect, a computer-readable storage medium is also provided, wherein at least one program code is stored in the computer-readable storage medium, and the at least one program code is loaded and executed by a processor so that a computer implements any of the data recommendation methods described above.

[0040] In a fifth aspect, a computer program product or a computer program is also provided, wherein the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs any of the above-mentioned data recommendation methods.

[0041] The technical solution provided by this application can at least bring the following beneficial effects:

[0042] The technical solution provided in this application recommends more recommended data that meet user needs based on the user's historical self-driving data, making the results of data recommendation more diversified, and through the screening of recommendation conditions, making the recommended data more accurate, saving a lot of redundant time, improving recommendation efficiency, and facilitating the formulation of self-driving routes based on recommended data. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application, 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 It is a schematic diagram of an implementation environment of a data recommendation method provided in an embodiment of the present application;

[0045] Figure 2 is a flow chart of a data recommendation method provided in an embodiment of the present application;

[0046] Figure 3 is a flow chart of obtaining multiple second recommendation data provided by an embodiment of the present application;

[0047] Figure 4 is a schematic diagram of a data recommendation device provided in an embodiment of the present application;

[0048] Figure 5 is a structural schematic diagram of a computer device provided in an embodiment of the present application;

[0049] Figure 6 It is a structural diagram of a server provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0051] With the popularity of self-driving travel, the present application embodiment provides a data recommendation method that can actively recommend a variety of recommended data related to the search data to the user based on the user's search data. First, the implementation environment of the data recommendation method provided by the present application embodiment is briefly introduced, see Figure 1 The implementation environment includes a computer device 101. Optionally, an application client is installed in the computer device 101, and the application client includes a display interface. A user can input search data related to self-driving travel through the display interface, and the application client displays recommended data determined based on the search data to the user through the display interface.

[0052] In a possible implementation, the computer device 101 may refer to a terminal or a server. Exemplarily, the terminal may be any electronic product that can interact with a user through one or more methods such as a keyboard, a touchpad, a touch screen, a remote control, voice interaction, or a handwriting device, such as a personal computer (PC), a smart phone, a personal digital assistant (PDA), a wearable device, a handheld computer PPC (Pocket PC, PPC), a tablet computer, an intelligent car machine, etc. The server may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0053] Those skilled in the art should understand that the above-mentioned computer device 101 is only an example, and other existing or future computer devices may also be applicable to the present application, should also be included in the protection scope of the present application, and are included here by reference.

[0054] Based on the above Figure 1In the implementation environment shown, the present application embodiment provides a data recommendation method, which is applied to a computer device 101. The computer device 101 can be a terminal or a server, and the present application embodiment does not limit this. Figure 2 As shown, the data recommendation method provided in the embodiment of the present application includes but is not limited to the following steps 201 to 204.

[0055] Step 201, obtaining the search data of the target user.

[0056] In a possible implementation, the display interface of the application client provides a search entry, through which the target user can input search data, so that the computer device can obtain the search data of the target user. Optionally, the search data is used to indicate the number of regional ranges and destinations, and the destinations can be places such as scenic spots, restaurants, check-in places, or shopping malls. The embodiment of the present application does not limit the division criteria of the regional range. For example, different regional ranges can be divided according to standards such as provinces, cities, and districts.

[0057] The embodiments of the present application do not limit the specific form of the search data, and it is sufficient to indicate the regional scope and the number of destinations. For example, the search data is "One-day tour in Beijing". In this case, the regional scope indicated by the search data is Beijing, and based on experience, it can be predicted that the number of destinations indicated by the search data is 2. Alternatively, the search data is "Destination 1, Destination 2, Destination 3 and Destination 4". In this case, the regional scope indicated by the search data is the common area to which Destination 1, Destination 2, Destination 3 and Destination 4 belong, and the number of destinations indicated by the search data is 4. Alternatively, the search entry includes two search conditions, the regional scope and the number of destinations, and the user enters the search data including the regional scope and the number of destinations according to the search conditions.

[0058] Step 202: determine first recommended data corresponding to the search data.

[0059] In a possible implementation, after the search data of the target user is obtained, the first recommended data with the highest matching degree with the search data can be determined according to the content information included in the search data. Optionally, the recommended data in the embodiment of the present application is a set including at least one destination.

[0060] The embodiment of the present application does not limit the method of determining the first recommended data corresponding to the search data. It is understandable that the specific form of the search data is different, and the method of determining the first recommended data corresponding to the search data is also different. For example, if the search data is "Beijing One-Day Tour", the most popular self-driving route for Beijing One-Day Tour in the self-driving route database can be used as the first recommended data. Alternatively, if the search data is the area range and the number of destinations input by the user, the most popular self-driving route in the self-driving route database that belongs to the area range and includes the number of destinations can be used as the first recommended data. Among them, the popularity can be determined based on the probability of the self-driving route being selected by the user or the user evaluation of the self-driving route. For another example, if the search data is directly "Destination 1, Destination 2, Destination 3 and Destination 4", Destination 1, Destination 2, Destination 3 and Destination 4 can be directly used as the first recommended data.

[0061] Step 203: Acquire a plurality of second recommended data related to the first recommended data based on the recommended reference data, wherein the recommended reference data is acquired based on the historical self-driving data of the target user.

[0062] In the case where the first recommended data corresponding to the search data is usually determined based on the big data of the public's historical self-driving, the historical self-driving data of the target user is not fully considered, so the first recommended data determined for the same search data of different users is often the same, resulting in a relatively single first recommended data. Therefore, after determining the first recommended data corresponding to the search data, the embodiment of the present application further obtains multiple second recommended data related to the first recommended data based on the historical self-driving data of the target user, and can provide more possible recommended data that meet the user's needs.

[0063] Optionally, the historical self-driving data of the target user may include the historical self-driving destinations of the target user and the travel frequencies of each historical self-driving destination. For example, the historical self-driving data includes destination 1, destination 4, destination 3, and destination 2, and the travel frequencies of destination 1, destination 4, destination 3, and destination 2 are 1, 1, 2, and 3, respectively. In one possible implementation, the application client can obtain the user's historical self-driving data. For example, the user's historical self-driving data is obtained based on the recommended data selected by the user in the application client, or the application client obtains the user's historical self-driving data through the Global Positioning System (GPS) of the bound self-driving vehicle.

[0064] In a possible implementation, before obtaining a plurality of second recommended data related to the first recommended data based on the recommended reference data, it is necessary to first obtain the recommended reference data of the target user based on the historical self-driving data of the target user. Optionally, the recommended reference data includes a plurality of destinations within the area, and the plurality of destinations may be all destinations within the area, or may be some of all destinations within the area that are related to the historical self-driving destinations of the target user.

[0065] Exemplarily, the regional scope is determined based on the search data of the target user, and first all destinations within the regional scope are determined. Then, based on the target type to which the target user's historical self-driving destination belongs, destinations corresponding to the target type are filtered from all destinations within the regional scope as recommended reference data. Since the target user has multiple historical self-driving destinations, the multiple destinations may belong to the same type or may belong to multiple different types. Therefore, the target type to which the target user's historical self-driving destination belongs includes at least one type.

[0066] The embodiment of the present application does not limit the classification standard of the type of destination. For example, according to the viewing nature of the destination, the destination is classified into entertainment venues, world cultural heritage sites, check-in places or amusement parks. Or, according to the level of the destination, the destination is classified into 5A-level attractions, 4A-level attractions, 3A-level attractions or free attractions.

[0067] After determining the destinations included in the recommended reference data, further determining the recommendation index corresponding to each destination in the recommended reference data, the recommendation index is used to indicate the probability of the destination being recommended. Optionally, determining the recommendation index corresponding to each destination in the recommended reference data includes: obtaining the popularity index of the destination included in the recommended reference data, the popularity index is used to indicate the popularity of the destination; combining the travel frequency of the destination included in the historical self-driving data and the popularity index of the destination included in the recommended reference data, to obtain the recommendation index corresponding to the destination included in the recommended reference data.

[0068] In a possible implementation, the heat index of a destination can be obtained through Internet big data, and the heat index can indicate the number of tourists received by the destination. For example, the heat index of a destination is the average number of tourists received by the destination in a recent period of time. The more the average number of tourists received, the higher the heat index of the destination, which means that the destination is more popular.

[0069] Optionally, the embodiment of the present application does not limit the method of obtaining the recommendation index corresponding to the destination included in the recommended reference data by combining the travel frequency of the destination included in the historical self-driving data and the heat index of the destination included in the recommended reference data. For example, for the destination included in the historical self-driving data, the sum of the travel frequency corresponding to the destination and the heat index is used as the recommendation index of the destination; or, for the destination included in the historical self-driving data, the product of the heat index of multiple destinations of the same type as the destination and the multiple corresponding to the travel frequency of the destination is used as the recommendation index of the destination. For example, if the travel frequency of the destination is 1, then the multiple corresponding to the travel frequency of the destination is 1.1 times.

[0070] Thus, the target user's recommended reference data is obtained in combination with the target user's historical self-driving data, and the recommended reference data includes multiple destinations, each of which includes a corresponding recommendation index. It is understandable that the number of multiple destinations included in the recommended reference data is greater than the number of multiple destinations included in the first recommended data, and the multiple destinations included in the recommended reference data include the multiple destinations included in the first recommended data. Next, multiple second recommended data related to the first recommended data can be obtained based on the recommended reference data.

[0071] In one possible implementation, see Figure 3 , based on the recommended reference data, multiple second recommended data related to the first recommended data are obtained, including but not limited to the following steps 2031 to 2033.

[0072] Step 2031: Acquire multiple candidate recommendation data based on the recommendation reference data, where the candidate recommendation data and the first recommendation data include the same number of destinations.

[0073] In an embodiment of the present application, the recommended reference data includes all destinations within the area or some destinations within the area that are related to the target user's historical self-driving destinations, and the first recommended data includes multiple destinations corresponding to the search data. Therefore, the number of multiple destinations included in the recommended reference data is greater than the number of multiple destinations included in the first recommended data.

[0074] Optionally, since the number of multiple destinations included in the recommended reference data is greater than the number of multiple destinations included in the first recommended data, random combinations can be performed from the recommended reference data according to the number of multiple destinations included in the first recommended data to obtain multiple candidate recommended data with the same number of destinations as the first recommended data.

[0075] Step 2032: Obtain the support of the plurality of candidate recommendation data in the recommendation reference data, respectively. The support is used to indicate the degree of relevance between the candidate recommendation data and the recommendation reference data.

[0076] In an embodiment of the present application, the recommended reference data is obtained based on the historical self-driving data of the target user. Therefore, for the multiple candidate recommended data, the higher the degree of correlation with the recommended reference data, the more the candidate recommended data meets the needs of the target user, and the more accurate the obtained second recommended data is.

[0077] Optionally, the embodiments of the present application do not limit the manner of obtaining the support of multiple candidate recommendation data in the recommendation reference data. For example, the support of multiple candidate recommendation data in the recommendation reference data can be obtained through a data mining algorithm of a sequence pattern. Optionally, for any candidate recommendation data among the multiple candidate recommendation data, the process of obtaining the support of any candidate recommendation data in the recommendation reference data is as follows: serializing the recommendation reference data and any candidate recommendation data based on the recommendation index corresponding to the destination included in the recommendation reference data, obtaining a reference sequence corresponding to the recommendation reference data and a candidate sequence corresponding to any candidate recommendation data; obtaining the number of occurrences of the sequence pattern of the candidate sequence in the reference sequence, and using the number of occurrences as the support of any candidate recommendation data in the recommendation reference data. Among them, the sequence pattern is used to indicate a combination pattern of the arrangement relationship of the candidate sequence.

[0078] In one possible implementation, taking serialization of recommended reference data as an example, the recommended reference data is serialized based on a recommendation index corresponding to the destination included in the recommended reference data to obtain a reference sequence corresponding to the recommended reference data, including: using the recommendation index corresponding to the destination included in the recommended reference data as a sequence value of the destination included in the recommended reference data, and using the sequence value of the destination included in the recommended reference data as a reference sequence corresponding to the recommended reference data.

[0079] Exemplarily, any candidate sequence includes multiple destinations of destination 1, destination 2, and destination 5, and the reference sequence includes multiple destinations of destination 1-destination n, where n is a positive integer greater than 5. Optionally, any candidate sequence is represented by Q=(Q1, Q2, Q3), and any candidate sequence is represented by S=(S1, S2, ..., S n ) represents the reference sequence, Q1, Q2, and Q3 are the recommendation indexes corresponding to destination 1, destination 2, and destination 5, respectively, S1-S nThe values are the recommended indices corresponding to destinations 1 to n respectively. For example, when n = 6, the recommended indices corresponding to destinations 1 to 6 are 2, 5, 3, 7, 3, and 1 respectively, then the reference sequence is S = (2, 5, 3, 7, 3, 1), and any candidate sequence is Q = (2, 5, 1). The sequence pattern of this any candidate sequence is a combination pattern of the permutation relations of Q1 < Q2, Q2 < Q3, and Q3 > Q1. It can be determined that the number of times the sequence pattern of this any candidate sequence appears in the reference sequence is 1, specifically the support sequence (2, 5, 3). Then the support degree of this any candidate sequence in the recommended reference data is 1.

[0080] Optionally, for the convenience of calculation, the reference sequence and any candidate sequence can be converted into binary data to calculate the support degree of this any candidate sequence in the recommended reference data. For example, through the following formula, S = (S1, S2, …, S n ) is converted into S’ = (a1, a2, …, a n ).

[0081]

[0082] Therefore, S = (2, 5, 3, 7, 3, 1) can be converted into the binary sequence S’ = (1, 0, 1, 0, 0). Similarly, Q = (2, 5, 1) can be converted into the binary sequence Q’ = (1, 0). By comparison, it is found that Q’ appears twice in the same order in S’, that is, positions (a1, a2) and (a3, a4) in S’. That is, the support degree of Q’ in S’ in binary is 2. Since the support degree in binary satisfies the permutation relations of Q1 < Q2 and Q2 < Q3 in the sequence pattern, but it does not verify whether it satisfies the permutation relation of Q3 > Q1 in the sequence pattern.

[0083] Furthermore, the following auxiliary table can be used to verify whether positions (a1, a2) and (a3, a4) satisfy the sequence pattern of Q = (2, 5, 1). First, sort Q = (2, 5, 1) to obtain the sorted sequence pattern Q = (Q3, Q1, Q2). The position index index[i] is the subscript value of the sorted sequence pattern Q = (Q3, Q1, Q2). For example, Q index[1] = Q3 = 1. Taking the support sequence (S1, S2, S3) corresponding to position (a1, a2) as an example, the rank L[i] is the subscript value corresponding to the support sequence (S1, S2, S3). Since in the sequence pattern Q, Q index[i] ≤ Q index[i+1] , where 1 ≤ i ≤ 2. Therefore, if positions (a1, a2) and (a3, a4) satisfy the sequence pattern of Q = (2, 5, 1), then it should satisfy S L[i]-1+index[i] ≤ S L[i]-1+index[i+1] .

[0084] For the support sequence (S1, S2, S3), when i = 1, S 1-1+3 =S3=3,S 1-1+1 =S1=2, at this time S3>S1 does not satisfy the above situation S L[i]-1+index[i] ≤S L[i]-1+index[i+1] , then the support sequence (S1, S2, S3) does not satisfy the sequence pattern of Q = (2, 5, 1). Based on the same principle, calculate whether the support sequence (S3, S4, S5) corresponding to position (a3, a4) satisfies the sequence pattern of Q = (2, 5, 1). When i = 1, S 3-1+3 =S5=3,S 3-1+1 =S3=3, then S5≤S3 satisfies the above situation S L[i]-1+ndex[i] ≤S L[i]-1+index[i+1] , when i = 2, S 3-1+1 =S2=3,S 3-1+2 =S4=7, at this time S2≤S4 satisfies the above situation S L[i]-1+ndex[i] ≤S L[i]-1+index[i+1] Therefore, the supporting sequence (S3, S4, S5) satisfies the sequence pattern condition of Q = (2, 5, 1), and the support degree of any candidate sequence in the recommended reference data is 1.

[0085] Auxiliary table

[0086] The sorted sequence pattern Q 1 2 5 Rank L[i] 1 2 3 Index index[i] 3 1 2

[0087] In the embodiment of the present application, the reference sequence S=(S1, S2, ..., S n ) is obtained by respectively corresponding recommendation indexes of destinations 1 to destinations n included in the reference sequence, and the arrangement order of destinations 1 to destinations n is not limited in this embodiment of the application. For example, the arrangement order of destinations 1 to destinations n in the reference sequence S is randomly arranged. Since the arrangement order of the reference sequence S can change randomly, the second recommendation data obtained each time by the data recommendation method provided in the embodiment of the present application is different, thereby enhancing the diversity of the data recommendation results. And since the recommendation index included in the reference sequence S is obtained based on the user's historical self-driving data, even if the arrangement order of the reference sequence S can change randomly, the second recommendation data obtained each time can meet the user's needs.

[0088] Step 2033: Take the candidate recommendation data whose support degree is greater than the support degree threshold among the multiple candidate recommendation data as the second recommendation data.

[0089] Optionally, the support threshold can be set based on experience, or flexibly adjusted according to the application scenario, for example, the support threshold is the average of the support corresponding to the multiple candidate recommendation data. Thus, the candidate recommendation data with a support greater than the support threshold among the multiple candidate recommendation data is used as the second recommendation data, which realizes the accurate screening of multiple candidate sequences and makes the data recommendation result more accurate.

[0090] Step 204: recommend the first recommended data and the second recommended data that meets the recommendation condition among the plurality of second recommended data.

[0091] Through the above step 203, more possible multiple second recommended data are obtained according to the historical self-driving data of the target user. In this step 204, the multiple second recommended data can be further screened according to the recommendation conditions. The recommendation conditions can be flexibly set according to the application scenario or user needs to make the recommendation results more accurate. In addition, the embodiment of the present application recommends the first recommended data and the second recommended data that meets the recommendation conditions to the user at the same time, so that on the basis of determining the first recommended data, more types of recommended data are provided to the user, increasing the user's selection space.

[0092] Optionally, the embodiments of the present application exemplarily provide the following three methods for setting the recommendation conditions.

[0093] Method 1: The recommendation condition is that the sum of the recommendation indexes of the included destinations is greater than the index threshold.

[0094] In the first mode, the second recommended data whose recommendation index of the destinations included in the plurality of second recommended data is greater than the index threshold is recommended to the user, so that the recommended data obtained by the user has a higher recommendation index and is more likely to be selected by the user. Optionally, the index threshold can be set based on experience, or flexibly adjusted according to the application scenario, for example, the index threshold is the average of the index thresholds corresponding to the second recommended data.

[0095] In a second method, the recommendation condition is that the type of the included destination and the target type of the historical self-driving destination of the target user have a degree of overlap greater than a threshold of the degree of overlap.

[0096] In the second method, the second recommended data whose type of destination included in the plurality of second recommended data and the type of destination of the target user's historical self-driving trip are overlapped by more than the overlap threshold are recommended to the user, so that the recommended data obtained by the user is more in line with his / her travel style and is more likely to be selected by the user. Optionally, the overlap threshold can be set based on experience, or flexibly adjusted according to the application scenario, for example, the overlap threshold is the average of the overlap thresholds corresponding to the second recommended data.

[0097] In one possible implementation, obtaining the degree of overlap between the type of destination included in any second recommendation data and the target type of the target user's historical self-driving destination includes: obtaining a reference type to which the destination included in any second recommendation data belongs, and obtaining the target type to which the target user's historical self-driving destination belongs; obtaining a first number that is the same as the reference type and the target type, and taking the ratio of the first number to the second number of the target type as the degree of overlap.

[0098] Method three: the recommendation condition is that the similarity with the first recommended data is greater than the similarity threshold.

[0099] In this third mode, the second recommended data whose similarity with the first recommended data is greater than the similarity threshold among the plurality of second recommended data is recommended to the user, so that the recommended data obtained by the user is more similar to the first recommended data determined according to the search data and has a greater probability of being selected by the user. Optionally, the similarity threshold can be set based on experience or flexibly adjusted according to the application scenario, for example, the similarity threshold is 60%.

[0100] The embodiments of the present application do not limit the method for calculating the similarity between the second recommended data and the first recommended data. For example, the Euclidean distance, Manhattan distance, Minkowski distance, or cosine similarity between the second recommended data and the first recommended data may be calculated, and the calculated Euclidean distance value, Manhattan distance value, Minkowski distance value, or cosine similarity value may be used as the similarity between the second recommended data and the first recommended data.

[0101] In a possible implementation, obtaining the similarity between any second recommended data and the first recommended data includes: serializing the first recommended data based on the recommendation index of the destination included in the first recommended data to obtain a first recommendation sequence corresponding to the first recommended data; serializing the second recommended data based on the recommendation index of the destination included in the second recommended data to obtain a second recommendation sequence corresponding to the second recommended data; obtaining a distance value between the first recommended sequence and the second recommended sequence based on a dynamic time warping algorithm (Dynamic Time Warping, DTW), and using the distance value as the similarity between the second recommended data and the first recommended data.

[0102] For example, the first recommended sequence is x(x i ), the second recommended sequence is y(y j ) as an example, the sequence lengths of x and y are both m, 1≤i≤m, 1≤j≤m, where m is a positive integer greater than 1. The process of obtaining the distance value between the first recommended sequence and the second recommended sequence based on DTW is to obtain an m*m distance matrix D according to the product of the x and y sequences, where the distance element D[i,0] of the initial row in the distance matrix D is:

[0103] D[i,0]=dis(x i ,y0)+D[i-1,0]

[0104] The distance element D[0, j] of the initial column in the matrix D is:

[0105] D[0,j]=dis(x0,y j )+D[0,j-1]

[0106] The distance elements D[i, j] of the remaining rows or columns in the matrix D are:

[0107] D[i, j] = dis(x i ,y j )+min(D[i-1,j],D[i,j-1],D[i-1,j-1])

[0108] Where dis() is the distance function. The lower right corner element in the distance matrix D, namely D[m, m], is the distance value between the two sequences x and y.

[0109] In a possible implementation, before calculating the distance value between the second recommended sequence and the first recommended sequence through the above DTW algorithm, the first recommended sequence and the second recommended sequence may be data reduced to reduce the sequence values ​​to the same numerical range to speed up the calculation speed and accuracy of DTW.

[0110] Optionally, data reduction is performed on the first recommended sequence, including: determining the maximum value and the minimum value in the first recommended sequence, calculating the difference between the maximum value and the minimum value; then setting the maximum value to 1, setting the minimum value to 0, and the remaining elements in the first recommended sequence are the result of dividing the current element value by the difference between the maximum value and the minimum value. In the same way, data reduction can also be performed on the second recommended sequence, and then DTW calculation is performed on the first recommended sequence and the second recommended sequence after data reduction to obtain the similarity between the second recommended data and the first recommended data.

[0111] Thus, second recommendation data that meets the recommendation conditions is obtained from multiple second recommendation data. Usually, there is at least one second recommendation data that meets the recommendation conditions. The at least one second recommendation data that meets the recommendation conditions and the first recommendation data are recommended to the user through the application client. The user can plan his or her own self-driving route based on the at least one second recommendation data that meets the recommendation conditions and the first recommendation data, thereby improving the efficiency of the user in planning the self-driving route.

[0112] The data recommendation method provided in the embodiment of the present application recommends more recommended data that meet the user's needs to the user based on the user's historical self-driving data, making the results of data recommendation more diversified, and through the screening of recommendation conditions, making the recommended data more accurate, saving a lot of redundant time, improving the recommendation efficiency, and facilitating the user to formulate a self-driving route according to the recommended data.

[0113] See also Figure 4 , an embodiment of the present application provides a data recommendation device, the device comprising:

[0114] The first acquisition module 401 is used to acquire the search data of the target user;

[0115] A determination module 402, configured to determine first recommended data corresponding to the search data;

[0116] A second acquisition module 403 is used to acquire a plurality of second recommended data related to the first recommended data based on the recommended reference data, the recommended reference data being acquired based on the historical self-driving data of the target user;

[0117] The recommendation module 404 is configured to recommend the second recommended data that meets the recommendation condition among the first recommended data and the plurality of second recommended data.

[0118] In one possible implementation, the first recommendation data includes multiple destinations; the second acquisition module 403 is used to acquire multiple candidate recommendation data based on the recommendation reference data, and the number of the candidate recommendation data and the destinations included in the first recommendation data is the same; acquire the support of the multiple candidate recommendation data in the recommendation reference data, respectively, and the support is used to indicate the degree of relevance between the candidate recommendation data and the recommendation reference data; and use the candidate recommendation data whose support is greater than a support threshold among the multiple candidate recommendation data as the second recommendation data.

[0119] In one possible implementation, each destination includes a corresponding recommendation index, and the recommendation index is used to indicate the probability of the destination being recommended; the second acquisition module 403 is used to serialize the recommendation reference data based on the recommendation index corresponding to the destination included in the recommendation reference data, and obtain a reference sequence corresponding to the recommendation reference data; for any candidate recommendation data among the multiple candidate recommendation data, serialize the any candidate recommendation data based on the recommendation index of the destination included in the any candidate recommendation data, and obtain a candidate sequence corresponding to the any candidate recommendation data; obtain the number of times a sequence pattern of the candidate sequence appears in the reference sequence, and use the number of occurrences as the support of the any candidate recommendation data in the recommendation reference data, and the sequence pattern is used to indicate a combination pattern of the arrangement relationship of the candidate sequence.

[0120] In a possible implementation, the recommendation condition includes that the similarity with the first recommended data is greater than a similarity threshold; the recommendation module 404 is used to obtain the similarity between each second recommended data among the multiple second recommended data and the first recommended data; and recommend the first recommended data and the second recommended data among the multiple second recommended data whose similarity with the first recommended data is greater than the similarity threshold.

[0121] In a possible implementation, each destination includes a corresponding recommendation index, and the recommendation index is used to indicate the probability of the destination being recommended; the recommendation module 404 is used to serialize the first recommendation data based on the recommendation index of the destination included in the first recommendation data, and obtain a first recommendation sequence corresponding to the first recommendation data; for any second recommendation data among the multiple second recommendation data, serialize the any second recommendation data based on the recommendation index of the destination included in the any second recommendation data, and obtain a second recommendation sequence corresponding to the any second recommendation data; obtain the distance value between the first recommendation sequence and the second recommendation sequence based on the dynamic time warping algorithm, and use the distance value as the similarity between the any second recommendation data and the first recommendation data.

[0122] In a possible implementation, the second acquisition module 403 is also used to obtain a popularity index of the destination included in the recommended reference data, where the popularity index is used to indicate the number of tourists received by the destination; combining the travel frequency of the destination included in the historical self-driving data and the popularity index of the destination included in the recommended reference data, to obtain a recommendation index corresponding to the destination included in the recommended reference data.

[0123] The data recommendation device provided in the embodiment of the present application recommends more recommended data that meet the user's needs to the user based on the user's historical self-driving data, making the results of data recommendation more diversified, and through the screening of recommendation conditions, making the recommended data more accurate, saving a lot of redundant time, improving the recommendation efficiency, and facilitating the user to formulate a self-driving route according to the recommended data.

[0124] It should be understood that the device provided in the above embodiment only uses the division of the above functional modules as an example to implement its functions. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and their specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0125] Please refer to Figure 5, which shows a schematic diagram of the structure of a computer device provided by an embodiment of the present application. The computer device may be a terminal, such as a smart phone, a tablet computer, a vehicle-mounted terminal, a laptop computer or a desktop computer. The terminal may also be called a user device, a portable terminal, a laptop terminal, a desktop terminal or other names.

[0126] Typically, the terminal includes: a processor 701 and a memory 702 .

[0127] The processor 701 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 701 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 701 may also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 701 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 701 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0128] The memory 702 may include one or more computer-readable storage media, which may be non-transitory. The memory 702 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 702 is used to store at least one instruction, which is used to be executed by the processor 701 to implement the data recommendation method provided in the method embodiment of the present application.

[0129] In some embodiments, the terminal may further optionally include: a peripheral device interface 703 and at least one peripheral device. The processor 701, the memory 702 and the peripheral device interface 703 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 703 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 704, a display screen 705, a camera assembly 706, an audio circuit 707 and a power supply 709.

[0130] The peripheral device interface 703 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 701 and the memory 702. In some embodiments, the processor 701, the memory 702, and the peripheral device interface 703 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 701, the memory 702, and the peripheral device interface 703 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0131] The radio frequency circuit 704 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 704 communicates with communication networks and other communication devices through electromagnetic signals. The radio frequency circuit 704 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 704 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. The radio frequency circuit 704 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes, but is not limited to: a metropolitan area network, various generations of mobile communication networks (2G, 3G, 4G and 5G), a wireless local area network and / or a wireless fidelity (Wireless Fidelity, WiFi) network. In some embodiments, the radio frequency circuit 704 may also include circuits related to NFC (Near Field Communication, short-range wireless communication), which is not limited in this application.

[0132] The display screen 705 is used to display the UI (User Interface). The UI may include graphics, text, icons, videos and any combination thereof. When the display screen 705 is a touch display screen, the display screen 705 also has the ability to collect touch signals on the surface or above the surface of the display screen 705. The touch signal can be input to the processor 701 as a control signal for processing. At this time, the display screen 705 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen 705 can be one, set on the front panel of the terminal; in other embodiments, the display screen 705 can be at least two, respectively set on different surfaces of the terminal or in a folding design; in some other embodiments, the display screen 705 can be a flexible display screen, set on the curved surface or folding surface of the terminal. Even, the display screen 705 can also be set to a non-rectangular irregular shape, that is, a special-shaped screen. The display screen 705 can be made of materials such as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode) and the like.

[0133] The camera assembly 706 is used to capture images or videos. Optionally, the camera assembly 706 includes a front camera and a rear camera. Typically, the front camera is arranged on the front panel of the terminal, and the rear camera is arranged on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, a wide-angle camera, and a telephoto camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, the fusion of the main camera and the wide-angle camera to realize panoramic shooting and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 706 may also include a flash. The flash can be a monochrome temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

[0134] The audio circuit 707 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals and input them into the processor 701 for processing, or input them into the radio frequency circuit 704 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the terminal. The microphone may also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 701 or the radio frequency circuit 704 into sound waves. The speaker may be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 707 may also include a headphone jack.

[0135] The power supply 709 is used to power various components in the terminal. The power supply 709 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 709 includes a rechargeable battery, the rechargeable battery can support wired charging or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0136] In some embodiments, the terminal further includes one or more sensors 710 , including but not limited to: an acceleration sensor 711 , a gyroscope sensor 712 , a pressure sensor 713 , an optical sensor 715 , and a proximity sensor 716 .

[0137] The acceleration sensor 711 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established by the terminal. For example, the acceleration sensor 711 can be used to detect the components of gravity acceleration on the three coordinate axes. The processor 701 can control the display screen 705 to display the user interface in a horizontal view or a vertical view according to the gravity acceleration signal collected by the acceleration sensor 711. The acceleration sensor 711 can also be used for collecting game or user motion data.

[0138] The gyroscope sensor 712 can detect the body direction and rotation angle of the terminal, and the gyroscope sensor 712 can cooperate with the acceleration sensor 711 to collect the user's 3D actions on the terminal. The processor 701 can implement the following functions based on the data collected by the gyroscope sensor 712: motion sensing (such as changing the UI according to the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0139] The pressure sensor 713 can be set in the side frame of the terminal and / or the lower layer of the display screen 705. When the pressure sensor 713 is set in the side frame of the terminal, the user's holding signal of the terminal can be detected, and the processor 701 performs left and right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 713. When the pressure sensor 713 is set in the lower layer of the display screen 705, the processor 701 controls the operability controls on the UI interface according to the user's pressure operation on the display screen 705. The operability controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0140] The optical sensor 715 is used to collect the ambient light intensity. In one embodiment, the processor 701 can control the display brightness of the display screen 705 according to the ambient light intensity collected by the optical sensor 715. Specifically, when the ambient light intensity is high, the display brightness of the display screen 705 is increased; when the ambient light intensity is low, the display brightness of the display screen 705 is reduced. In another embodiment, the processor 701 can also dynamically adjust the shooting parameters of the camera assembly 706 according to the ambient light intensity collected by the optical sensor 715.

[0141] The proximity sensor 716, also known as a distance sensor, is usually arranged on the front panel of the terminal. The proximity sensor 716 is used to collect the distance between the user and the front of the terminal. In one embodiment, when the proximity sensor 716 detects that the distance between the user and the front of the terminal is gradually decreasing, the processor 701 controls the display screen 705 to switch from the screen-on state to the screen-off state; when the proximity sensor 716 detects that the distance between the user and the front of the terminal is gradually increasing, the processor 701 controls the display screen 705 to switch from the screen-off state to the screen-on state.

[0142] Those skilled in the art will understand that Figure 5 The structure shown in the figure does not constitute a limitation on the computer device, and may include more or less components than shown in the figure, or combine some components, or adopt a different component arrangement.

[0143] Please refer to Figure 6 , Figure 6This is a schematic diagram of the structure of a server provided in an embodiment of the present application. The server 600 may have relatively large differences due to different configurations or performances, and may include one or more processors 601 and one or more memories 602, wherein the one or more memories 602 store at least one program instruction, and the at least one program instruction is loaded and executed by the one or more processors 601 to implement the data recommendation method provided in the above-mentioned various method embodiments. Of course, the server 600 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output. The server 600 may also include other components for implementing device functions, which will not be described in detail here.

[0144] In an exemplary embodiment, a computer device is also provided, the computer device comprising a processor and a memory, wherein at least one program code is stored in the memory. The at least one program code is loaded and executed by one or more processors to enable the computer device to implement any of the above data recommendation methods.

[0145] In an exemplary embodiment, a computer-readable storage medium is also provided, in which at least one program code is stored. The at least one program code is loaded and executed by a processor of a computer device to enable the computer to implement any of the above-mentioned data recommendation methods.

[0146] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0147] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs any of the above-mentioned data recommendation methods.

[0148] The terms "first", "second", "third" and "fourth" etc. in the specification and claims of the present application and the drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0149] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions. For example, the search data of the target user involved in this application is obtained with full authorization.

[0150] The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A data recommendation method, characterized in that: The method comprises: Obtain search data of target users; Determine first recommendation data corresponding to the search data, the first recommendation data including a plurality of destinations, each destination including a corresponding recommendation index, the recommendation index being used to indicate a probability of the destination being recommended; Acquire a plurality of second recommended data related to the first recommended data based on recommended reference data, wherein the recommended reference data is acquired based on historical self-driving data of the target user; performing serialization processing on the first recommendation data based on the recommendation index of the destination included in the first recommendation data, and acquiring a first recommendation sequence corresponding to the first recommendation data; For any second recommendation data among the plurality of second recommendation data, serializing the any second recommendation data based on the recommendation index of the destination included in the any second recommendation data, and acquiring a second recommendation sequence corresponding to the any second recommendation data; Acquire a distance value between the first recommended sequence and the second recommended sequence based on a dynamic time warping algorithm, and use the distance value as a similarity between any second recommended data and the first recommended data; The second recommended data among the first recommended data and the plurality of second recommended data, the second recommended data having a similarity with the first recommended data greater than a similarity threshold, is recommended.

2. The method according to claim 1, characterized in that The acquiring a plurality of second recommended data related to the first recommended data based on the recommended reference data includes: acquiring a plurality of candidate recommendation data based on the recommendation reference data, wherein the candidate recommendation data and the first recommendation data include the same number of destinations; Obtaining support degrees of the plurality of candidate recommendation data in the recommendation reference data respectively, wherein the support degrees are used to indicate the relevance degree between the candidate recommendation data and the recommendation reference data; The candidate recommendation data whose support degree is greater than the support degree threshold among the multiple candidate recommendation data is used as the second recommendation data.

3. The method according to claim 2, characterized in that Each destination includes a corresponding recommendation index, and the recommendation index is used to indicate the probability of the destination being recommended; and obtaining the support of the plurality of candidate recommendation data in the recommendation reference data respectively includes: Serializing the recommended reference data based on the recommendation index corresponding to the destination included in the recommended reference data to obtain a reference sequence corresponding to the recommended reference data; For any candidate recommendation data among the plurality of candidate recommendation data, serializing the any candidate recommendation data based on the recommendation index of the destination included in the any candidate recommendation data to obtain a candidate sequence corresponding to the any candidate recommendation data; The number of occurrences of the sequence pattern of the candidate sequence in the reference sequence is obtained, and the number of occurrences is used as the support degree of any candidate recommended data in the recommended reference data, wherein the sequence pattern is used to indicate a combination pattern of the arrangement relationship of the candidate sequences.

4. The method according to claim 1 or 3, characterized in that: Before acquiring a plurality of second recommended data related to the first recommended data based on the recommended reference data, the method further includes: Acquire a popularity index of the destination included in the recommendation reference data, where the popularity index is used to indicate the number of tourists received by the destination; The recommendation index corresponding to the destination included in the recommendation reference data is obtained by combining the travel frequency of the destination included in the historical self-driving data and the popularity index of the destination included in the recommendation reference data.

5. A data recommendation device, characterized in that: The device comprises: A first acquisition module is used to acquire the search data of the target user; a determination module, configured to determine first recommendation data corresponding to the search data, wherein the first recommendation data includes a plurality of destinations, each destination includes a corresponding recommendation index, and the recommendation index is used to indicate a probability of the destination being recommended; A second acquisition module, configured to acquire a plurality of second recommended data related to the first recommended data based on recommended reference data, wherein the recommended reference data is acquired based on historical self-driving data of the target user; A recommendation module, used to serialize the first recommendation data based on the recommendation index of the destination included in the first recommendation data, and obtain a first recommendation sequence corresponding to the first recommendation data; for any second recommendation data among the multiple second recommendation data, serialize the any second recommendation data based on the recommendation index of the destination included in the any second recommendation data, and obtain a second recommendation sequence corresponding to the any second recommendation data; obtain the distance value between the first recommendation sequence and the second recommendation sequence based on a dynamic time warping algorithm, and use the distance value as the similarity between the any second recommendation data and the first recommendation data; recommend the second recommendation data among the first recommendation data and the multiple second recommendation data whose similarity with the first recommendation data is greater than a similarity threshold.

6. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory stores at least one computer program or instruction, and the at least one computer program or instruction is loaded and executed by the processor so that the computer device implements the data recommendation method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one program code, and the at least one program code is loaded and executed by a processor so that a computer implements the data recommendation method according to any one of claims 1 to 4.

8. A computer program product, characterized in that The computer program product includes: a computer program code, and the computer program code is loaded and executed by a computer to enable the computer to implement the data recommendation method according to any one of claims 1 to 4.

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