User presence behavior prediction method and device

Abstract

The embodiment of the invention provides a user presence behavior prediction method and device, and the method comprises the steps: calculating the behavior similarity of a target user and sampling users according to application access data and behavior track data, and determining at least one sampling user as a similar user of the target user; predicting on-site behaviors of the similar users in the target time period; according to the behavior similarity, carrying out weight distribution on the similar users, and according to the distributed prediction weight, calculating prediction scores of the similar users of which the on-site behaviors obtained by prediction belong to the same behavior category; and determining a predicted presence behavior of the target user in the target time period based on the prediction score.

Description

[0001] This patent application is a divisional application of the Chinese patent application with application number CN202111327935.7, application date November 10, 2021, and invention name “User presence behavior prediction method and device”. Technical Field

[0002] This document relates to the field of data processing technology, and in particular to a method and device for predicting user presence behavior. Background Art

[0003] With the rapid development of mobile Internet and the rapid growth of Internet users, more and more information transmission and advertising rely on various mobile Internet applications. Users can access applications to obtain corresponding data information. In this process, a huge amount of data with user time and space attributes is generated. Usually, massive user data can be used for data processing to explore potential data patterns and provide users with better services. Summary of the Invention

[0004] One or more embodiments of this specification provide a method for predicting user presence behavior, including: calculating the behavioral similarity between a target user and sampled users based on application access data and behavioral trajectory data, and determining at least one sampled user as a similar user of the target user. Predicting the presence behavior of the similar users in a target time period. Assigning weights to the similar users according to the behavioral similarity, and calculating prediction scores for similar users whose presence behaviors belong to the same behavioral category based on the assigned prediction weights. Determining the predicted presence behavior of the target user in the target time period based on the prediction score.

[0005] One or more embodiments of the present specification provide a user behavior prediction device, comprising: a similarity calculation module, configured to calculate the behavioral similarity between a target user and a sampled user based on application access data and behavioral trajectory data, and determine at least one sampled user as a similar user of the target user. A presence behavior prediction module, configured to predict the presence behavior of the similar users in a target time period. A prediction score calculation module, configured to assign weights to the similar users according to the behavioral similarity, and calculate the prediction scores of similar users whose presence behaviors belong to the same behavior category based on the assigned prediction weights. A presence behavior determination module, configured to determine the predicted presence behavior of the target user in the target time period based on the prediction score.

[0006] One or more embodiments of the present specification provide a user behavior prediction device, comprising: a processor; and a memory configured to store computer-executable instructions, wherein when the computer-executable instructions are executed, the processor is caused to: calculate the behavioral similarity between a target user and a sampled user based on application access data and behavioral trajectory data, and determine at least one sampled user as a similar user of the target user. Predict the presence behavior of the similar users in a target time period. Assign weights to the similar users according to the behavioral similarity, and calculate the predicted scores of similar users whose presence behaviors belong to the same behavior category based on the assigned prediction weights. Determine the predicted presence behavior of the target user in the target time period based on the prediction score.

[0007] One or more embodiments of this specification provide a storage medium for storing computer-executable instructions, which implement the following process when executed by a processor: Calculate the behavioral similarity between a target user and a sampled user based on application access data and behavioral trajectory data, and determine at least one sampled user as a similar user of the target user. Predict the presence behavior of the similar users in a target time period. Assign weights to the similar users according to the behavioral similarity, and calculate the predicted scores of similar users whose presence behaviors belong to the same behavioral category based on the assigned prediction weights. Determine the predicted presence behavior of the target user in the target time period based on the prediction score. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate one or more embodiments of this specification or technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments described in this specification. Those skilled in the art can derive other drawings based on these drawings without inventive effort. Figure 1 A flowchart of a method for predicting user presence behavior provided in one or more embodiments of this specification; Figure 2 A flowchart of a method for predicting user presence behavior in a service push scenario provided by one or more embodiments of this specification; Figure 3 A schematic diagram of a user behavior prediction device provided in one or more embodiments of this specification; Figure 4 A schematic diagram of the structure of a user behavior prediction device provided in one or more embodiments of this specification. DETAILED DESCRIPTION

[0009] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below in conjunction with the drawings in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this document.

[0010] This specification provides an embodiment of a method for predicting user presence behavior: Reference Figure 1 , which shows a process flow chart of a method for predicting user presence behavior provided by this embodiment, referring to Figure 2 , which shows a processing flow chart of a user presence behavior prediction method applied to a service push scenario provided by this embodiment.

[0011] Reference Figure 1 The user presence behavior prediction method provided in this embodiment specifically includes steps S102 to S108.

[0012] Step S102 : calculating the behavioral similarity between the target user and the sampled users based on the application access data and the behavioral trajectory data, and determining at least one sampled user as a similar user to the target user.

[0013] In the user presence behavior prediction method provided by this embodiment, the presence behavior of the target user in the target time period is first predicted. If the prediction result obtained is unpredictable, the predicted presence behavior of the target user is determined with the help of the predicted presence behaviors of similar users in the target time period. Specifically, in the process of predicting the presence behavior of similar users in the target time period, a weight is assigned to each similar user according to the behavioral similarity, and the prediction score of similar users whose predicted presence behaviors belong to the same behavior category is calculated. The predicted presence behavior of the target user in the target time period is further determined based on the prediction score. If the predicted presence behavior is in the presence state, a service message is pushed to the target user in the target time period.

[0014] By introducing a joint processing mechanism that predicts the presence behavior of the target user in the target time period and uses the presence behavior prediction of similar users in the target time period to complete the presence behavior prediction of the target user, high precision and high reliability of the presence behavior prediction of the target user can be achieved, and the confidence and timeliness of the prediction results can be improved.

[0015] In actual document service push scenarios, users can perform real-name registration by uploading document information such as identity credentials. However, the operation process of uploading document information is often cumbersome, and users cannot complete the task of uploading document information without carrying their documents. Since users are in the target location (such as residence) and generally have more free time, in this case, if it can be predicted that the user is in the target location or target route, service messages can be pushed to the user in a targeted manner, avoiding waste of data resources, increasing the probability of users triggering service information, and improving user conversion efficiency.

[0016] In this embodiment, the application access data and the behavior trajectory data are generated based on historical access records. For example, when a user accesses an application at the (x, y) position at time T and uploads the geographical location information of the location, the behavior similarity indicates the degree of similarity between the on-site behaviors of two users. In actual applications, users can upload their own geographical location information when accessing an application. Here, the users who access the application and upload the geographical location information are historical users. A certain number of users are extracted from the historical users as samples for behavior similarity calculation. The certain number of users are sampled users, and the target users are users for whom on-site behavior prediction for the target time period is required.

[0017] The presence behavior includes the presence state or the absence state.

[0018] During service processing, presence refers to whether the user's location satisfies the requirements for the service. Conversely, if the user's location does not meet these requirements, the user's presence is considered absent. For example, during service message push, if the user frequently accesses the app while at home, the message push is effective. Therefore, presence includes the user's location being at home. Furthermore, the user frequently accesses the app in certain locations (such as restaurants or on public transportation on the way home from get off work). Therefore, presence also includes the user's location being at a location or route where the app access frequency satisfies the access requirements.

[0019] In specific implementations, to improve the accuracy of the prediction results, the behavioral similarity between the target user and the sampled users can be calculated during the process of determining the target user's predicted presence behavior based on the predicted presence behavior of similar users during the target time period. In an optional implementation provided by this embodiment, during the process of calculating the behavioral similarity between the target user and the sampled users, the following operations are performed: Extracting the sampled users from historical users' application access data and behavior trajectory data; constructing a behavior feature vector based on the application access data and behavior trajectory data of the target user, and constructing a behavior feature vector based on the application access data and behavior trajectory data of the sampled user; The behavior feature vector of the target user and the behavior feature vector of the sampled user are input into a similarity algorithm to calculate the behavior similarity, and the behavior similarity between the target user and the sampled user is output.

[0020] For example, in the process of determining whether to send real-name registration to user u, whether to send a service message push to user u can be determined by judging whether user u is in the target field during the time period of 19:00-22:00. In this case, the behavior feature vector constructed based on the application access data and behavior trajectory data of user u can be expressed as: "Ua-Features={f1, f2, f3, ..., fn}", and the behavior feature vector constructed based on the application access data and behavior trajectory data of the sampled users of user u can be expressed as: "Ubx-Features={fx1, fx2, fx3, ..., fxn}, where x is the user number of each sampled user, x={1b, 2b, 3b, ..., nb}", and the obtained behavior feature vector of user u and the behavior feature vector of the sampled user are input into the similarity algorithm, and the behavior similarity between user u and the sampled user can be output.

[0021] The similarity algorithm can be calculated by calculating the distance between the feature vectors of the target user and similar users. If the distance is small, the similarity is high; if the distance is large, the similarity is low. It can also be measured by measuring the difference in the direction of the two feature vectors to measure the similarity between the target user and similar users. Specifically, in the process of similarity calculation, the following three candidate similarity algorithms are provided: (1) Euclidean distance For example, the constructed behavioral feature vector of user u is: Ua-Features={f1, f2, f3, ..., fn}, and the constructed behavioral feature vector of any sampled user is: Ubx-Features={fx1, fx2, fx3, ..., fxn}, x={1b, 2b, 3b, ..., nb}, then the Euclidean distance, that is, the behavioral similarity between user u and any sampled user, is: .

[0022] (2) Manhattan Distance For example, the constructed behavioral feature vector of user u is: Ua-Features={f1, f2, f3, ..., fn}, and the constructed behavioral feature vector of any sampled user is: Ubx-Features={fx1, fx2, fx3, ..., fxn}. Then the Manhattan distance, that is, the behavioral similarity between user u and any sampled user, is: .

[0023] (3) Cosine distance For example, the constructed behavioral feature vector of user u is: Ua-Features={f1, f2, f3, ..., fn}, and the constructed behavioral feature vector of any sampled user is: Ubx-Features={fx1, fx2, fx3, ..., fxn}. Then the cosine distance, that is, the behavioral similarity between user u and any sampled user, is: .

[0024] In addition, in addition to the above-mentioned implementation of using Euclidean distance, Manhattan distance, and cosine distance to calculate similarity, other similarity algorithms may also be used, which are not specifically limited in this embodiment.

[0025] Since the target user's presence behavior is divided into a presence state and an absence state, in order to promote the accurate generation of the prediction results and make the predicted presence behavior of the target user closer to the actual presence behavior, which is helpful for the subsequent targeted push of service messages, in the process of constructing the behavior feature vector based on the application access data and behavior trajectory data of the sampled users, the sampled users can be classified into positive sample users and negative sample users. The positive sample users are the presence sample users, and the negative sample users are the absence sample users.

[0026] In an optional implementation provided by this embodiment, in the process of constructing a behavior feature vector based on the application access data and behavior trajectory data of the sampled user, the following operations are performed: Extracting positive sample users and negative sample users from the application access data and behavior trajectory data of the sampled users; Extracting time feature elements and presence behavior elements through the application access data and behavior trajectory data of the positive sample user and the application access data and behavior trajectory data of the negative sample user; A behavior feature vector of the sampled user is constructed according to the time feature element and the presence behavior element.

[0027] The time feature element includes at least one of the following: weekdays, weekends, holidays, time periods, date ranges, and frequencies; and the presence behavior element includes presence or absence.

[0028] It should be noted that, based on the extraction of time feature elements and presence behavior feature elements, the time feature elements are used to generate time behavior features, and the presence behavior elements are used to generate presence behavior features, and then a time feature vector is constructed through the time behavior features, and a presence behavior feature vector is constructed through the presence behavior features, and then the time feature vector and the presence behavior feature vector are combined to form a behavior feature vector; it is also possible to use time feature elements to generate time behavior features, and use presence behavior elements to generate presence behavior features, and then combine the time behavior features and the presence behavior features to form user behavior features, and quantize the user behavior features to generate a behavior feature vector; it is also possible to first combine the time feature elements and the presence behavior elements to form user behavior elements, and then use the user behavior elements to generate user behavior features, and then construct a behavior feature vector through the user behavior features.

[0029] For example, the user behavior feature formed by integrating the time behavior feature and the presence behavior feature may be: a sampled user is present at 21:00 on Monday, and a sampled user is absent at 12:00 on Saturday.

[0030] Similarly, in the above process of constructing a behavior feature vector based on the target user's application access data and behavior trajectory data, time feature elements and presence behavior elements can be extracted from the target user's application access data and behavior trajectory data, and then the target user's behavior feature vector can be constructed based on the time feature elements and presence behavior elements.

[0031] It should be added that, in addition to extracting time feature elements and presence behavior elements through the application access data and behavior trajectory data of positive sample users and the application access data and behavior trajectory data of negative sample users, it is also possible to extract either time feature elements or presence behavior elements through the application access data and behavior trajectory data of positive sample users and the application access data and behavior trajectory data of negative sample users.

[0032] After the behavioral similarity calculation is completed, similar users to the target user may be further determined. Specifically, in an optional implementation provided by this embodiment, in the process of determining at least one sampled user as a similar user to the target user, the following operations are performed: Selecting a sampled user from the sampled users that matches the geographic location of the target user; The sampled users obtained by screening are sorted in descending order of the behavior similarity, and the sampled users whose sorting positions are before a preset position are determined as the similar users.

[0033] Continuing with the above example, based on the behavioral similarity calculation using the behavioral feature vector of user u: "Ua-Features={f1, f2, f3, ..., fn}" and the behavioral feature vector of user u's sampled users: "Ubx-Features={fx1, fx2, fx3, ..., fxn}", similar users to the target user can be determined based on behavioral similarity. In the process of determining similar users, sampled users that match the city location of user u are screened out from the sampled users, and sampled users that do not meet the city location conditions are eliminated. Then, the sampled users obtained by screening are sorted in descending order according to the calculated behavioral similarity, and the sampled users whose sorting position is before the preset position are determined as similar users, expressed as: "U-similarity={u1, u2, ..., un}", so as to increase the reliability of user data and achieve efficient prediction process.

[0034] Furthermore, to save prediction time and improve the timeliness of prediction results, allowing users to receive service notification push notifications in a shorter period of time, before predicting the presence behavior of similar users in the target time period, the target user's presence behavior in the target time period can be predicted based on the target user's application access data and behavior trajectory data. The prediction results include presence status, absence status, or null, where null means that the target user's presence behavior in the target time period cannot be predicted.

[0035] In an optional implementation provided by this embodiment, after calculating the behavioral similarity between the target user and historical users based on the application access data and the behavioral trajectory data, and determining at least one historical user as a similar user of the target user, the following operations are performed: Predicting the target user's presence behavior during the target time period; Determining whether the prediction result of the target user's presence behavior in the target time period is empty; If it is not empty, the prediction result is used as the predicted presence behavior of the target user in the target time period; If it is empty, execute step S104.

[0036] To improve the accuracy and reliability of the prediction results for the target user's presence behavior during the target time period, the following three specific implementation methods are provided: In a first optional implementation provided by this embodiment, during the process of predicting the presence behavior of the target user in the target time period, the following operations are performed: Reading the target user's last visited location information from the target user's application access data and behavior trajectory data, and calculating the distance between the access location in the last visited location information and the target field; A prediction result of the target user's presence behavior in the target time period is determined based on the calculated distance.

[0037] The last visited location information includes the last visited location information, the last two visited location information, and the like.

[0038] Specifically, in the process of determining the predicted result of the presence behavior of the target user in the target time period based on the calculated distance, if the calculated distance is lower than the preset distance threshold, it is determined that the predicted presence behavior of the similar user in the target time period is in the presence state; if the calculated distance is higher than the preset distance threshold, it is determined that the predicted presence behavior of the similar user in the target time period is in the non-presence state; if the prediction result cannot be obtained through calculation, it is determined that the predicted presence behavior of the similar user in the target time period is empty, and the operation of determining whether the prediction result obtained is empty is continued.

[0039] For example, from user A's application access data and behavior trajectory data, the last access location information read from the time period of 9:00-10:00 includes an access location of a supermarket near the target venue of user A. The distance between the supermarket and the target venue is calculated to be 10m. If the calculated distance (10m) is less than the preset distance threshold (20m), then the predicted presence behavior of user A in the time period of 9:00-10:00 is determined to be in the presence state, and the prediction result is not empty.

[0040] In a second optional implementation provided by this embodiment, during the process of predicting the presence behavior of the target user in the target time period, the following operations are performed: Obtaining the target user's historical access time and historical location information through the target user's application access data and behavior trajectory data; Based on the historical access time and the historical location information, the target user is counted in the valid time period in which the target user is in the presence state, and the effective frequency of the target user being in the presence state in each time period in the valid time period is calculated; A prediction result of the target user's presence behavior in the target time period is determined based on the effective frequency.

[0041] For example, the historical access time and historical location information of user B in the past month are obtained through the application access data and behavior trajectory data of user B. The effective time periods of user B's presence are counted as 12:00-13:30, 19:00-22:00, and 23:00-8:30 the next day. The effective frequency of the presence in the time period of 12:00-13:30 is calculated to be 10, the effective frequency of the presence in the time period of 19:00-22:00 is calculated to be 50, and the effective frequency of the presence in the time period of 23:00-8:30 the next day is calculated to be 20. Through comparison, it is determined that the predicted presence behavior of user B in the time period of 12:00-13:30 is not present, and the prediction result is not empty.

[0042] It should be added that in addition to obtaining the target user's historical access time and historical location information through the target user's application access data and behavior trajectory data, one of the historical access time and historical location information can also be obtained through the target user's application access data and behavior trajectory data.

[0043] In a third optional implementation provided by this embodiment, during the process of predicting the presence behavior of the target user in the target time period, the following operations are performed: Classifying the target users into corresponding group characteristic categories based on the user characteristics of the target users; A prediction result of the target user's presence behavior in the target time period is determined according to the presence behavior characteristics corresponding to the group characteristic category.

[0044] For example, the user characteristics of user c include a 25-year-old female programmer. Based on the user characteristics of user c, user c is divided into the corresponding group characteristic category. According to the presence behavior characteristics corresponding to the group characteristic category, no prediction result of user c's presence behavior in the time period of 13:00-16:00 is obtained, indicating that the prediction result is empty.

[0045] In addition, in addition to the three aforementioned methods for predicting the predicted presence behavior of a target user in a target time period, other prediction methods may be used to obtain prediction results using more accurate prediction methods and improve the confidence of the prediction results.

[0046] It should be added that the process of predicting the target user's presence behavior in the target time period can also be performed before the behavior similarity between the target user and the sampled users is calculated based on the application access data and the behavior trajectory data, and at least one sampled user is determined to be a similar user to the target user. The method for predicting user presence behavior includes: Predict the target user's presence behavior during the target time period; Determine whether the prediction result of the target user's presence behavior in the target time period is empty; If it is not empty, the prediction result is used as the predicted presence behavior of the target user in the target time period; If it is empty, the behavior similarity between the target user and the sampled users is calculated based on the application access data and behavior trajectory data, and at least one sampled user is determined to be a similar user to the target user; the presence behavior of similar users in the target time period is predicted; similar users are weighted according to the behavior similarity, and the predicted scores of similar users whose presence behaviors belong to the same behavior category are calculated based on the assigned prediction weights; the predicted presence behavior operation of the target user in the target time period is determined based on the prediction score.

[0047] Step S104: predicting the presence behavior of the similar users in the target time period.

[0048] In specific implementation, when predicting the presence behavior of similar users in the target time period, the following three implementation methods are provided: (1) Read the previous access location information of similar users from their application access data and behavior trajectory data, and calculate the distance between the access location in the previous access location information and the target field; The predicted results of the presence behavior of similar users in the target time period are determined based on the calculated distances.

[0049] Specifically, if the calculated distance is lower than the preset distance threshold, it is determined that the predicted presence behavior of the similar user in the target time period is in the presence state, which means that the prediction result is not empty; if the calculated distance is higher than the preset distance threshold, it is determined that the predicted presence behavior of the similar user in the target time period is in the absence state, which means that the prediction result is not empty; if the prediction result cannot be obtained through calculation, it is determined that the predicted presence behavior of the similar user in the target time period is empty.

[0050] (2) Obtain historical access time and historical location information of similar users through application access data and behavior trajectory data of similar users; Based on historical access time and historical location information, the effective time periods in which similar users were present are counted, and the effective frequency of being present in each time period within the effective time period is calculated; Determine the prediction results of the presence behavior of similar users in the target time period based on the effective frequency.

[0051] (3) Classify similar users into corresponding group characteristic categories based on their user characteristics; Based on the presence behavior characteristics corresponding to the group feature categories, the prediction results of the presence behavior of similar users in the target time period are determined.

[0052] In actual applications, when predicting the presence behavior of similar users in a target time period, in addition to the three implementation methods provided above, a prediction model can also be used to achieve high accuracy and high reliability of the prediction results. The specific operation process is as follows: The model input data of similar users is extracted from the application access data and behavior trajectory data of similar users, and the data is input into the behavior prediction model to predict the presence behavior of similar users in the target time period, and the prediction results of the presence behavior of similar users are obtained; wherein, the behavior prediction model is trained and generated based on positive sample user data and negative sample user data.

[0053] The model input data can be a standardized sequence obtained by converting application access data and behavior trajectory data, or a standardized sequence obtained by converting on-site behavior characteristics. The constructed behavior feature vector of similar users can also be directly input into the prediction model for prediction.

[0054] Furthermore, since the predicted results may contain null values, after predicting the presence behavior of similar users during the target time period, the predicted results for their presence behavior can be evaluated. If all predicted results are null, no processing is performed. This improves data processing efficiency, avoids waste of data resources, reduces the number of prediction steps, and promotes flexible execution of the prediction process. If the presence behavior of similar users during the target time period cannot be predicted (all predicted results are null), there is a high probability that the presence behavior of target users with similarities during the target time period cannot be predicted either. By testing whether the presence behavior of similar users is predictable, it is determined whether all predicted results are null.

[0055] In an optional implementation provided by this embodiment, after predicting the presence behavior of similar users in the target time period, the following operation is performed: determining whether the predicted results of the presence behavior of the similar users in the target time period are all empty; if so, determining that the predicted presence behavior of the target user in the target time period is empty; if not, executing step S106.

[0056] Step S106 , assigning weights to the similar users according to the behavior similarities, and calculating prediction scores of similar users whose behaviors on-site belong to the same behavior category according to the assigned prediction weights.

[0057] The above judgment determines whether the prediction results of the presence behaviors of similar users are all empty. On this basis, if the prediction results are not empty, similar users are weighted according to the behavioral similarity, and the prediction scores of similar users whose presence behaviors belong to the same behavior category are calculated based on the assigned prediction weights.

[0058] In an optional implementation provided by this embodiment, in the process of assigning weights to similar users according to behavioral similarity and calculating predicted scores for similar users whose behaviors in the same behavioral category are predicted based on the assigned prediction weights, the following operations are performed: weighting the similar users according to the behavior similarity, and removing similar users with empty prediction results from the similar users; Based on the prediction weights, the presence scores of similar users whose behavior categories after elimination are present are calculated, and the absence scores of similar users whose behavior categories after elimination are absent are calculated.

[0059] Optionally, the predicted weights are assigned in descending order according to the sorting order, and the weight differences between two adjacent predicted weights are equal. By assigning weights to the sorted similar users in descending order and with equal weight differences, and calculating the predicted scores for similar users in both the present and absent states, the prediction effect is improved. The presence behavior data of a large amount of similar users is used to compare the presence behavior of the target user, thereby improving the data's usability and exploring potential patterns in the massive data to enhance the user experience.

[0060] For example, the top 10 similar users of user u are U-similarity = {u1, u2, ..., u10}. Similar users are assigned to {u1, u2, ..., u10} -> {10, 9, ..., 1} in reverse order of 10 according to their behavioral similarity. The prediction results for the top 10 similar users are {present, present, present, present, present, present, absent, absent, absent, absent, null, null}. The two similar users with empty prediction results are eliminated, and the prediction results are expressed as {present, present, present, present, present, absent, absent, absent, absent}. According to the assigned prediction weights, the presence score of similar users in the present state is calculated as 10 + 9 + 8 + 7 + 6 = 40, and the absence score of similar users in the absent state is calculated as 5 + 4 + 3 = 12.

[0061] Step S108: determining the predicted presence behavior of the target user in the target time period based on the predicted score.

[0062] Based on the aforementioned weighting of similar users based on behavioral similarity and the calculation of predicted scores for similar users whose presence behaviors belong to the same behavioral category based on the assigned prediction weights, this embodiment determines the target user's predicted presence behavior during the target time period based on the predicted scores. The predicted scores include the aforementioned presence score and absence score, and the predicted presence behavior includes at least one of the following: presence state, absence state, or null.

[0063] In specific implementation, in the process of determining the predicted presence behavior of the target user in the target time period based on the predicted score, the following operations are performed: If the presence score is higher than the absence score, the presence status is used as the predicted presence behavior of the target user in the target time period; If the presence score is lower than the absence score, the absence state is determined to be the predicted presence behavior of the target user in the target time period.

[0064] Continuing with the previous example, based on the assigned prediction weights, the presence score of similar users in the present state is calculated as 10+9+8+7+6=40, and the absence score of similar users in the absent state is calculated as 5+4+3=12. Since the presence score of 40 is greater than the absence score of 12, user u's predicted presence behavior during the 19:00-22:00 time period is determined to be present.

[0065] During the specific implementation process, in order to push service messages to the target users in a targeted manner based on the predicted presence behavior of the target users, reduce the failure rate of service message push, increase the probability of users triggering service efficiency, and thus increase the user conversion rate and the operational efficiency of the service provider, service messages can be pushed to the target users based on the presence status as the predicted presence behavior of the target users in the target time period.

[0066] In an optional implementation provided by this embodiment, after the presence status is used as the predicted presence behavior of the target user in the target time period, the following operations are performed: Push a service message to the target user in the target time period according to the service information of the service to be processed.

[0067] Continuing with the above example, if user u's predicted presence behavior during the 19:00-22:00 time period is determined to be present, a service message push regarding real-name registration can be sent to user u during the 19:00-22:00 time period, so that user u can complete the task of uploading identity credentials and other document information by triggering the service message, thereby avoiding the invalidity of the service message push.

[0068] The following uses the application of a user presence behavior prediction method provided by this embodiment in a service push scenario as an example to further illustrate the user presence behavior prediction method provided by this embodiment. Figure 2 ,The user presence behavior prediction method applied to the service ,push scenario specifically includes the following steps.

[0069] Step S202: extracting sample users from historical user application access data and behavior trajectory data.

[0070] Step S204 : constructing a behavior feature vector based on the target user's application access data and behavior trajectory data, and constructing a behavior feature vector based on the sample user's application access data and behavior trajectory data.

[0071] In this process, positive sample users and negative sample users are extracted from the application access data and behavior trajectory data of the sampled users, and time feature elements and presence behavior elements are extracted through the application access data and behavior trajectory data of the positive sample users and the application access data and behavior trajectory data of the negative sample users. The behavior feature vector of the sampled users is constructed based on the time feature elements and presence behavior elements. In addition, time feature elements and presence behavior elements are extracted from the application access data and behavior trajectory data of the target users, and the behavior feature vector of the target user is constructed based on the time feature elements and presence behavior elements.

[0072] Step S206 : Input the target user's behavior feature vector and the sample user's behavior feature vector into a similarity algorithm to calculate behavior similarity, and output the behavior similarity between the target user and the sample user.

[0073] Step S208: Filter the sampled users whose geographical locations match the target user from the sampled users.

[0074] Step S210 , sorting the sampled users obtained by screening in descending order of behavior similarity, and determining the sampled users whose sorting positions are before a preset position as similar users.

[0075] Step S212 : reading the target user's last visited location information from the target user's application access data and behavior trajectory data, and calculating the distance between the access location in the last visited location information and the target field.

[0076] Step S214: determining a prediction result of the target user's presence behavior in the target time period based on the calculated distance.

[0077] Step S216, determining whether the prediction result of the target user's presence behavior in the target time period is unpredictable; If not, the prediction result is used as the target user's predicted presence behavior in the target time period; If so, execute steps S218 to S222.

[0078] Step S218: classify similar users into corresponding group feature categories based on their user features.

[0079] Step S220 , determining a prediction result of the presence behavior of similar users in a target time period based on the presence behavior characteristics corresponding to the group characteristic categories.

[0080] Step S222, determining whether the prediction results of the presence behaviors of similar users are all unpredictable; If so, it is determined that the predicted presence behavior of the target user in the target time period is unpredictable; If not, execute steps S224 to S230.

[0081] In step S224 , weights are assigned to similar users according to their behavior similarities, and similar users whose prediction results are unpredictable are removed from the similar users.

[0082] Step S226 , calculating the presence scores of similar users whose behavior categories after elimination are present, and calculating the absence scores of similar users whose behavior categories after elimination are absent, based on the predicted weights.

[0083] Among them, the weight of the prediction weight decreases in descending order according to the sorting order, and the weight difference between two adjacent prediction weights is equal.

[0084] Step S228: If the presence score is higher than the absence score, the presence status is used as the predicted presence behavior of the target user in the target time period.

[0085] Step S230: Push a service message of quantified carbon saving indicators to the target user in the target time period according to the carbon saving service information.

[0086] In summary, the user presence behavior prediction method provided by this embodiment first extracts sample users from the application access data and behavior trajectory data of historical users, constructs a behavior feature vector based on the application access data and behavior trajectory data of the target user, and constructs a behavior feature vector based on the application access data and behavior trajectory data of the sampled user. Then, the behavior feature vector of the target user and the behavior feature vector of the sampled user are input into a similarity algorithm to calculate behavior similarity, and the behavior similarity between the target user and the sampled user is output. Then, sample users whose geographic locations match the target user are screened from the sampled users, and the screened sampled users are sorted in descending order of behavior similarity, and sampled users whose sorting positions are before a preset position are determined as similar users. Secondly, the target user's presence behavior in the target time period is predicted, and then it is determined whether the prediction result obtained is empty. If not, the prediction result is used as the predicted presence behavior of the target user in the target time period. If it is empty, the presence behavior of similar users in the target time period is predicted, and then it is determined whether the prediction results of the presence behavior of similar users are all empty. If so, it is determined that the predicted presence behavior of the target user in the target time period is empty. If not, similar users are weighted according to their behavioral similarity, and similar users with empty prediction results are eliminated from the similar users. According to the prediction weights, the presence scores of similar users whose behavior category after elimination is present are calculated, and the non-presence scores of similar users whose behavior category after elimination is non-present are calculated; Finally, if the presence score is higher than the absence score, the presence status will be used as the predicted presence behavior of the target user in the target time period, and then the service message will be pushed to the target user in the target time period based on the service information of the pending service. In this way, the accuracy and reliability of the user's presence behavior prediction will be improved, the confidence and timeliness of the prediction results will be improved, and targeted service information will be pushed to the user, avoiding the ineffectiveness of service message push and the waste of data resources. At the same time, the probability of users triggering service information will be increased, thereby improving the user's conversion efficiency.

[0087] An embodiment of a user presence behavior prediction device provided in this specification is as follows: In the above embodiment, a user presence behavior prediction method is provided, and correspondingly, a user presence behavior prediction device is also provided, which will be described below with reference to the accompanying drawings.

[0088] Reference Figure 3 , which shows a schematic diagram of a user presence behavior prediction device provided by this embodiment.

[0089] Since the device embodiment corresponds to the method embodiment, the description is relatively simple. For the relevant parts, please refer to the corresponding description of the method embodiment provided above. The device embodiment described below is only illustrative.

[0090] This embodiment provides a user presence behavior prediction device, including: A similarity calculation module 302 is configured to calculate the behavioral similarity between the target user and the sampled users based on the application access data and the behavioral trajectory data, and determine at least one sampled user as a similar user of the target user; The presence behavior prediction module 304 is configured to predict the presence behavior of the similar users in the target time period; The prediction score calculation module 306 is configured to assign weights to the similar users according to the behavior similarity, and calculate the prediction scores of similar users whose behaviors on-site belong to the same behavior category based on the assigned prediction weights; The presence behavior determination module 308 is configured to determine the predicted presence behavior of the target user in the target time period based on the prediction score.

[0091] An embodiment of a user presence behavior prediction device provided in this specification is as follows: Corresponding to the user presence behavior prediction method described above, based on the same technical concept, one or more embodiments of this specification also provide a user presence behavior prediction device, which is used to execute the user presence behavior prediction method provided above. Figure 4 A schematic diagram of the structure of a user presence behavior prediction device provided in one or more embodiments of this specification.

[0092] This embodiment provides a user presence behavior prediction device, including: like Figure 4 As shown, the user presence behavior prediction device can vary significantly due to different configurations and performance. It may include one or more processors 401 and memory 402. Memory 402 may store one or more applications or data. Memory 402 may be either ephemeral or persistent. The applications stored in memory 402 may include one or more modules (not shown), each of which may include a series of computer-executable instructions for the user presence behavior prediction device. Furthermore, processor 401 may be configured to communicate with memory 402 to execute the series of computer-executable instructions in memory 402 on the user presence behavior prediction device. The user presence behavior prediction device may also include one or more power supplies 403, one or more wired or wireless network interfaces 404, one or more input / output interfaces 405, one or more keyboards 406, and the like.

[0093] In a specific embodiment, a user presence behavior prediction device includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the user presence behavior prediction device, and the one or more programs are configured to be executed by one or more processors, including computer-executable instructions for performing the following: Calculate the behavioral similarity between the target user and the sampled users based on the application access data and the behavioral trajectory data, and determine at least one sampled user as a similar user of the target user; Predicting the presence behavior of the similar users in a target time period; Assigning weights to the similar users according to the behavioral similarities, and calculating predicted scores of similar users whose behaviors on-site belong to the same behavioral category based on the assigned prediction weights; A predicted presence behavior of the target user in the target time period is determined based on the predicted score.

[0094] An embodiment of a storage medium provided in this specification is as follows: Corresponding to the user presence behavior prediction method described above, based on the same technical concept, one or more embodiments of this specification further provide a storage medium.

[0095] The storage medium provided in this embodiment is used to store computer-executable instructions. When the computer-executable instructions are executed by a processor, the following process is implemented: Calculate the behavioral similarity between the target user and the sampled users based on the application access data and the behavioral trajectory data, and determine at least one sampled user as a similar user of the target user; Predicting the presence behavior of the similar users in a target time period; Assigning weights to the similar users according to the behavioral similarities, and calculating predicted scores of similar users whose behaviors on-site belong to the same behavioral category based on the assigned prediction weights; A predicted presence behavior of the target user in the target time period is determined based on the predicted score.

[0096] It should be noted that the embodiment of the storage medium in this specification and the embodiment of the user presence behavior prediction method in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned corresponding method, and the repeated parts will not be repeated.

[0097] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0098] In the 1930s, technological improvements could be clearly distinguished as either hardware improvements (for example, improvements to circuit structures like diodes, transistors, and switches) or software improvements (improvements to process flows). However, with the advancement of technology, many process flow improvements today can be considered direct improvements to hardware circuit structures. Designers almost always create the corresponding hardware circuit structure by programming the improved process flow into the hardware circuit. Therefore, it cannot be said that a process flow improvement cannot be implemented using physical hardware modules. For example, a programmable logic device (PLD), such as a field programmable gate array (FPGA), is an integrated circuit whose logical function is determined by user programming. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to hire a chip manufacturer to design and manufacture a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly performed using software called a "logic compiler." This is similar to the software compilers used during program development. Before compilation, the original code must be written in a specific programming language, called a Hardware Description Language (HDL). There are many types of HDL, including ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art will also understand that simply by programming a method flow in one of these hardware description languages and then programming it into an integrated circuit, a hardware circuit that implements the logic method flow can be easily obtained.

[0099] The controller can be implemented in any suitable manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the memory control logic. Those skilled in the art will also appreciate that, in addition to implementing the controller purely in computer-readable program code, the controller can also be implemented in the form of logic gates, switches, an application-specific integrated circuit, a programmable logic controller, an embedded microcontroller, etc. by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the means for implementing the various functions included therein can also be considered as structures within the hardware component. Alternatively, the means for implementing the various functions can be considered both a software module implementing the method and a structure within the hardware component.

[0100] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0101] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0102] Those skilled in the art will appreciate that one or more embodiments of this specification may be provided as a method, system, or computer program product. Thus, one or more embodiments of this specification may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0103] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable user behavior prediction device to produce a machine, so that the instructions executed by the processor of the computer or other programmable user behavior prediction device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0104] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable user behavior prediction device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0105] These computer program instructions can also be loaded onto a computer or other programmable user behavior prediction device so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0106] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0107] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0108] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0109] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0110] One or more embodiments of this specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. One or more embodiments of this specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0111] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0112] The foregoing description is merely an example of the present invention and is not intended to limit the present invention. Persons skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be included within the scope of the claims herein.

Claims

1. A method for predicting user presence behavior, comprising: Calculate the behavioral similarity between the target user and the sampled users based on the application access data and the behavioral trajectory data, and determine similar users to the target user; Predicting the presence behavior of the similar users in a target time period to obtain the presence behavior; Assigning weights to the similar users according to their behavior similarity, and calculating prediction scores for the similar users whose behaviors belong to the same behavior category based on the assigned prediction weights; A predicted presence behavior of the target user in the target time period is determined based on the predicted score.

2. The user presence behavior prediction method according to claim 1, wherein the step of calculating the behavior similarity between the target user and the sampled users based on the application access data and the behavior trajectory data comprises: Extracting the sampled users from historical users' application access data and behavior trajectory data; constructing a behavior feature vector based on the application access data and behavior trajectory data of the target user, and constructing a behavior feature vector based on the application access data and behavior trajectory data of the sampled user; The behavior feature vector of the target user and the behavior feature vector of the sampled user are input into a similarity algorithm to calculate the behavior similarity, and the behavior similarity between the target user and the sampled user is output.

3. The user presence behavior prediction method according to claim 2, wherein constructing a behavior feature vector based on the application access data and behavior trajectory data of the sampled users comprises: Extracting positive sample users and negative sample users from the application access data and behavior trajectory data of the sampled users; Extracting time feature elements and / or presence behavior elements through the application access data and behavior trajectory data of the positive sample user and the application access data and behavior trajectory data of the negative sample user; A behavior feature vector of the sampled user is constructed according to the time feature element and / or the presence behavior element.

4. The user presence behavior prediction method according to claim 1, wherein determining similar users of the target user comprises: Selecting a sampled user from the sampled users that matches the geographic location of the target user; The sampled users obtained by screening are sorted in descending order of behavior similarity, and the sampled users whose sorting positions are before a preset position are determined as the similar users.

5. The user presence behavior prediction method according to claim 1, after the step of calculating the behavioral similarity between the target user and the sampled users based on the application access data and the behavioral trajectory data and determining similar users of the target user is performed, and before the step of predicting the presence behavior of the similar users in the target time period to obtain the presence behavior is performed, further comprising: Predicting the target user's presence behavior during the target time period; Determining whether the prediction result of the target user's presence behavior in the target time period is empty; If it is not empty, the prediction result of the target user's presence behavior in the target time period is used as the predicted presence behavior of the target user in the target time period; If it is empty, the step of predicting the presence behavior of the similar users in the target time period to obtain the presence behavior is executed.

6. The user presence behavior prediction method according to claim 5, wherein the step of predicting the presence behavior of the target user during the target time period comprises: Reading the target user's last visited location information from the target user's application access data and behavior trajectory data, and calculating the distance between the access location in the last visited location information and the target field; Determining a prediction result of the target user's presence behavior in the target time period based on the calculated distance; or, Obtaining the target user's historical access time and / or historical location information through the target user's application access data and behavior trajectory data; Counting the effective time periods during which the target user is in the presence state based on the historical access time and / or the historical location information, and calculating the effective frequency of the target user being in the presence state in each time period within the effective time periods; Determining a prediction result of the target user's presence behavior in the target time period based on the effective frequency; or, Classifying the target users into corresponding group characteristic categories based on the user characteristics of the target users; A prediction result of the target user's presence behavior in the target time period is determined according to the presence behavior characteristics corresponding to the group characteristic category.

7. The user presence behavior prediction method according to claim 1, further comprising: If the prediction results of the presence behaviors of the similar users in the target time period are all empty, it is determined that the predicted presence behavior of the target user in the target time period is empty.

8. The user presence behavior prediction method according to claim 7, wherein the weights of the prediction weights decrease in descending order according to the sorting order, and the weight differences between two adjacent prediction weights are equal.

9. The method for predicting user presence behavior according to claim 7, wherein weights are assigned to the similar users according to their behavior similarities, and prediction scores are calculated for the similar users whose presence behaviors belong to the same behavior category based on the assigned prediction weights, including: Weights are assigned to the similar users according to their behavior similarity, and similar users with empty prediction results are removed from the similar users; Based on the prediction weights, the presence scores of similar users whose behavior categories after elimination are present are calculated, and the absence scores of similar users whose behavior categories after elimination are absent are calculated.

10. The user presence behavior prediction method according to claim 9, wherein determining the predicted presence behavior of the target user in the target time period based on the prediction score comprises: If the presence score is higher than the absence score, the presence status is used as the predicted presence behavior of the target user in the target time period; If the presence score is lower than the absence score, the absence state is determined to be the predicted presence behavior of the target user in the target time period.

11. The method for predicting user presence behavior according to claim 10, further comprising: Push a service message to the target user in the target time period according to the service information of the service to be processed.

12. The user presence behavior prediction method according to claim 11, wherein the presence status includes at least one of the following: the user location is at a residence, the user location is at a place or route where the application access frequency meets the access condition.

13. A user behavior prediction device, comprising: A similarity calculation module is configured to calculate the behavioral similarity between the target user and the sampled users based on the application access data and the behavioral trajectory data, and determine similar users to the target user; A presence behavior prediction module is configured to predict the presence behavior of the similar users in a target time period to obtain the presence behavior; a prediction score calculation module configured to assign weights to the similar users according to their behavior similarities, and to calculate prediction scores for the similar users whose behaviors belong to the same behavior category based on the assigned prediction weights; The presence behavior determination module is configured to determine the predicted presence behavior of the target user in the target time period based on the prediction score.

14. A user behavior prediction device, comprising: processor; and a memory configured to store computer-executable instructions that, when executed, cause the processor to: Calculate the behavioral similarity between the target user and the sampled users based on the application access data and the behavioral trajectory data, and determine similar users to the target user; Predicting the presence behavior of the similar users in a target time period to obtain the presence behavior; Assigning weights to the similar users according to their behavior similarity, and calculating prediction scores for the similar users whose behaviors belong to the same behavior category based on the assigned prediction weights; A predicted presence behavior of the target user in the target time period is determined based on the predicted score.

15. A storage medium for storing computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the following process: Calculate the behavioral similarity between the target user and the sampled users based on the application access data and the behavioral trajectory data, and determine similar users to the target user; Predicting the presence behavior of the similar users in a target time period to obtain the presence behavior; Assigning weights to the similar users according to their behavior similarity, and calculating prediction scores for the similar users whose behaviors belong to the same behavior category based on the assigned prediction weights; A predicted presence behavior of the target user in the target time period is determined based on the predicted score.

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