31results about How to "Improve recommendations" patented technology

The invention relates to a multimedia resource display method and device, equipment and a storage medium, and the method comprises the steps: responding to a continuous triggering instruction of interaction controls corresponding to a plurality of playing pages, and displaying a resource recommendation identifier in a current playing page; the multimedia resources played by the plurality of playing pages are resources of the same target category; the current playing page is the last playing page in the plurality of playing pages; and in response to a trigger instruction for the resource recommendation identifier, displaying the recommended multimedia resources of the target category. According to the method and the device, the current playing page can be quickly skipped to the page corresponding to the recommended multimedia resource, and compared with a mode of obtaining the recommended multimedia resource only through searching, the diversity of the display form of the recommended multimedia resource is improved.

The invention relates to the technical field of code peculiar smell detection and reconstruction recommendation systems, in particular to an incremental code peculiar smell detection and reconstruction recommendation system and a method thereof.The incremental code peculiar smell detection and reconstruction recommendation system comprises a code extraction and static analysis module used for extracting a to-be-analyzed code snippet from a target project and preprocessing the code snippet to form a to-be-analyzed file; the runtime information acquisition and dynamic analysis module is used for operating the file to be analyzed in the sandbox environment, acquiring and summarizing runtime information and forming a dynamic analysis report; the learning module is used for receiving a static analysis result and a dynamic analysis report, comprehensively considering static information and an incremental model updating report, constructing vector representation of code potential features and updating an incremental learning model; and the reconstruction recommendation module is used for scoring each code snippet based on the code feature vector and an incremental learning model, recommending a corresponding reconstruction rule, and providing a more accurate peculiar smell detection result by fusing static code features and dynamic operation information.

The invention discloses a concept recommendation method and system under knowledge graph modal deficiency based on a large model, and belongs to the field of knowledge graph recommendation. According to the method, firstly, a large model is fully utilized to optimize construction of the knowledge graph, then, a richer knowledge graph is constructed through a feature complementation technology utilizing channel confidence to ensure integrity of concept features, and finally, in combination with an online learner model, knowledge states of learners are captured in real time, so that the knowledge states of the learners are captured in real time. Therefore, personalized recommendation of knowledge concepts in the online learning process is realized. According to the method, not only are the integrity and the dynamism of the knowledge graph improved, but also an effective solution is provided for the knowledge recommendation problem in online education.

The invention discloses a social recommendation method based on differential privacy. A target function is disturbed based on Laplacian differential privacy so as to ensure the safety of user data. Firstly, data collection and initialization of a user description matrix and an article description matrix are carried out; and secondly, traversing is carried out for each article and each user in each round, the user carries out data interaction with a recommendation server and other users, a predicted value of the score of each article by the user is finally calculated after multi-round iteration, and personalized recommendation is carried out on the user based on the predicted value. On one hand, the method can protect privacy data of a user, so that intermediate data and results can resist attacks of stealing, reasoning and the like carried out by hostile attackers. On the other hand, the influence of the social relation between the users and the similar preferences between the users on the preferences of the users is considered at the same time, and a better recommendation effect can be provided.

The invention provides a retrieval method and system for film and television extensive search, electronic equipment and a storage medium, and relates to the technical field of natural language processing, and the method comprises the following steps: determining a target field involved by a user intention by using a user interaction text; under the condition that the target field is the movie and television search field, extracting a semantic slot position of the user interaction text; under the condition that the semantic slot position is not empty and the semantic slot position is inconsistent with a retrieval slot position corresponding to a knowledge text of each movie and television resource in the information source movie and television knowledge base, or the semantic slot position is empty, inquiring movie and television resource vectors in a movie and television vector knowledge base by utilizing vector representation of a user interaction text, and carrying out semantic splicing on the movie and television resource vectors to obtain a movie and television resource vector library; and obtaining the film and television resource semantic information, and sending the film and television resource semantic information to the user terminal. According to the method, film and television extensive search can be realized under the condition that the intention of the user is indefinite, the accuracy of semantic information of film and television resources is improved, more accurate target film and television resources can be conveniently recommended to the user, and the user experience is improved.

The invention discloses a historical recall data filtering method and device based on driver click behaviors, and the method comprises the steps: obtaining the historical order record of a target driver, and the historical order record comprises the arrival click behavior data of the target driver arriving at a loading and unloading position; carrying out clustering on the arrival click behavior data; determining an outlier based on a clustering result, the outlier representing arrival at a click behavior abnormal point location; and obtaining a target outlier filtering rule, and carrying out filtering processing on the outlier according to the target outlier filtering rule. Clustering processing is performed on the click behavior data by using a clustering algorithm, so that the irregularity of the loading / unloading point group can be better adapted. And the clustered outliers are filtered through the outlier filtering rule, so that noise in the arrival click behavior data can be removed, the accuracy of loading / unloading points is improved, subsequent recommendation, order charging and other services are facilitated, and the user experience can be improved.

The invention relates to an optimization method and device of a fine arrangement model, a storage medium and electronic equipment. The method comprises the following steps: acquiring user behavior data, marking an object clicked by a target user in the user behavior data as a positive sample, and marking an object which is not clicked as a negative sample; obtaining a model prediction probability, an exposure frequency and a sample feature of each negative sample, and calculating a dynamic weight of each negative sample; inputting all browsing objects and object information thereof in the user behavior data into a fine arrangement model to perform recommendation probability prediction on all the browsing objects to obtain a prediction result, and calculating to obtain a basic loss function according to the prediction result and click information of all the browsing objects; and performing weighted calculation on the basic loss function according to the dynamic weight of each negative sample to obtain a weighted loss function, and iteratively updating parameters of the fine arrangement model according to the weighted loss function. The technical problems that an existing fine ranking model does not make full use of difficult-to-load samples, and the judgment and ranking precision is limited are solved.

The present invention discloses an item recommendation method and device, and relates to the field of computer technology. A specific implementation of the method includes: determining an item candidate set of the current user based on historical behavior data of several users; wherein the item candidate set includes several candidate items; for each candidate item: determining several similar items corresponding to the candidate item; generating a recall item candidate set of the current user based on each candidate item and its corresponding similar items; determining several target items based on the recall item candidate set and several preset items to be recommended; and recommending the several target items to the current user. This implementation can effectively recommend the items to be recommended that the user is interested in to the user, and the recommendation effect is good.

The present invention relates to the technical field of online marketing, and discloses a marketing method for online sales, comprising: obtaining user's favorite products, dividing a day into multiple time periods, and calculating the user's historical average browsing time in each time period; judging in real time whether the user has opened a shopping software, and if the user has opened it, obtaining the current time period and calculating the total number and total time of the user's browsed products to determine the browsing time of a single product; recommending a specific number of products from a set of intended products according to the user's browsing time and average browsing time of a single product in the current time period; obtaining the user's browsing status of the recommended products in real time, and adjusting subsequent recommended products according to the user's browsing feedback.

The invention relates to a commodity recall combination method and device, equipment and a medium in the technical field of e-commerce, and the method comprises the steps: determining a plurality of recall sources associated with a recommendation scene and recommendation effect scores of the recall sources for various recommendation scenes corresponding to an independent station shop, adding the recall source with the highest score to a target recall combination, adding the remaining recall sources to a candidate recall set, starting iteration, combining each recall source in the set with a target recall combination, determining the recommendation value improvement degree of each correspondingly obtained candidate recall combination compared with the target recall combination, and if the highest recommendation value improvement degree reaches the standard, returning to the candidate recall set; rejecting a combined recall source in a candidate recall combination corresponding to the improvement degree from the set, updating the candidate recall combination into a target recall combination, and continuing iteration; if the highest recommendation value improvement degree does not reach the standard, iteration is ended, and a current target recall combination is obtained. According to the method, the recall combination with the highest recommendation value can be fully mined for the scene.

The present invention discloses a service recommendation method based on graph differential equations and twin learning, comprising the following steps: inputting historical service interaction data of users; reading and processing historical service interaction data of users; using linear spatiotemporal embedding and embedding-based self-attention module to standardize and generate adaptive embedding; building a model through graph differential equations, and using the attention mechanism of time graph sequence and perceptual interval to learn dynamic interest transfer; twin learning and service recommendation. The above technical scheme firstly solves the graph differential equation of user interaction behavior, explicitly utilizes the continuous change of user interest, and then uses the attention mechanism of time graph sequence and perceptual interval to learn dynamic interest transfer. Finally, twin learning is used in the recommendation process, and learning is directly from the feature representation of the basic truth label. By comparing with positive samples, the probability of future service use is inferred to provide users with more accurate service recommendations.

This application is intended to provide a user recommendation method and device. In comparison to the prior art, in this application, regional activity record information about a target user is determined according to connection record information (S11) about the target user establishing a connection to a wireless access point, a matching user corresponding to the target user is determined based on overlap of activity regions of different users, and then the matching user is provided to the target user (S12). According to this application, a user is recommended based on connection record information about the user establishing a connection to a wireless access point, to help the user develop a related social relationship chain, thereby improving user experience. Further, in this application, the matching user corresponding to the target user is determined according to the regional activity record information about the target user and in combination with a plurality of user matching rules. According to this application, users are classified according to a plurality of flexible user matching rules, so that the users are more appropriately recommended, and user experience is optimized.

The invention relates to a content processing method and device, electronic equipment and a storage medium. The method comprises the steps that to-be-recommended content for a target object is acquired, the to-be-recommended content comprises associated content and target content, and display of the associated content is used for guiding display of the target content; and taking the object information of the target object and the content information of the to-be-recommended content as input, and outputting a corresponding target prediction result by utilizing a target prediction model, so as to recommend the to-be-recommended content based on the target prediction result. According to the method, the adaptability of the target prediction model obtained by training to a specific scene can be improved, so that the content recommendation effect is facilitated. The embodiment of the invention can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation, intelligent entertainment and the like.

The invention discloses a recommendation method based on a parallel graph neural network in the technical field of intelligent recommendation, and the method comprises the steps: 1, distributing an initial embedding vector for each user, item and relation, and enabling the initial vector to be 64-dimensional; 2, inputting the user vector and the item vector in the step 1 into a lightweight graph convolution module to obtain the user vector and the item vector; and 3, inputting the user and item vectors output in the step 2 and the initialized relation and item vectors in the step 1 into an attention and graph convolutional network module to obtain the user and item vectors of the stage. And 4, repeating the steps 2 and 3 for L times to obtain a plurality of user and item vectors, and respectively weighting and combining the user and item vectors to obtain the user and item vectors of the stage. And 5, calculating the item and the user vector obtained in the step 4 to obtain the item and the user vector of the final stage. And 6, performing dot product operation on the user and item vectors obtained in the step 5 to obtain an interaction probability of the user to the item, and returning a recommendation result. The scheme is used for improving the recommendation quality and optimizing the user experience.

The present invention discloses a method and device for processing requests, and relates to the field of computer technology. A specific implementation of the method includes: constructing a multi-tuple set based on the behavior data of each user on each item; wherein the multi-tuple set includes multiple multi-tuples, each multi-tuple includes a user and at least one item; training a neural network based on the multi-tuple set to obtain vectors of each user and vectors of each item; receiving and parsing a recommendation request to obtain a target user and a target item set; based on the vector of the target user and the vector of each target item in the target item set, screening out at least one item to be recommended from each item, and returning the at least one item to be recommended. This implementation can solve the technical problem that the possibility of hitting user needs is not high.

The present invention discloses an App advertisement delivery method and system, including: obtaining App user data and social data supplementing the App user data; extracting user social feature vectors; extracting user information feature vectors and content feature vectors; obtaining user feature vectors; based on the extracted user feature vectors, calculating the similarity between App users and social users, selecting a preset number of social users with high similarity to each App user, and supplementing the corresponding data to the App user; using historical advertisement information clicked by App users as supplementary information of App users and calculating the matching degree between App users and candidate advertisements, and recommending a preset number of advertisements with high matching degrees to App users; the present invention has the advantage of achieving an ideal recommendation effect in a scenario where user data and social information are lacking.

The invention relates to the technical field of data processing, and particularly provides an asset level determination method, electronic equipment and a computer readable storage medium, the asset level determination method comprises the following steps: obtaining customer label data, the customer label data comprising attribute information, asset information and investment behavior information of a target customer; determining a wallet share of the target customer based on the asset information; and determining the asset level of the target customer based on the attribute information, the asset information, the investment behavior information and the wallet share. In this way, the accuracy of asset level determination can be improved.

The embodiment of the invention discloses a point location recommendation method and device, electronic equipment and a computer readable storage medium. The method comprises the steps that a travel request of a user is received; in response to the condition that a riding point in the travel request is a highway section point location, recalling a point location recommendation list, the point location recommendation list comprising at least one candidate point location, the candidate point location being a highway section point location or a non-highway section point location, candidate point locations of expressway section point locations in the point location recommendation list are determined based on historical get-off point locations of expressway sections; and the point location recommendation list is transmitted to the user, so that the point location recommendation effect can be optimized, and the driver and passenger experience is improved.

The embodiment of the invention provides a model updating method and a recommendation method, and the model updating method comprises the steps: responding to a model updating request, and determining a sample data set and an initial recommendation model; dividing the sample data set according to a reference domain division rule and an auxiliary domain division rule to obtain a reference domain data set and an auxiliary domain data set; performing fine tuning on the initial recommendation model according to the reference domain data set and the auxiliary domain data set, and determining a reference domain adaptation parameter and an auxiliary domain adaptation parameter according to a model fine tuning result; and updating the initial recommendation model based on the reference domain adaptation parameter and the auxiliary domain adaptation parameter to obtain a target recommendation model corresponding to the model updating request.

The invention belongs to the technical field of oil-gas exploration and development, and particularly relates to a drill bit selection scheme recommendation method based on mechanical specific energy. According to the method, a database is created, drilling data of a well under construction is collected, mechanical specific energy and a drill bit benefit index are calculated in real time, the trend of the mechanical specific energy and stratum compressive strength is analyzed, and when the change rate of the mechanical specific energy is larger than the change rate of the stratum compressive strength and away from the trend, drill bit evaluation is conducted in combination with the drill bit benefit index. Whether a drill bit needs to be replaced or not is considered, the optimal mechanical specific energy of historical well drilling parameter data of the same well type, the same block and the same stratum in a database is analyzed, the cost per meter of the drill bit is combined, corresponding historical optimal drilling scheme data is selected as a recommended drill bit scheme, and then construction well operation is optimized according to the recommended drill bit scheme. Therefore, the purposes of speed increasing and efficiency increasing of well drilling production are achieved.

An article recommendation method, a computer device and a computer readable storage medium, the method comprising: obtaining a plurality of evaluation texts; performing word segmentation processing on each evaluation text to obtain a corresponding sentence set; extracting sentiment evaluation words of each evaluation text using a preset sentiment dictionary to obtain a corresponding sentiment evaluation word set; obtaining a corresponding sentiment evaluation unit set based on a preset evaluation object dictionary, a preset adverb dictionary and the sentence set and the sentiment evaluation word set of each evaluation text; calculating an evaluation score of the evaluation text for the article recorded by the evaluation text based on the sentiment evaluation unit set of the evaluation text, and obtaining an evaluation value of each article by aggregating the evaluation scores of each article; and selecting at least one article from the articles according to the evaluation value of each article to recommend to a target user. The present application constructs sentiment evaluation units based on evaluation texts, and then obtains the evaluation score of the evaluation text for the article to recommend the article, so that the article recommendation effect is better.

This invention provides a method, apparatus, device, and storage medium for recommending technologies that link supply and demand. The method includes: obtaining technical information from technology suppliers through a patent trading platform; constructing a technology library based on the technical information, which includes patent application texts, application dates, and IPC classification numbers; obtaining demand information from technology demanders through the patent trading platform and classifying the demands, with each demand classification associated with the IPC classification number; filtering technical information in the technology library according to set conversion criteria to obtain technologies to be converted; classifying the technologies to be converted based on the IPC classification number to generate a list of technologies to be converted, and pushing this list to the technology demanders corresponding to the demand categories associated with the IPC classification numbers. This invention can break down information barriers between patent technology suppliers and demanders, enabling targeted recommendations of patent technologies, improving patent transfer efficiency, and meeting the needs of both parties.

The application provides a method for analyzing user level intention from the space-time angle in conversation recommendation, and the training steps include an intention perception step, a time intention decoupling step, a feature transformation step, an item prediction step and a cross-scale contrast learning step. Compared with traditional conversation recommendation models, the application innovatively introduces the concept of hierarchical intention, uses a time intention decoupling module to decouple long-term intention and short-term intention from the original conversation, thereby avoiding mutual interference in the learning process. Secondly, in order to utilize the compatibility of spatial dimension intention, HearInt also proposes a cross-scale contrast learning loss calculation method, which is beneficial to the model to integrate large-scale intention information into small-scale intention when perceiving intention, thereby enriching the information amount of small-scale intention. The application not only enriches the information amount of the current conversation representation, but also fully excavates the user intention which can achieve the best recommendation effect in the conversation recommendation reference.

This application discloses a content recommendation method, apparatus, storage medium, and device, belonging to the field of artificial intelligence technology. It includes: for the t-th time step of the current recommendation round, acquiring user data of the target user to be recommended; generating state data for the t-th time step based on the first t-1 recommended content generated in the previous t-1 time steps and the user data acquired at the t-th time step; determining the recommended content for the t-th time step from the candidate content set based on the state data of the t-th time step; repeatedly executing the steps of generating recommended content at each time step until N recommended content items are obtained; the reward value corresponding to any recommended content item is related to the recommended content item and other recommended content items ranked after it; and outputting a list of recommended content items to the target user, where the N recommended content items in the list are arranged in order. This application can recommend a list that maximizes the overall reward, achieving the expected recommendation effect.

The application discloses a kind of interest point recommendation methods based on cross-city knowledge migration, first, three kinds of graph structures are constructed for source city and target city respectively, include: user-interest interaction graph, user-user association graph, interest point-interest point association graph, wherein user-interest point interaction graph is as main view, the rest two are as auxiliary view;And utilize multilayer graph neural network on each view carries out information propagation and feature learning;Second, design cross-view contrast learning task, maximize the consistency of the representation of the same entity object in the main view and the corresponding auxiliary view, enhance the discriminability and robustness of the model under the condition of data sparsity;Then, a double adapter transfer architecture is constructed, a two-stage training strategy is used.The method of the application can effectively alleviate the data sparsity problem of the target city, and provides strong support for improving the interest point recommendation effect in data scarce cities.

The application discloses a commodity list file generation method, device and system. The method is used for inferring the certainty degree of a customer to a commodity by searching the number of times of the customer to the commodity in a historical time, and inferring the interest degree of the customer to other commodities based on the certainty degree. Finally, the commodity list file of the customer is generated by setting a priority, so that the server can recommend commodities to the customer according to the commodity list file. The device and the system are used for executing the commodity list file generation method. The application is mainly used in the technical field of e-commerce.

The invention relates to the technical field of costume design online advertising, and discloses a costume design online advertising service platform based on user feedback, which comprises the following steps: connecting display videos of all presale costumes end to end to obtain a costume advertisement; if the user pauses when watching the clothing advertisement, recording the display video at the pause position as a marked video; the display adding strategy is executed, a picture set is displayed at the pause position of the user, and the picture set comprises a plurality of presale clothing display pictures; calculating the average playing speed of the clothing advertisement watched by each user; executing a multi-speed constant value strategy, predicting the probability of purchasing pre-sold clothes when the user watches the clothes advertisement at the average playing multi-speed, and recording the probability as a prediction probability; executing the matched display strategy, and displaying the matched video of the presale clothing; the average playing speed of the clothing advertisement watched by the user is calculated, the probability that the user purchases the presale clothing is predicted, clothing merchants are helped to determine the sales amount of the presale clothing, the insight of the market is increased, and the marketing effect is improved.

A neural network collaborative filtering recommendation method based on federated learning does not need to centrally collect users' personal data and upload it to a trusted central server for storage and model training. It only performs data training locally, and then optimizes multiple local models in coordination with the central server, and then aggregates the intermediate parameters of multiple local models to obtain a globally optimal model on the server side, and sends it to each terminal. In the local participant training, this method is different from traditional methods based on clustering, nearest neighbor, and matrix decomposition. It converts the data set into an implicit feedback data set at a certain ratio, constructs a neural collaborative filtering general framework NCF, inputs one-hot encoded user and item vectors, outputs prediction results, and performs differential privacy noise on the uploaded updated weights. This method effectively improves the recommendation effect while ensuring the privacy security of the data model.