51 results about "Retention rate" patented technology

Systems and methods for providing item recommendations to increase retention rates of customers are disclosed. In some embodiments, a disclosed method includes: receiving, from a computing device, a recommendation request for recommending items to a customer; determining, based on the recommendation request, an anchor item to be displayed to the customer via a user interface; determining relevance scores between the anchor item and a plurality of candidate items; determining retention scores between the anchor item and the plurality of candidate items; generating, using at least one machine learning model, a ranked list of recommended items based on the plurality of candidate items, the relevance scores and the retention scores; and transmitting to the computing device the ranked list of recommended items to be displayed to the customer with the anchor item on the user interface.

The invention discloses a service strategy determination method and device and electronic equipment, and relates to the artificial intelligence technology, and the method comprises the steps: obtaining the credit information of a target user, the credit information at least comprises personal basic information, credit transaction information, financial institution interaction information and credit score information; respectively inputting the personal basic information, the credit transaction information, the financial institution interaction information and the credit scoring information into at least two evaluation models, and obtaining evaluation results respectively output by each evaluation model; and determining a target service strategy of the target user according to the at least two evaluation results. According to the method and the device, the loss condition of the user can be determined through the at least two evaluation results, so that the user retention rate can be improved by implementing the corresponding service strategy, and the beneficial effects of providing personalized services for different users, improving the user experience and reducing the user loss rate are achieved.

The invention relates to the technical field of computers, and discloses a user behavior response method and device, a storage medium and computer equipment, and the method can be applied to high-interaction-frequency business scenes such as insurance, financial management and credit loan, and comprises the following steps: responding to an operation behavior of a user for a target business; the method comprises the following steps: generating a user operation identifier, collecting user behavior characteristic data, determining a decision buffer period based on the user behavior characteristic data, then obtaining a behavior response strategy corresponding to a behavior response stage in the decision buffer period, and sequentially pushing a plurality of behavior response strategies to a user terminal. And obtaining an operation decision result of the user for the target business after the decision buffer period is ended. According to the method, the user behavior feature data is collected, the decision buffer period is constructed, intelligent pushing of the staged response strategy is combined, accurate recognition and timely intervention of the user behavior intention are achieved before the user executes key operation, and the user retention rate is increased.

The invention provides a deduction resource exchange method and device, electronic equipment, a medium and a program product, which can be used in the field of data processing. The method comprises the following steps: determining a first deduction resource required by a commodity, a first account for purchasing the commodity and a first identification level where the first deduction resource is located; determining an identity identifier of a user associated with the first account according to the account identifier of the first account, and determining each second deduction resource held by the user in the first identifier level according to the identity identifier; determining an exchange proportion of exchanging each second deduction resource into the first deduction resource, and determining a first proportion in each exchange proportion, the first proportion being used for indicating an exchange proportion less than a preset threshold value; and exchanging the second deduction resource corresponding to the first proportion into the first deduction resource. According to the method and the device, the effect of improving the user retention rate by reducing and avoiding resources is improved.

The invention discloses a daily active user number prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: responding to a prediction request for the daily active user number of a target product on the Nth day after a preset reference day, determining an active user retention number of the initial active user group on the preset reference day after N days of attenuation, a daily newly-added user number from the preset reference day to the Nth day and a retention rate of the daily newly-added user on the Nth day; determining influence coefficients of the external influence factors on the retention number of active users, the number of newly-added users every day and the retention rate of the newly-added users every day in the Nth day; according to the respective corresponding influence coefficients, respectively adjusting the retention number of active users, the number of newly-added users every day and the retention rate of the newly-added users every day on the Nth day; and estimating the daily active user number of the Nth day according to the adjusted active user retention number, the daily new user number and the retention rate of the daily new user in the Nth day. By adopting the scheme of the invention, the accuracy of predicting the daily active user number can be improved.

The invention discloses a website performance optimization method and related products thereof. The method comprises the following steps: creating performance baseline data; performing performance optimization on each website core scene related to the performance data, and outputting a performance test report and an operation information material; determining the performance change condition of the website based on the performance baseline data, the performance test report and the operation information material; and determining whether to end the performance optimization cycle based on the performance change condition. By utilizing the technical scheme provided by the invention, the performance of the website can be optimized, and the overall user experience is improved, so that the user retention rate is improved, and the e-commerce conversion rate is improved.

The invention relates to the technical field of computers, and discloses a member strategy processing method, system and device and a storage medium. The method comprises the following steps: collecting target information of a user, wherein the target information comprises historical payment behavior data, historical use behavior data and fund change data of a platform account; based on the historical payment behavior data and fund change data of a platform account, obtaining a payment capability level of the user; and based on the historical use behavior data, obtaining the loss risk level of the user, generating a member maintenance strategy for the user based on the payment capability level and the loss risk level of the user; and based on the member maintenance strategy, executing a fee deduction operation of subscribing a member service on a platform account of the user. By adopting the method, the accurate personalized member maintenance strategy can be customized for the user, and the user experience and the member retention rate are improved.

The invention relates to the technical field of artificial intelligence, and discloses a text data preprocessing method suitable for a large financial model, and the method comprises the steps: receiving an original financial text sequence sorted according to timestamps; performing word segmentation and part-of-speech tagging; dynamically calculating an emotional fluctuation index of each text unit based on the sliding time window; self-adaptively setting an emotion noise judgment threshold value and marking candidate noise units; semantic stability is evaluated through embedding space cosine similarity, and noise is confirmed for the second time; after the confirmation noise is removed, anaphora resolution and logic connection reconstruction are executed to repair context coherence; and outputting the pure text sequence. The system comprises corresponding function modules. According to the method, through a dynamic threshold and semantic dual verification mechanism, the effective information retention rate and the noise elimination accuracy rate are remarkably improved in a high-noise scene, and the quality and the processing throughput of financial large model input data are guaranteed.

The invention belongs to the technical field of internet information recommendation, and discloses a collaborative filtering recommendation method and device based on user grading, and the method comprises the following steps: S1, user grading processing: obtaining user data of a target platform, grading users based on a preset grading dimension, obtaining at least two user levels and a level weight corresponding to each user level; s2, data preprocessing and feature extraction: collecting user behavior data and content data, performing data cleaning, constructing a user-content interaction matrix, and extracting user features and content features; according to the scheme, the user level weights are deeply fused into similarity calculation and recommendation priority ranking, so that the preferences of high-value users and payment users can be remarkably amplified, and therefore, a more accurate high-quality recommendation result which better conforms to the identities and requirements of the users is obtained, and the satisfaction degree, retention rate and commercial conversion efficiency of core users are effectively improved.

The invention relates to the technical field of computers, provides a cross-platform jump payment method and device, electronic equipment and a storage medium, and can be applied to the financial industry. According to the method, when the first use object enters the payment service, the account interface of the at least one bound payment account is presented, manual binding of the use object is not needed, the convenience of account binding operation is improved, and further, when the service platform to which one payment account selected by the use object belongs is jumped to for the first time, the user experience is improved. By presenting the authorized login interface associated with the selected payment account, the user does not need to manually input login information for identity verification, the input operation of first cross-platform jump is reduced, the convenience and response speed of the first cross-platform jump are improved, the service experience of the user is effectively improved, and the user experience is improved. And the retention rate and the competitiveness of the third-party service platform are improved.

The present application provides a short play user behavior analysis method and system, relating to the technical field of data processing, the method comprising: step 1, obtaining the dynamic behavior data of the user on the short play platform through multi-dimensional data, including real-time watching behavior data and advertisement interaction data, and establishing a user behavior time sequence database; step 2, based on the user behavior time sequence database, extracting three fixed detection points, constructing a user behavior feature topology relationship with the three fixed detection points as the vertex, and analyzing the user behavior feature topology relationship to generate a behavior feature adjustment value; step 3, based on the behavior feature adjustment value, dynamically identifying the user's preference in entertainment, information, socialization and emotional needs, while constructing a psychological resistance evaluation mechanism to quantify the user's psychological resistance to advertisements, and dynamically updating the resistance level label. The present application understands the user's preference and watching habit, helps the short play platform to adjust the content creation, and improves the user retention rate and the platform business value.

The invention discloses an interactive audience participation feedback method based on sentiment analysis and behavior prediction. User behavior data and text sentiment information are collected in real time through an audience terminal, behavior analysis and sentiment recognition are executed in parallel on a cloud server, and behavior tags and multi-level sentiment scores are generated; in combination with the historical portrait of the user, personalized interactive feedback content including an expression package, a knowledge point card, a microphone connection invitation or a voting entry is dynamically generated through a weighted scoring mechanism, and the pushing frequency is controlled to be not more than once every three minutes to avoid interference; meanwhile, when the individual continuously carries out negative feedback or the overall negative emotion ratio of the live broadcast room exceeds a threshold value, an instruction is automatically sent to the live broadcast stream pushing end, and real-time content adjustment is achieved. The method effectively solves the problems that traditional live broadcast interaction lags behind, is single in form and lacks emotional response, remarkably improves the audience immersion, participation and platform retention rate, and is suitable for various live broadcast scenes such as education, entertainment and e-commerce.

The invention discloses a user purchase willingness analysis method and system based on multi-source data. The method comprises the following steps: collecting and storing user behavior data, merchant behavior data and interactive language data in an e-commerce live broadcast process; constructing a BiGRU-ATT fusion model, training the BiGRU-ATT fusion model by using the collected interactive language data, and performing sentiment classification prediction on the newly generated interactive language data by the trained BiGRU-ATT fusion model to obtain a sentiment index EI; defining a user behavior sequence, calculating a behavior entropy weight and a retention rate of each stage in the user behavior sequence and an average time consumption ratio of the stage according to the collected user behavior data and merchant behavior data, and obtaining a behavior sequence index BI; and obtaining a fusion index CI indicating the purchase willingness of the user through the emotion index EI and the behavior sequence index BI. Therefore, the front emotion bullet screen can be guided, the overall atmosphere of the live broadcast room is improved, the user participation degree and satisfaction degree can be improved, and a new view angle is provided for live broadcast e-commerce marketing.

The application discloses a service providing method and device, which can be used in the financial field or other arbitrary fields, and the method comprises the following steps: determining whether a target user belongs to a preset target group; providing preset service for the target user in the case that the target user is determined to belong to the preset target group; determining whether the target user has a predetermined relationship with a user in the preset target group in the case that the target user is determined not to belong to the preset target group; and providing preset service for the target user in the case that the target user is determined to have the predetermined relationship with the user in the preset target group. According to the scheme, the user in the non-pre-set target group who has the preset relationship with the user in the preset target group can also obtain the service of the preset target group, so that the application retention rate can be effectively improved.

The present invention discloses a promotion activity generation method, a computing device and a computer storage medium. The method includes: calculating a first user viscosity index value within a currently corresponding first time window; matching the first user viscosity index value with a second user viscosity index value within any second time window stored in a historical database, identifying a target second user viscosity index value that matches the first user viscosity index value; identifying a target second time window according to the target second user viscosity index value; and generating a currently corresponding promotion activity based on historical promotion activity information corresponding to the target second time window. This solution can realize the automatic generation of promotion activities, reduce the generation cost of promotion activities, and improve the generation efficiency of promotion activities. In addition, the promotion activities generated by this solution have a high degree of matching with the current actual situation, which is conducive to improving the user retention rate and user activity of the application platform, improving the user experience and avoiding the waste of promotion resources.

The invention relates to a recommendation method and system for a psychological self-service intervention scheme, and the method comprises the steps: screening out an alternative set from an intervention scheme library through a psychological test result, optimizing the alternative set in combination with a physiological classification label to form a target set, and pushing a high-score scheme according to the score and credibility of the scheme and the sorting of tendency types for new users. The psychological state of the user is accurately evaluated, personalized recommendation is provided, attraction to the new user is effectively enhanced, doubts of the new user to the platform are reduced, the credibility and the retention rate are improved, and the problem that the new user is difficult to attract by an existing psychological self-service technology is solved.

Embodiments of the present application disclose information content recommendation method and device, computer equipment and storage medium. The method comprises: setting the proportion value of the feature classification in the user portrait in the information recommendation, and setting the latest information proportion value; filtering information content meeting a first requirement by using the most important feature label in the user portrait, filtering information content meeting a second requirement by using the behavior data label in the user portrait; filtering information content meeting a third requirement by using the basic attribute data label in the user portrait; filtering information content meeting a fourth requirement according to the information publishing time; performing a merging operation to obtain an operation result; sorting the operation result; and sending the sorted result to a terminal. The method can improve the demand hit rate of users viewing information content, reduce the information cocoon house with smaller and smaller recommended information range, and improve the activity rate and retention rate of users.

The invention discloses an employee incentive and organization behavior optimization method in enterprise development, and particularly relates to the field of enterprise management, and the specific optimization steps are as follows: S1, early diagnosis and demand identification, S2, dynamic intervention implementation, S3, re-detection, and S4, effect verification and feedback optimization. According to the method, an organic and collaborative organization development intervention system is constructed by creatively integrating a reform leadership mode, a "world coffee" discussion method and incentive enhancement activities in workshops; under the system, the reform leadership style can formulate and implement a targeted incentive strategy based on accurate analysis of individual demands of the employees, so that the work satisfaction and organization commitment of the employees are greatly improved, and the problems of low incentive strategy matching degree, insufficient discussion achievement conversion rate, lack of data support and the like in the traditional technology are solved; the employee retention rate and the organization citizen behavior score are significantly improved, and the method is suitable for small and medium-sized enterprises to construct a high-performance management system.

The invention discloses an intelligent hierarchical management method and system based on customer full life cycle value evaluation, and relates to the field of big data processing, and the method comprises the steps: forming an initial data set through a multi-source heterogeneous data collection terminal and a preprocessing module, and extracting feature data in stages; a dynamic weight adjustment model is adopted to quantify the customer value of each stage, and a comprehensive score is calculated by combining value, stability and developability indexes; realizing customer clustering layering according to the grading threshold value by using an improved K-means algorithm; aiming at customer characteristics of different grades and stages, matching corresponding strategies in a personalized management strategy library; the strategy effect is evaluated through indexes such as the value improvement rate and the retention rate, analysis and optimization are carried out when the standard is not reached, and continuous monitoring is carried out after the standard is reached. The method has the advantages that through full-life-cycle multi-source data integration and dynamic value evaluation, accurate classification and personalized strategy matching of customers are achieved, closed-loop optimization is formed, the customer operation accuracy and value conversion are efficiently improved, and the management cost is reduced.

The invention relates to the technical field of big data and the technical field of artificial intelligence, and discloses a retention rate prediction method, device and equipment based on a large model, and the method comprises the steps: carrying out the feature extraction of an emotion description text corresponding to an emotion level, obtaining the emotion features of an IPTV account, carrying out the fusion of the behavior features of the IPTV account and the emotion features of the IPTV account, and carrying out the prediction of the retention rate of the IPTV account. The fusion features are utilized to retrieve similar cases in a vector knowledge base, and context information corresponding to the similar cases is obtained; the fusion features, the context information corresponding to the similar cases and the multiple time intervals are written into a prompt word template, a prompt text of the IPTV account is obtained, the prompt text of the IPTV account is processed through a large model, a prediction result of the IPTV account is obtained, and the retention rate of the IPTV account in each time interval is obtained from the prediction result of the IPTV account. The acquisition efficiency of the retention rate can be improved.

The present invention discloses a book search method and a computing device based on the connection between a plugin and a host application. The method includes: receiving a book search operation triggered by a user at the search entry of the plugin, and searching for a first book data entry matching the search keyword in the plugin database; if the first book data entry is empty, using the connection interface between the plugin and the host application to invoke the host application to search for book information search results matching the search keyword; encapsulating the book information search results in a preset format to obtain a second book data entry; and rendering and displaying the second book data entry on the search result page of the plugin. By the above method, the probability of plugin user loss can be reduced, which helps to improve the plugin user retention rate.

The embodiment of the invention provides a complaint order processing method and system and a computer readable medium, and can solve the technical problem of poor flexibility of work order processing in related technologies. The method comprises the steps of obtaining key information of a to-be-processed work order based on natural language processing; inputting the question type and the emergency degree result into a case supervised learning model to obtain a complaint processing result; based on the scene parameters, the key information and the complaint processing result, obtaining a resource configuration strategy according to a reinforcement learning algorithm; and obtaining a target processing result according to the state space variable, the action definition set, the reward function and the resource configuration strategy. The ever-increasing requirements for quick response and high-precision work order processing are met, and the limitation of related technologies in dealing with large-scale data sets and diversified problem types is solved, so that the flexibility of work order processing is improved. And the processing speed, the accuracy, the first solution rate and the like are remarkably improved. And moreover, the labor cost is saved, and the customer retention rate is improved.

The invention provides a personalized subscription page generation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring multi-dimensional behavior data generated when a user uses a target application program; extracting behavior characteristics corresponding to the user from the multi-dimensional behavior data; constructing a behavior portrait corresponding to the user according to the behavior characteristics; predicting a renewing willingness level corresponding to the user according to the behavior characteristics; and determining a corresponding renewal welfare allocation strategy according to the behavior portrait and the renewal willingness level, and generating a personalized subscription page according to the renewal welfare allocation strategy. According to the method and the device, the renewal welfare allocation strategy suitable for the user can be determined in combination with the user behavior portrait and the renewal willingness, and the personalized subscription page is generated according to the renewal welfare allocation strategy to stimulate the user to renew, so that the retention rate of the user is improved.

The invention provides a data processing method and device, and the method supports the inputting of a commodity screening demand of a natural language according to an actual live broadcast demand during the screening of a commodity in a live broadcast session, and obtains a commodity screening tag used for describing the commodity screening demand based on an intelligent analysis model. And searching and pushing a target commodity with the commodity screening label in a commodity library. The intelligent analysis model can understand the meaning of the natural language, so that the commodity screening demand of inputting the natural language is supported, the mode of inputting the commodity screening demand is free and not fixed, and an anchor or a merchant supporting a live broadcast session can express the commodity screening demand more conveniently. According to the method and the device, commodity screening based on multiple labels is supported, so that the input of commodity screening requirements with complex expression can be supported, the commodity screening efficiency and accuracy can be improved, the viscidity and retention rate of a user to a live broadcast platform, a live broadcast room or a live broadcast session in the live broadcast room can be improved, and the conversion rate of commodities can be improved.

The present invention discloses an online prediction method and device for the remaining life of a cable based on the retention rate of the correlation coefficient. The method includes: injecting incident signals of the spread spectrum time domain reflectometry method into the cable specimens before and after aging respectively, and collecting the reflected signals; calculating the correlation coefficients of the cable specimens before and after aging according to the incident signals and the reflected signals, wherein the cross-correlation between the incident signal and the noise signal is 0; calculating the actual value of the retention rate of the correlation coefficient according to the correlation coefficients; obtaining the undetermined parameters of the degradation model based on the least squares method according to the time of collecting the reflected signals and the actual values of the corresponding retention rates of the correlation coefficient; and online predicting the remaining life of the cable according to the actual values of the retention rates of the correlation coefficients of the obtained cable specimens, the determined degradation model and the determined cable specimen life termination point. The method provided by the present invention realizes the efficient and rapid online prediction of the remaining life of the cable without damaging the cable.

The invention discloses an advertisement design and delivery management method based on artificial intelligence, and belongs to the technical field of artificial intelligence. According to the method, through full-process closed loop of multi-dimensional quality vector initialization, real-time cognitive impedance calculation, joint attenuation model construction, composite reward function design and double-layer agent decision, dynamic optimization of advertisement originality and fine management of user experience are realized, and the intelligent level of advertisement putting and the user retention rate are remarkably improved; by collecting implicit behaviors of a user in real time and combining pre-estimation of cognitive load and novelty entropy, user granularity quantification of cognitive impedance intensity is achieved, an eye movement model and behavior data are fused, and whether psychological resistance is generated by the user due to too high creative complexity or not is accurately judged through a sight escape speed mean value and a page residence time fluctuation coefficient; a dynamic threshold mechanism ensures that impedance judgment adapts to different user groups, and misjudgment is avoided.

The invention discloses a user loss early warning method and system, and the method comprises the steps: obtaining a loss analysis period and a preset repurchase analysis interval, and determining a time node and a loss analysis interval according to the current time, the loss analysis period and the preset repurchase analysis interval; acquiring all flight ticket buying success records of the user in the loss analysis interval, and extracting a first type of flight ticket buying success records with the ticket buying time closest to the time node from all the flight ticket buying success records; and calculating the time difference between the ticket buying time of the ticket buying success record of the first type of flight and the time node, and taking the time difference as the time threshold of user loss of the user. According to the user loss early warning method and system, the user behavior is automatically analyzed, so that the accuracy of user loss early warning is improved. By automatically executing the retention strategy, manual intervention is reduced, the retention efficiency is improved, and the user churn rate is reduced, so that the user retention rate is increased.

The embodiment of the invention discloses an interface interaction method and device, electronic equipment and a computer storage medium. In the embodiment of the invention, a commodity interface of a target live broadcasting room is displayed, and the commodity interface comprises at least one commodity; in response to a purchase operation on a target commodity in the at least one commodity, displaying a prompt interface, the prompt interface being triggered and displayed when target attributes of target rights and interests of the target commodity include that an association relationship exists between a purchase object and a target anchor object and the association relationship does not exist between the purchase object and the target anchor object; and in response to a confirmation operation on the prompt interface, establishing an association relationship between the purchase object and the target anchor object, and displaying the target rights and interests of the target commodity in a purchase order of the target commodity, the purchase order being triggered and displayed in response to a purchase operation on the target commodity. According to the embodiment of the invention, the probability that the purchase object purchases the target commodity in the target live broadcast room can be improved, and the retention rate of the target anchor object on the platform is improved.

The invention provides a short play user behavior analysis method and system, and relates to the technical field of data processing, and the method comprises the steps: 1, obtaining dynamic behavior data, including real-time watching behavior data and advertisement interaction data, of a user on a short play platform through multi-dimensional data, and building a user behavior time sequence database; 2, extracting three fixed detection points based on the user behavior time sequence database, constructing a user behavior feature topological relation by taking the three fixed detection points as vertexes, and analyzing the user behavior feature topological relation to generate a behavior feature adjustment value; and step 3, dynamically identifying preferences of the user in entertainment, information, social contact and emotional demands based on the behavior feature adjustment value, constructing a psychological resistance evaluation mechanism, quantifying the psychological resistance degree of the user to the advertisement, and dynamically updating a resistance level label. According to the invention, preferences and watching habits of the user are insighted, a short episode platform is assisted to adjust content creation, and the user retention rate and the platform commercial value are improved.

The application relates to the technical field of artificial intelligence, and discloses a text data preprocessing method suitable for a financial large model, which comprises the following steps: receiving an original financial text sequence sorted by time stamp; performing word segmentation and part-of-speech tagging; dynamically calculating an emotional fluctuation index of each text unit based on a sliding time window; adaptively setting an emotional noise judgment threshold and marking a candidate noise unit; evaluating semantic stability through embedding space cosine similarity, and secondarily confirming noise; after the confirmed noise is removed, performing reference resolution and logical connection reconstruction to repair context coherence; and outputting a pure text sequence. The system comprises corresponding function modules. Through the dynamic threshold and semantic double verification mechanism, the application significantly improves the effective information retention rate and noise removal accuracy in a high-noise scene, and guarantees the quality and processing throughput of input data of the financial large model.