Business recommendation method and device, electronic equipment, storage medium and program product

Abstract

The invention discloses a service recommendation method and device, electronic equipment, a storage medium and a program product. According to the specific implementation scheme, the method comprises the steps of determining position data, mobile data traffic information and call data of a to-be-recommended object; based on the position data and the mobile data traffic information, determining a target resident position corresponding to the to-be-recommended object, and constructing a service recommendation range corresponding to the target resident position; and based on the call data and the mobile data traffic information, determining recommendation time for recommending services within the service recommendation range to the to-be-recommended object, and recommending services within the service recommendation range to the to-be-recommended object at the recommendation time. According to the method, the service recommendation range is accurately determined, the problem that the recommended service is not matched with the demand of the to-be-recommended object is solved, the recommendation time is determined, the experience feeling of the to-be-recommended object is improved, and the attention degree of the service in the service recommendation range is increased.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to a business recommendation method, apparatus, electronic device, storage medium, and program product. Background Technology

[0002] With the widespread adoption of mobile devices, more and more users rely on them to access information, engage in social interactions, and complete daily tasks. Big data analytics is used to send personalized recommendations to users in an attempt to increase market reach and user engagement for products or services. However, despite technological advancements, the accuracy of current recommendation systems remains insufficient, which not only affects the effectiveness of promotions but also generates user resentment.

[0003] The main existing recommendation methods and their problems are as follows: Location- and time-based automatic recommendation triggers recommendations when a user arrives at a specific geographical location or within a specific time period. However, if the recommended content does not match the user's current needs, or if the push frequency is too high, it can easily cause user resentment and complaints. Recommendation schemes based on computer models and big data analysis require the collection of large amounts of user behavior data to achieve high-precision recommendations. Collecting this type of information is difficult and may lead to privacy risks. Furthermore, processing massive amounts of data and performing complex analysis and calculations places high demands on the system, including storage, processing speed, and algorithm efficiency. Maintaining such a system also requires significant costs. Therefore, there is an urgent need for a business recommendation technology that utilizes readily available data to provide users with a high-quality service experience, addressing the problems of existing technologies. Summary of the Invention

[0004] This invention provides a business recommendation method, apparatus, electronic device, storage medium, and program product to solve the problem of mismatch between recommended content and user needs, thereby improving the user experience.

[0005] According to one aspect of the present invention, a business recommendation method is provided, comprising:

[0006] The location data, mobile data traffic information, and call data of the object to be recommended are determined, wherein the location data, mobile data traffic information, and call data include data related to the object to be recommended within a historical period;

[0007] Based on the location data and the mobile data traffic information, the target permanent location corresponding to the object to be recommended is determined, and the service recommendation range corresponding to the target permanent location is constructed;

[0008] Based on the call data and the mobile data traffic information, a recommendation time is determined to recommend services within the service recommendation range to the target object, and services within the service recommendation range are recommended to the target object at the recommendation time.

[0009] According to another aspect of the present invention, a business recommendation apparatus is provided, comprising:

[0010] The first determining module is used to determine the location data, mobile data traffic information and call data of the object to be recommended, wherein the location data, the mobile data traffic information and the call data include data related to the object to be recommended within a historical period;

[0011] The second determining module is used to determine the target permanent location corresponding to the object to be recommended based on the location data and the mobile data traffic information, and to construct the business recommendation range corresponding to the target permanent location;

[0012] The third determining module is used to determine, based on the call data and the mobile data traffic information, the recommendation time for recommending services within the service recommendation range to the object to be recommended, and to recommend services within the service recommendation range to the object to be recommended at the recommendation time.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the business recommendation method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the business recommendation method described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the business recommendation method described in any embodiment of the present invention.

[0019] The technical solution of this invention involves determining the location data, mobile data traffic information, and call data of the object to be recommended; based on the location data and mobile data traffic information, determining the target permanent location corresponding to the object to be recommended, and constructing a service recommendation range corresponding to the target permanent location; based on the call data and mobile data traffic information, determining the recommendation time for services within the service recommendation range to be recommended to the object to be recommended, and recommending services within the service recommendation range to the object to be recommended at the recommendation time. By determining the service recommendation range for the object to be recommended through location data, mobile data traffic information, and call data, the accurate determination of the service recommendation range is achieved, solving the problem of mismatch between recommended services and the needs of the object to be recommended, and determining the recommendation time improves the user experience of the object to be recommended and increases attention to services within the service recommendation range.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a business recommendation method provided according to Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a business recommendation scope construction method provided in Embodiment 2 of the present invention;

[0024] Figure 3 This is a schematic diagram of a service recommendation range when there are multiple work locations according to Embodiment 2 of the present invention;

[0025] Figure 4 This is a schematic diagram of a service recommendation range when there are multiple residential locations according to Embodiment 2 of the present invention;

[0026] Figure 5 This is a schematic diagram of a business recommendation device according to Embodiment 3 of the present invention;

[0027] Figure 6 This is a block diagram of an electronic device provided according to Embodiment 4 of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] The acquisition, storage, use, and processing of data in the technical solution of this invention all comply with the relevant provisions of laws and regulations.

[0031] Example 1

[0032] Figure 1 This is a flowchart of a service recommendation method according to Embodiment 1 of the present invention. This embodiment is applicable to the case of recommending services. The method can be executed by a service recommendation device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0033] S110. Determine the location data, mobile data traffic information, and call data of the object to be recommended.

[0034] The location data, the mobile data traffic information, and the call data include data related to the target object within a historical period.

[0035] In this embodiment, the object to be recommended can be understood as the object to be recommended for services. The object to be recommended can be an object located within a target area, which can be a region defined by latitude and longitude. Location data can indicate the location of the object to be recommended and the time at which it is located; the location can be represented by latitude and longitude. Mobile data traffic information can be understood as the amount of mobile data used by the mobile device held by the object to be recommended, and the time of mobile data usage. Call data can be understood as the type of call made by the mobile device used by the object to be recommended, and the time of each type; the call type can include inbound or outbound calls.

[0036] Specifically, within a given area, objects that may have business recommendation needs can be filtered out and designated as potential recommendation targets. The system then obtains the historical location data, mobile data traffic information, and call data of these target objects. The location data, mobile data traffic information, and call data can be obtained from the mobile devices held by the target objects.

[0037] For example, location data can be determined using the built-in positioning system (e.g., GPS) of the mobile device held by the target user, or by obtaining data from the base station corresponding to the signal received by the mobile device. For instance, in open areas with strong GPS signals, GPS should be prioritized for location data acquisition. When GPS signals are weak, such as indoors, location data can be obtained from a base station or wireless device. For mobile data traffic information, the mobile data consumption of the target user's mobile device can be collected at different time periods. For call data, the duration of calls made by the target user using the mobile device can be obtained. When acquiring location data for the target user, the collection frequency can be dynamically adjusted based on their activity patterns. The frequency of activity can be determined by analyzing the target user's movement speed and location changes at different time periods. For example, if a large change in location and significant acceleration are detected within a short period, indicating a period of high activity, the data collection frequency can be increased during this time period to obtain more detailed location data; conversely, the collection frequency can be decreased during periods of lower activity.

[0038] S120. Based on the location data and the mobile data traffic information, determine the target permanent location corresponding to the object to be recommended, and construct the service recommendation range corresponding to the target permanent location.

[0039] In this embodiment, the target permanent location can be understood as a location with low mobile data traffic consumption among the spatially clustered locations where the recommended object resides. The service recommendation scope can be the range of services recommended to the recommended object, which can include various services related to daily life.

[0040] Specifically, firstly, based on location data and mobile data traffic information, at least one target permanent location corresponding to the target user is determined. This target permanent location can be the target user's residence or workplace. The target user's workplace and residence generally have broadband network access; therefore, the target permanent location can be determined based on mobile data traffic information. Then, from the at least one target permanent location, the target user's residence and workplace are determined. Finally, based on the locations of the workplace and residence, a closed area is defined, and this closed area serves as the service recommendation scope for the target user. This service recommendation scope can include the area where the user's residence and workplace are located, as well as the area between the two locations.

[0041] S130. Based on the call data and the mobile data traffic information, determine the recommendation time for recommending services within the service recommendation range to the target object, and recommend services within the service recommendation range to the target object at the recommendation time.

[0042] In this embodiment, the recommendation time can be understood as the time period during which services are recommended to the target object. Within the time period indicated by the recommendation time, services within the recommendation range can be recommended to the target object.

[0043] Specifically, the recommendation time for services within the recommended service range can be determined based on call data and mobile data traffic information. For example, services within the recommended service range can be recommended to the target user during a period of high mobile data usage and moderate call data. Methods for recommending services within the recommended service range can include sending notification messages to the mobile devices held by the target user.

[0044] For example, the recommendation time can be adjusted based on historical data for different groups or different scenarios. For instance, for young people, the recommendation time could be in the evening or on weekends.

[0045] The technical solution of this invention involves determining the location data, mobile data traffic information, and call data of the object to be recommended; based on the location data and mobile data traffic information, determining the target permanent location corresponding to the object to be recommended, and constructing a service recommendation range corresponding to the target permanent location; based on the call data and mobile data traffic information, determining the recommendation time for services within the service recommendation range to be recommended to the object to be recommended, and recommending services within the service recommendation range to the object to be recommended at the recommendation time. By determining the service recommendation range for the object to be recommended through location data, mobile data traffic information, and call data, the accurate determination of the service recommendation range is achieved, solving the problem of mismatch between recommended services and the needs of the object to be recommended, and determining the recommendation time improves the user experience of the object to be recommended and increases attention to services within the service recommendation range.

[0046] Based on the above embodiments, modified embodiments of the above embodiments are proposed. It should be noted that, in order to keep the description brief, only the differences from the above embodiments are described in the modified embodiments.

[0047] In one embodiment, determining the recommendation time for recommending services within the service recommendation range to the target user based on the call data and the mobile data traffic information includes:

[0048] Divide the objects to be recommended into at least one time period;

[0049] For each time period, determine the traffic tag of mobile data traffic information within the time period and the call volume tag corresponding to the call data within the time period, wherein the call volume tag indicates the duration of the call corresponding to the call data;

[0050] For each time period, based on the traffic and call volume tags corresponding to that time period, the recommendation time for recommending services within the service recommendation range to the target audience is determined.

[0051] In this embodiment, a time period can be understood as a time interval divided for the object to be recommended, which can divide the object's day into multiple evenly spaced time intervals. A data usage tag can be used to indicate the amount of mobile data used by the object to be recommended within a time period. A call duration tag can be used to indicate the duration of calls made by the object to be recommended within a time period.

[0052] Specifically, based on the user group or application scenario of the target audience, a day can be divided into multiple time slots. The recommendation time can be determined based on the traffic and call volume tags corresponding to the target audience within each time slot, thus deciding whether to include that time slot in the recommended service offerings. When determining the recommendation time, different conditions can be set based on the user group or application scenario of the target audience to meet the needs of different target audiences.

[0053] For example, the user group may include young people, office workers, etc., and the application scenarios may include different seasons, holidays, etc.

[0054] Optionally, determining the recommendation time for recommending services within the service recommendation range to the target audience based on the traffic tags and call volume tags corresponding to the time period includes:

[0055] During the time period, when the traffic tag meets the second set traffic condition and the call volume tag meets the set call volume condition, the time period is determined as the recommendation time for recommending services within the service recommendation range to the target to be recommended.

[0056] Wherein, the second set traffic condition indicates that the value of the mobile data traffic information corresponding to the traffic tag is greater than the second traffic threshold, and the set call volume condition indicates that the value of the call data corresponding to the call volume tag is greater than the first call threshold and less than the second call threshold.

[0057] In this embodiment, the second set traffic condition can be understood as determining whether the mobile data traffic usage exceeds a second traffic threshold. The set call volume condition can be understood as determining whether the value corresponding to the call data is between a first call threshold and a second call threshold; the value corresponding to the call data is the call duration. The second traffic threshold can be understood as the mobile data traffic usage threshold set when determining the recommended time. The first call threshold and the second call threshold can be understood as the set call data values, where the value of the first call threshold must be less than the value of the second call threshold.

[0058] Specifically, the recommendation time can be set during a period when the mobile data usage of the target user is high and the call data is moderate. Therefore, the second set data usage condition is met (i.e., the mobile data usage value corresponding to the data usage tag is greater than the second data usage threshold), and the set call data usage condition is met (i.e., the call data value corresponding to the call data usage tag is greater than the first call data usage threshold but less than the second call data usage threshold). Within a certain time period, when both the data usage tag and the call data usage tag meet the set call data usage condition, that time period is determined as the recommendation time for services within the recommended range to be recommended to the target user. Different second data usage thresholds, first call data usage thresholds, and second call data usage thresholds can be set for different user groups or application scenarios to which the target user belongs. Therefore, the second set data usage condition and the set call data usage condition can be different for different target users.

[0059] For example, for potential users belonging to the younger demographic, who typically have more leisure time in the evenings and on weekends, the periods with high mobile data usage and moderate call volume might be 8-11 PM and 2-5 PM on weekends. During these periods, a higher second data usage threshold can be set, for example, 500MB. The first call threshold would be 10% of the total call duration, and the second call threshold would be 20% of the total call duration. For potential users belonging to the working demographic, the periods with high mobile data usage and moderate call volume might be weekday lunch breaks (12-2 PM) and evenings after get off work (7-9 PM). During lunch breaks, the second data usage threshold can be appropriately reduced to 300MB, as these users may primarily engage in some light online activity while still maintaining a certain level of activity.

[0060] Thresholds can also be adjusted for different seasons and holidays. In summer evenings, users may be more inclined to use mobile data while enjoying the cool evening air outdoors. Historical data analysis can be used to adjust the second data usage threshold accordingly. For example, if historical data shows that data usage is generally about 20% higher in summer evenings than in winter evenings, the data usage threshold for summer evenings can be increased accordingly. During holidays such as National Day and Spring Festival, the schedules and data usage habits of younger users and working professionals change. During these periods, the weight of call volume in threshold judgment can be reduced, while the weight of data usage can be increased. For example, the second data usage threshold can be increased by 30%-50%, while the first call threshold can be relaxed to 30% of the total call duration, and the second call threshold can be relaxed to 40% of the total call duration.

[0061] In one embodiment, determining the location data of the object to be recommended includes:

[0062] Obtain the original location data corresponding to the object to be recommended within the historical period. The original location data includes the latitude and longitude of the object to be recommended and the dwell time of the object to be recommended when it is located at the latitude and longitude.

[0063] Invalid data points are removed from the original location data to obtain the location data of the object to be recommended. The invalid data points include data points with abnormal latitude and longitude or abnormal positioning time.

[0064] In this embodiment, the original location data can be understood as the latitude and longitude of the object to be recommended, and the dwell time of the object at that latitude and longitude. Latitude and longitude can be understood as the coordinates used to determine the location of the object to be recommended. Dwell time can be understood as the time when the object to be recommended is located at the latitude and longitude. Invalid data points can be understood as data in the original location data whose latitude and longitude are out of range without a reasonable reason (such as the object to be recommended cannot appear in an extremely distant area instantly), or whose dwell time does not conform to normal time logic (such as time reversal, a large difference between future time and current system time, etc.).

[0065] For example, the original location data corresponding to the object to be recommended within the historical period is obtained, including the latitude and longitude of the object and the dwell time of the object when it is located at that latitude and longitude. Then, invalid data points in the original location data are removed to obtain the location data of the object to be recommended. For example, if the target area of ​​the object to be recommended is a city, but the latitude and longitude data shows that the object to be recommended moved instantly from one end of the city to another city thousands of miles away, and there is no reasonable record of rapid movement such as taking a plane, then this data point is considered invalid. For duplicate latitude and longitude and dwell time data in the original location data, the earliest record is retained. Data points with extremely small latitude and longitude deviations and extremely short dwell time intervals (such as within a few seconds) are merged.

[0066] Example 2

[0067] Figure 2 This is a flowchart of a method for constructing a business recommendation scope according to Embodiment 2 of the present invention. This embodiment focuses on the method for constructing the business recommendation scope in the above embodiment. Figure 2 As shown, the method includes:

[0068] S210. Determine the location data, mobile data traffic information, and call data of the object to be recommended.

[0069] S220. Based on the location data and the mobile data traffic information, determine at least one target permanent location corresponding to the object to be recommended.

[0070] Specifically, firstly, based on location data, clusters of geographically concentrated points among the various geographical locations of the target object are identified, and the location of these clusters is determined as the initial permanent location. Therefore, the initial permanent location is the location of the clustered geographically concentrated points among the geographical locations of the target object. Next, based on the mobile data traffic usage information, the target permanent location corresponding to the target object within the initial permanent location is determined. Since the target permanent location generally has broadband network access, mobile data usage is relatively low; therefore, the target permanent location can be determined by analyzing the mobile data traffic usage at the initial permanent location.

[0071] Optionally, determining at least one target permanent location corresponding to the object to be recommended based on the location data and the mobile data traffic information includes:

[0072] The location data is identified to obtain at least one point group corresponding to the object to be recommended, and the point group includes geographical locations with clustering.

[0073] In the at least one point group, at least one target point group is determined, and the location of the target point group is determined as the initial permanent location. The number of dwell points among the geographical locations included in the target point group is greater than a set dwell point threshold, and the difference between the location of the dwell point and the location of the geographical location adjacent to the dwell point is less than a set location difference threshold.

[0074] For each initial permanent location, a traffic tag is determined corresponding to the mobile data traffic information at the initial permanent location, and the traffic tag indicates the usage of the mobile data traffic corresponding to the mobile data traffic information;

[0075] The initial permanent location corresponding to the traffic tag that satisfies the first set traffic condition is determined as the target permanent location. The first set traffic condition indicates that the value of the mobile data traffic information corresponding to the traffic tag is less than the first traffic threshold.

[0076] In this embodiment, the point cluster can be formed by clustered geographical locations of the target object. The target point cluster can be understood as a cluster where the number of dwell points is greater than a set dwell point threshold. A dwell point can be a geographical location indicating that the location of the target object has not changed significantly. The set dwell point threshold can be understood as a threshold used to determine the number of dwell points in the point cluster. The set location difference threshold can be understood as a threshold used to determine whether a geographical location point is a dwell point; the set location difference threshold can be a set value indicating the location difference. The first set traffic condition can be understood as a condition to determine whether the mobile data traffic usage is less than a first traffic threshold. The first traffic threshold can be understood as a mobile data traffic usage threshold set when determining the target's permanent location.

[0077] For example, clustering algorithms can be used to identify location data, determine the spatial clustering of the stopping locations of the objects to be recommended, and find at least one group of points formed by geographically clustered geographical locations.

[0078] For each point group, if the number of dwell points in the point group exceeds a set dwell point threshold, the point group is designated as a target point group. When determining target point groups, sampling can be used. Sampling can be based on time proportions; for example, if there are 100 points from 9:00 to 12:00 and 10 points from 15:00 to 17:00, 10 points can be sampled from 9:00 to 12:00 and 1 point from 15:00 to 17:00. The results of all sampled points are then used to determine if the number of dwell points in the point group exceeds the set dwell point threshold. When determining dwell points, for each geographical location point in a point group, the distance difference between that geographical location point and its immediate neighbors can be used to determine if it is a dwell point. If the distance difference is less than a set location difference threshold, that geographical location point is designated as a dwell point. After obtaining at least one target point group, the center point of the target point group can be determined as the initial permanent location.

[0079] For each initial permanent location, a traffic tag is determined for that location. Traffic tags can include low, medium, and high traffic ranges. The average and standard deviation of mobile data usage for each target user over a month can be calculated. Usage below the average minus one standard deviation is classified as low traffic; usage between the average minus one standard deviation and the average plus one standard deviation is classified as medium traffic; and usage above the average plus one standard deviation is classified as high traffic (a second traffic threshold can be set as usage above the average plus one standard deviation). Considering the differences in mobile data usage across different time periods, peak and off-peak usage times can be weighted when acquiring mobile data usage data. For example, usage is lower and weighted during evening rest periods, while usage is relatively higher during daytime work and leisure periods. Weighting coefficients are assigned to each time period by statistically analyzing the traffic usage ratio of the target user at different times (e.g., 6-9 AM, 9-12 AM, 12-2 PM). For example, the weight of daytime working hours can be set to 1.2-1.5, and the weight of evening rest time can be set to 0.5-0.8.

[0080] The initial permanent location corresponding to the traffic tag that meets the first preset traffic condition is determined as the target permanent location. The first preset traffic condition indicates that the mobile data traffic information corresponding to the traffic tag is less than a first traffic threshold. This first traffic threshold can be determined as the mobile data traffic usage being lower than the average value minus one standard deviation. Therefore, the initial permanent location of the traffic tag in the low traffic range can be determined as the target permanent location.

[0081] S230. For each target's permanent location, determine the permanent location time corresponding to that location in the location data.

[0082] The dwell time includes the dwell time of the object to be recommended when it is located at the target dwell location within the historical period.

[0083] In this embodiment, the dwell time can be understood as the dwell time of the object to be recommended when it is located at the target dwell location.

[0084] Specifically, based on the dwell time indicated by the location data when the target is located at each geographical location in the target point group corresponding to the target's permanent location, the dwell time corresponding to the target's permanent location is obtained.

[0085] S240. Determine the target permanent residence location corresponding to the permanent residence time that meets the residence time condition among the various permanent residence times as the residence location, and determine the target permanent residence location corresponding to the permanent residence time that meets the work time condition among the various permanent residence times as the work location.

[0086] In this embodiment, the residence time condition can be understood as a condition used to determine whether the target's permanent residence location is a residence location. The workplace time condition can be understood as a condition used to determine whether the target's permanent residence location is a workplace location.

[0087] Specifically, there can be multiple residential and work locations. The residential location corresponds to a longer period of time spent in the workplace. Therefore, a longer period of time can be designated as meeting the residential location time requirement, and the target residential location corresponding to that period of time can be determined as the residential location. Conversely, a shorter period of time can be designated as meeting the work location time requirement, and the target residential location corresponding to that period of time can be determined as the work location.

[0088] For example, time series analysis can be used to determine residential and workplace locations. By analyzing the location and call data of the individuals to be recommended at different times, we can observe whether their activity patterns exhibit significant periodicity. For instance, if the individuals exhibit different activity patterns on weekdays and weekends, during weekdays, they might travel from their residence to their workplace in the morning, showing a clear movement trajectory; and return from their workplace to their residence in the evening, exhibiting a clear round-trip pattern. By analyzing location data for a week or even a month, we can extract periodic features to determine whether there are obvious daily or weekly cycles. Simultaneously, combining this with the distribution of call data at different times further verifies and refines the periodic features. Furthermore, we can utilize the daily behavioral habits data of the individuals to be recommended to assist in determining their residential and workplace locations. For example, a pattern of staying at a certain location between 7-9 am with a gradual increase in mobile data usage, and returning from another location between 6-9 pm with another increase in mobile data usage, might correspond to a relationship between their residential and workplace locations.

[0089] S250. Based on the residential location and the workplace location, construct the business recommendation range corresponding to the object to be recommended.

[0090] For example, two circles are formed with the residential location and the workplace location as the centers, and the business recommendation scope is defined by the two circles. Businesses within the recommendation scope are then recommended to the target. Figure 3 This is a schematic diagram of a service recommendation range when there are multiple work locations, according to Embodiment 2 of the present invention. Figure 4 This is a schematic diagram illustrating a service recommendation range for multiple residence locations according to Embodiment 2 of the present invention. Figure 3 The diagram illustrates the method for defining the scope of business recommendations when multiple work locations exist. For example... Figure 4 The diagram illustrates the method for defining the scope of business recommendations when multiple residential locations exist.

[0091] Optionally, constructing the business recommendation scope corresponding to the object to be recommended based on the residential location and the workplace location includes:

[0092] The residential area is defined by taking the location of the residence as the center and the radius of the residence as the radius. The radius of the residence is related to the distribution density of infrastructure in the area where the residence is located.

[0093] The work area is defined by taking the work location as the center and the work location radius as the radius. The work location radius is related to the supporting service information, office area concentration measurement information, and transportation convenience measurement information of the area where the work location is located.

[0094] The closed area formed by connecting the residential area, the workplace area, and the tangents between the residential area and the workplace area is determined as the business recommendation range corresponding to the object to be recommended.

[0095] In this embodiment, the residential radius can be understood as a numerical value used to delineate the scope of a residential area. The residential radius can be determined based on the distribution density of infrastructure in the area where the residential location is located. The residential scope can be understood as the area where the residential location is located; the residential scope can be a circle with the residential location as the center and the residential radius as the radius. The workplace radius can be understood as a numerical value used to delineate the scope of a workplace; the workplace radius can be determined based on supporting service information, office area concentration measurement information, and transportation convenience measurement information in the area where the workplace is located. The workplace scope can be understood as the area where the workplace is located; the workplace scope can be a circle with the workplace location as the center and the workplace radius as the radius. Transportation convenience measurement information can be understood as a numerical value used to describe the level of transportation convenience within the area where the workplace is located. Office area concentration measurement information can be understood as used to describe the density of office areas within the area where the workplace is located.

[0096] For example, the radius D1 of a residence is determined based on the density of infrastructure distribution in the area where the residence is located. If the residence is located in a central urban area with abundant surrounding commercial and living service facilities, D1 is set relatively small, such as 1-2 kilometers. In this case, the target audience can meet most of their living service needs within a smaller radius, and the smaller radius more accurately defines the scope of business recommendations. If the residence is located in a suburban area or other areas with relatively dispersed infrastructure, D1 is set to 3-5 kilometers to cover a wider range of possible business recommendations.

[0097] The radius D2 of the work location is determined by considering factors such as the availability of supporting services, the concentration of office space, and accessibility. If the work location is in an area with a high concentration of office space and abundant supporting services, D2 is set to 0.5-1 km. If the work location is in a large area, such as an industrial park, but with low office space concentration and limited supporting services, D2 is set to 2-3 km. Furthermore, considering accessibility, the radius should be appropriately reduced if the work location is in an area with good transportation, such as proximity to subway hubs or bus transfer stations; conversely, it should be appropriately increased if transportation is inconvenient.

[0098] like Figure 3 and Figure 4 The diagram shows the work location and residence location. Connecting the tangents between the work location and residence location, and combining them, forms a closed area. This closed area defines the business recommendation scope for the target user. This closed area primarily includes the area where the target user travels between their work location and residence.

[0099] S260. Based on the call data and the mobile data traffic information, determine the recommendation time for recommending services within the service recommendation range to the target object, and recommend services within the service recommendation range to the target object at the recommendation time.

[0100] The technical solution of this invention, based on the location data and the mobile data traffic information, determines at least one target permanent location corresponding to the object to be recommended; for each target permanent location, determines the permanent time corresponding to the target permanent location in the location data; determines the target permanent location corresponding to the permanent time that meets the residential time condition among the permanent times among the permanent times, and determines the target permanent location corresponding to the permanent time that meets the work time condition among the permanent times among the permanent times among the permanent times; based on the residential location and the work location, constructs the service recommendation range corresponding to the object to be recommended. This refines the method of determining the service recommendation range of the object to be recommended through location data, mobile data traffic information, and call data, achieving accurate determination of the service recommendation range, solving the problem of mismatch between recommended services and the needs of the object to be recommended, and improving the user experience of the object to be recommended.

[0101] Example 3

[0102] Figure 5 This is a schematic diagram of a business recommendation device according to Embodiment 3 of the present invention. Figure 5 As shown, the device includes:

[0103] The first determining module 310 is used to determine the location data, mobile data traffic information and call data of the object to be recommended, wherein the location data, the mobile data traffic information and the call data include data related to the object to be recommended within a historical period;

[0104] The second determining module 320 is used to determine the target permanent location corresponding to the object to be recommended based on the location data and the mobile data traffic information, and to construct the service recommendation range corresponding to the target permanent location;

[0105] The third determining module 330 is used to determine, based on the call data and the mobile data traffic information, the recommendation time for recommending services within the service recommendation range to the object to be recommended, and to recommend services within the service recommendation range to the object to be recommended at the recommendation time.

[0106] The service recommendation device provided in this embodiment of the invention determines the location data, mobile data traffic information, and call data of the object to be recommended through a first determining module; determines the target permanent location corresponding to the object to be recommended through a second determining module based on the location data and mobile data traffic information, and constructs the service recommendation range corresponding to the target permanent location; and determines the recommendation time for recommending services within the service recommendation range to the object to be recommended through a third determining module based on the call data and mobile data traffic information, and recommends services within the service recommendation range to the object to be recommended during the recommendation time. Through the cooperation between the modules, the service recommendation range for the object to be recommended is determined based on location data, mobile data traffic information, and call data, achieving accurate determination of the service recommendation range, solving the problem of mismatch between recommended services and the needs of the object to be recommended, and determining the recommendation time, thereby improving the user experience of the object to be recommended and increasing attention to services within the service recommendation range.

[0107] In one embodiment, the second determining module 320 includes:

[0108] The first determining unit is used to determine at least one target permanent location corresponding to the object to be recommended based on the location data and the mobile data traffic information.

[0109] The second determining unit is used to determine the dwell time corresponding to the target dwell location in the location data for each target dwell location, wherein the dwell time includes the dwell time of the object to be recommended when it is located at the target dwell location within the historical period;

[0110] The third determining unit is used to determine the target permanent location corresponding to the permanent location that meets the residence time condition among the permanent residence times as the residence location, and to determine the target permanent location corresponding to the permanent location that meets the work time condition among the permanent residence times as the work location.

[0111] The construction unit is used to construct the business recommendation range corresponding to the object to be recommended based on the residential location and the workplace location.

[0112] In one embodiment, the building unit is specifically used for:

[0113] The residential area is defined by taking the location of the residence as the center and the radius of the residence as the radius. The radius of the residence is related to the distribution density of infrastructure in the area where the residence is located.

[0114] The work area is defined by taking the work location as the center and the work location radius as the radius. The work location radius is related to the supporting service information, office area concentration measurement information, and transportation convenience measurement information of the area where the work location is located.

[0115] The closed area formed by connecting the residential area, the workplace area, and the tangents between the residential area and the workplace area is determined as the business recommendation range corresponding to the object to be recommended.

[0116] In one embodiment, the first determining unit is specifically used for:

[0117] The location data is identified to obtain at least one point group corresponding to the object to be recommended, and the point group includes geographically clustered points.

[0118] In the at least one point group, at least one target point group is determined, and the location of the target point group is determined as the initial permanent location. The number of dwell points among the geographical locations included in the target point group is greater than a set dwell point threshold, and the difference between the location of the dwell point and the location of the geographical location adjacent to the dwell point is less than a set location difference threshold.

[0119] For each initial permanent location, a traffic tag is determined corresponding to the mobile data traffic information at the initial permanent location, and the traffic tag indicates the usage of the mobile data traffic corresponding to the mobile data traffic information;

[0120] The initial permanent location corresponding to the traffic tag that satisfies the first set traffic condition is determined as the target permanent location. The first set traffic condition indicates that the value of the mobile data traffic information corresponding to the traffic tag is less than the first traffic threshold.

[0121] In one embodiment, the third determining module 330 includes:

[0122] A segmentation unit is used to divide the object to be recommended into at least one time period;

[0123] The fourth determining unit is used to determine, for each time period, the traffic tag of the mobile data traffic information within the time period and the call volume tag corresponding to the call data within the time period, wherein the call volume tag indicates the duration of the call corresponding to the call data;

[0124] The fifth determining unit is used to determine, for each time period, the recommendation time for recommending services within the service recommendation range to the object to be recommended, based on the traffic tag and call volume tag corresponding to the time period.

[0125] In one embodiment, the fifth determining unit is specifically used for:

[0126] During the time period, when the traffic tag meets the second set traffic condition and the call volume tag meets the set call volume condition, the time period is determined as the recommendation time for recommending services within the service recommendation range to the target to be recommended.

[0127] Wherein, the second set traffic condition indicates that the value of the mobile data traffic information corresponding to the traffic tag is greater than the second traffic threshold, and the set call volume condition indicates that the value of the call data corresponding to the call volume tag is greater than the first call threshold and less than the second call threshold.

[0128] In one embodiment, the first determining module 310 is specifically used for:

[0129] Obtain the original location data corresponding to the object to be recommended within the historical period. The original location data includes the latitude and longitude of the object to be recommended and the dwell time of the object to be recommended when it is located at the latitude and longitude.

[0130] Invalid data points are removed from the original location data to obtain the location data of the object to be recommended. The invalid data points include data points with abnormal latitude and longitude or abnormal positioning time.

[0131] The business recommendation device provided in this embodiment of the invention can execute the business recommendation method provided in any embodiment of the invention. Through the cooperation and collaborative work between the modules, it completes the recommendation of businesses and has the corresponding functional modules and beneficial effects of the execution method.

[0132] Example 4

[0133] According to embodiments of the present invention, the present invention also provides an electronic device, a computer-readable storage medium, and a computer program product.

[0134] Figure 6This is a block diagram of an electronic device according to Embodiment 4 of the present invention, which implements the business recommendation method described in the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0135] like Figure 6 As shown, the electronic device 410 includes at least one processor 411 and a memory, such as a read-only memory (ROM) 412 or a random access memory (RAM) 413, communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. The processor 411 can perform various appropriate actions and processes based on the computer program stored in the ROM 412 or loaded from storage unit 418 into the RAM 413. The RAM 413 may also store various programs and data required for the operation of the electronic device 410. The processor 411, ROM 412, and RAM 413 are interconnected via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0136] Multiple components in the electronic device are connected to the I / O interface 415, including: an input unit 416, such as a keyboard, mouse, etc.; an output unit 417, such as various types of displays, speakers, etc.; a storage unit 418, such as a disk, optical disk, etc.; and a communication unit 419, such as a network card, modem, wireless transceiver, etc. The communication unit 419 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0137] Processor 411 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 411 performs the various methods and processes described above, such as business recommendation methods.

[0138] In some embodiments, the business recommendation method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 410 via ROM 412 and / or communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the business recommendation method described above may be performed. Alternatively, in other embodiments, processor 411 may be configured to perform the business recommendation method by any other suitable means (e.g., by means of firmware).

[0139] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0140] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0141] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0142] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0143] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0144] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0145] In some embodiments, the computer program product includes a computer program that, when executed by a processor, implements the business recommendation method provided in the embodiments of the present invention.

[0146] The technical solution of this invention provides a service recommendation method, apparatus, electronic device, storage medium, and program product. It determines the location data, mobile data traffic information, and call data of the object to be recommended; based on the location data and mobile data traffic information, it determines the target permanent location corresponding to the object to be recommended and constructs a service recommendation range corresponding to the target permanent location; based on the call data and mobile data traffic information, it determines the recommendation time for recommending services within the service recommendation range to the object to be recommended, and recommends services within the service recommendation range to the object to be recommended at the recommendation time. By determining the service recommendation range for the object to be recommended through location data, mobile data traffic information, and call data, it achieves accurate determination of the service recommendation range, solves the problem of mismatch between recommended services and the needs of the object to be recommended, and determines the recommendation time, improving the user experience of the object to be recommended and increasing attention to services within the service recommendation range.

[0147] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0148] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A service recommendation method characterized by comprising: The method comprises the following steps: determining position data, mobile data traffic information and call data of a to-be-recommended object, the position data, the mobile data traffic information and the call data comprising data related to the to-be-recommended object in a historical period; based on the position data and the mobile data traffic information, determining a target permanent location corresponding to the to-be-recommended object, and constructing a business recommendation range corresponding to the target permanent location; based on the call data and the mobile data traffic information, determining a recommendation time for recommending a business in the business recommendation range to the to-be-recommended object, and recommending the business in the business recommendation range to the to-be-recommended object at the recommendation time.

2. The method of claim 1, wherein, The step of determining the target permanent location corresponding to the to-be-recommended object based on the position data and the mobile data traffic information comprises the following steps: determining at least one target permanent location corresponding to the to-be-recommended object based on the position data and the mobile data traffic information; for each target permanent location, determining a permanent time corresponding to the target permanent location in the position data, the permanent time comprising a residence time of the to-be-recommended object at the target permanent location in the historical period; determining a target permanent location corresponding to a permanent time meeting a residence time condition as a residence location, and determining a target permanent location corresponding to a permanent time meeting a working time condition as a working location; based on the residence location and the working location, constructing a business recommendation range corresponding to the to-be-recommended object.

3. The method of claim 2, wherein, The step of constructing the business recommendation range corresponding to the to-be-recommended object based on the residence location and the working location comprises the following steps: constructing a residence range with the residence location as the center and a residence radius as the radius, the residence radius being related to the distribution density of infrastructure in the area where the residence location is located; constructing a working range with the working location as the center and a working radius as the radius, the working radius being related to supporting service information, office area concentration degree measurement information and traffic convenience measurement information in the area where the working location is located; connecting the residence range, the working range and the tangent line between the residence range and the working range to form a closed range, and determining the closed range as the business recommendation range corresponding to the to-be-recommended object.

4. The method of claim 2, wherein, The step of determining the at least one target permanent location corresponding to the to-be-recommended object based on the position data and the mobile data traffic information comprises the following steps: identifying the position data to obtain at least one point group corresponding to the to-be-recommended object, the point group comprising geographic location points with aggregation; in the at least one point group, determining at least one target point group, and determining a location of the target point group as an initial permanent location, the target point group comprising a number of stay points greater than a set stay point threshold value, and a difference between a location of the stay point and a location of a geographic location point adjacent to the stay point being less than a set location difference threshold value; For each initial resident location, determine a traffic label corresponding to the mobile data traffic information at the initial resident location, the traffic label indicating usage of mobile data traffic corresponding to the mobile data traffic information; Among the traffic labels, determine the initial resident location corresponding to the traffic label satisfying a first set traffic condition as a target resident location, the first set traffic condition indicating that a value of the mobile data traffic information corresponding to the traffic label is less than a first traffic threshold.

5. The method of claim 1, wherein, The determination of the recommendation time for recommending the services within the service recommendation range to the to-be-recommended object based on the call data and the mobile data traffic information includes: dividing at least one time period for the to-be-recommended object; For each time period, determine a traffic label of the mobile data traffic information in the time period and a call volume label corresponding to the call data in the time period, the call volume label indicating a duration of calls corresponding to the call data; For each time period, determine the recommendation time for recommending the services within the service recommendation range to the to-be-recommended object based on the traffic label and the call volume label corresponding to the time period.

6. The method of claim 5, wherein, The determination of the recommendation time for recommending the services within the service recommendation range to the to-be-recommended object based on the traffic label and the call volume label corresponding to the time period includes: In the time period, when the traffic label satisfies a second set traffic condition and the call volume label satisfies a set call volume condition, determine the time period as the recommendation time for recommending the services within the service recommendation range to the to-be-recommended object; The second set traffic condition indicates that a value of the mobile data traffic information corresponding to the traffic label is greater than a second traffic threshold, and the set call volume condition indicates that a value of the call data corresponding to the call volume label is greater than a first call threshold and less than a second call threshold.

7. The method of claim 1, wherein, The determination of the location data of the to-be-recommended object includes: obtain original location data corresponding to the to-be-recommended object in the historical period, the original location data including a latitude and a longitude where the to-be-recommended object is located and a residence time when the to-be-recommended object is located at the latitude and the longitude; remove invalid data points in the original location data to obtain the location data of the to-be-recommended object, the invalid data points including data points with abnormal latitude, longitude or positioning time.

8. A service recommendation apparatus characterized by comprising: include: a first determination module configured to determine location data, mobile data traffic information and call data of a to-be-recommended object, the location data, the mobile data traffic information and the call data including data related to the to-be-recommended object in a historical period; a second determination module configured to determine a target resident location corresponding to the to-be-recommended object based on the location data and the mobile data traffic information, and construct a service recommendation range corresponding to the target resident location; a third determination module configured to determine a recommendation time for recommending services within the service recommendation range to the to-be-recommended object based on the call data and the mobile data traffic information, and recommend the services within the service recommendation range to the to-be-recommended object at the recommendation time.

9. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the business recommendation method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to implement the business recommendation method according to any one of claims 1-7 when executed.

11. A computer program product, characterised in that, The computer program product comprises a computer program which, when executed by a processor, implements the business recommendation method according to any one of claims 1-7.

Images