686 results about "Cold start" patented technology

A method and system allowing a mobile phone user to “opt in” to requests with multiple response options, to respond indicating that user wishes not to be tracked, or to respond indicating some information about state, possibly in addition to PVT information. The user has options besides allowing ping responses or turning off GPS tracking. When a ping is received, user chooses whether or not to opt in to the ping. The user may refuse to respond, to respond normally with or without PVT or other information, or to respond saying only that the mobile phone is turned on. GPS tracking continues to operate, so that upon return to normal responses, no cold start of GPS tracking is involved. More intelligent, such as for example rules-based, responses can be made to ping messages, in which GPS tracking interacts with a user interface to determine how to respond flexibly to pings.

In an Interactive Real-Time Distributed Navigation system a method and system is disclosed for implementing a warm start / cold start option. Through selection of the warm start option, an interactive session for providing navigational directions to a user is more quickly established because a user's position is assumed not to be ambiguous. A method of the invention verifies this assumption. Through selection of the cold start option, the method of the invention seeks to remove ambiguity in a user's position before providing navigational directions. If, however, a user's position is not ambiguous, the method of the invention reverts to a warm start condition to immediately transmit navigational directions to the user.

A telecommunications system and method for rapid start-up of a Global Position System (GPS) receiver integrated with a mobile terminal is disclosed. The location of a reference GPS receiver associated with a Base Transceiver Station (BTS) within the cell that the mobile terminal is currently located is used as the local position estimate for that mobile terminal. The GSM TDMA frame numbers are first correlated with the GPS clock signal. Optionally, this correlation is adjusted using the adaptive frame alignment mechanism. This local position estimate is used to ascertain the location of the GPS receiver. The correlation data is then sent to the GPS-capable mobile terminal through the Short Messaging Service (SMS) cell broadcast facility or the Broadcast Control Channel (BCCH) facility of the GSM network to enable the built-in GPS receiver to calculate its position relatively quickly.

The invention discloses an information recommending method based on a social network. The information recommending method includes the following steps that first, trust degree and similarity between users are calculated, and a user relation matrix is constructed through weighted values; second, the users are clustered through a community discovering algorithm, and then a closest neighbor set of the users is formed; third, scores are predicted, and a recommending list is generated. The information recommending method based on the social network can achieve the following advantages that first, the cold start problem is solved: trust degree is introduced into the method, if enough neighbors cannot be obtained according to the common grading articles in the recommending process, trustable friends can serve as the start point of prediction, and thus the cold start problem can be relieved, and user coverage can be improved; real time performance is improved: community division is performed on the user network through the community discovering algorithm commonly used in social network analysis, in other words, same user interests are clustered, and thus the time for finding the neighbor set of the users is greatly shortened, and the real time performance of the information recommending response is improved.

A method for start-up of a network, including a number of nodes, which are connected via channels. The nodes exchange information in the form of messages via the channels. The transition phase of a synchronizing node from its initial phase to a synchronized phase is separated in a first integration phase and a second subsequent cold-start phase. A synchronizing node in the integration phase listens to messages being sent from nodes in the synchronized phase and only reacts to an integration message (i-frame) if the integration message is a valid message. Furthermore, a synchronizing node, wherein integration of the synchronizing node to a set of already synchronized nodes was not successful after a specifiable period, changes into the cold-start phase, in which a cold-start procedure of the node is extracted, wherein in the cold-start phase the node does not react to integration messages of a node in the synchronized phase.

The invention discloses a personalized tourist attraction recommending method based on a knowledge domains map. The personalized tourist attraction recommending method based on the knowledge domains map comprises the following steps: constructing a tourist area knowledge domains map through a massive amount of data on the internet; coding information in the knowledge domains map through an improved TransE model; training attractions and user nodes into a n-dimensional vector (which is assumed to have n attributes) according to the number of link attributes; also showing the relation between users and attractions as an n-dimensional vector; after vector representation of the users and the attractions, calculating similarity of the users and similarity of the attractions; substituting the similarity into a prediction scoring formula to obtain two prediction scores; then normalizing difference between vectors calculated by f (h, r, t) to the range between scoring threshold values to obtain a third prediction score; and finally, carrying out weighted averaging on the three prediction scores to obtain a final scoring list which is used for recommending tourist attractions for the users.By the personalized tourist attraction recommending method based on the knowledge domains map, the problems of poor semanteme, low recommending accuracy and cold start in the prior art are solved, and the practicality is good.

The invention discloses an association-rule recommending method based on self-adaptive multiple minimum supports. The method comprises the following steps of firstly, establishing a commodity-classifying hierarchical tree according to commodity classification, and classifying concrete commodities according to the classifying hierarchical tree; next respectively carrying out minimum-support threshold-value setting on each concrete commodity and the upper-layer class of a concrete-commodity layer, and then mining frequent item sets and generating rules by utilizing a multiple-minimum-support association-rule expanding algorithm on the basis of the support threshold-value setting, wherein the threshold-value setting relates to the influences of time factors, concrete-commodity price factors and concrete-commodity brand factors; finally generating recommendation for each user by adopting a TOP-N recommending method. When personalized recommendation is made for the user by the association-rule recommending method, the characteristics of different objects can be better embodied by considering the influences of many factors on the multiple-minimum-support threshold-value setting for the concrete commodities and the classes; meanwhile, a data-sparsity problem and a cold-starting problem in a recommending system are relieved, so that the personalized recommendation can be more accurately made for the user.

The invention provides a commodity recommendation method. The method comprises steps: a user information vector corresponding to associated user information of each user in a target user set and a commodity information vector corresponding to associated commodity information of each commodity in a target commodity set are extracted; the commodity information vector and the user information vectorare inputted to a corresponding depth neural network model trained and obtained according to user historical behavior data for feature extraction; according to the extracted commodity feature vector and a user feature vector, the correlation coefficient between each user and each commodity is calculated; and according to all correlation coefficients associated with each user, a commodity is recommended for each user. Correspondingly, the invention also corresponding provides a commodity recommendation device, computer equipment and a computer readable storage medium. Through the technical scheme of the invention, the cold start problem in the existing recommendation scheme can be effectively solved, the feature extraction difficulties are effectively reduced, and the commodity recommendation efficiency and the commodity recommendation effects are thus improved.

The invention discloses a recommendation system with a hierarchical privacy protection function and an operation method on the basis of the recommendation system. The recommendation system comprises a client / browser, a database of the client / browser, a proxy server, a database of the proxy server, a plurality of recommendation servers and databases of the recommendation servers. Interfaces for exchanging data with the servers can be provided by the client / browser and the database of the client / browser, personal information and service requests which are inputted by users can be received, the personal information can be stored in the database of the client / browser, the service requests can be transmitted to the proxy server, and recommendation results fed by the recommendation servers can be displayed for the users; the service requests of the users can be received by the proxy server and the database of the proxy server, the proxy server and the database of the proxy server can respond to the service requests, the recommendation results of the recommendation servers can be received, and recommendation can be carried out for the users according to the recommendation results; project resources in the recommendation system can be stored and managed by the recommendation servers and the databases of the recommendation servers, and recommendation which meets the service requests of the users can be collaboratively generated according to score information of the users. The recommendation system and the operation method have the advantage that privacy of the users can be protected when the recommendation system is used in application scenes such as recommendation, query and small data cold start.

An embodiment of the invention provides an information recommendation method and apparatus and an electronic device, and relates to the field of telecommunication technology. The invention is designed to solve the problem of failing to accurately locate the real interest of a user. The method mainly includes the steps of starting a cold start page and displaying a first-level information tag; determining a first-level interest information tag in the first-level information tag according to a first selection instruction, and transmitting the first-level interest tag to a server; receiving and displaying a second-level information tag corresponding to the first-level interest information tag returned by the server, the second-level information tag being obtained by subdivision in the first-level interest tag by the server according to a preset rule; determining a second-level interest information tag in the second-level information tag according to a second selection instruction; and obtaining recommendation information including the second-level interest tag, and recommending the recommendation information including the second-level interest tag to the user. The invention is mainly applied to application programs in the process of starting a cold start page.

The invention discloses a personalized paper recommendation method and a system thereof. The method includes the following steps: step 1, finding out heterogeneous academic network data based on the behavioral characteristics of researchers writing academic papers in the filed of scientific research, and building a training dataset according to the heterogeneous academic network data, and training as per the training dataset to obtain a ranked study model; step 2, on-line building user configuration, generating candidate collected papers interested by users, generating the result of paper recommendation based on the ranked study model according to the candidate collected papers, generating paper recommendation as per a certain manner and returning the paper recommendation to a user based on the result of paper recommendation; step 3, on-line receiving user feedback, and updating the result of paper recommendation correspondingly as per different user feedback. The method and the system effectively solve the problem of cold start at the initial stage of the recommendation system, and guarantee the accuracy and recall rate of the recommendation result.

The invention discloses a social-label-based method for optimizing a personalized recommendation system. In the method, social label similarity and score similarity are adopted and applied to calculation of a user-and-project oriented K-nearest neighbor model, and then a user and a project of a K-nearest neighbor are used for calculating a prediction score of the project by the user at the same time. Because the label similarity and the score similarity are adopted in the method at the same time, so that the K-nearest neighbor calculation of the user and the project is more accurate, the recommendation accuracy is obviously higher than that obtained by singly adopting the score similarity, and a cold-start problem based on a label similarity model can be solved. Therefore, a data sparsityproblem can be solved by using a user-and-project oriented recommendation model, and the recommendation accuracy is also obviously higher than that of a conventional user-oriented recommendation model and a project-oriented recommendation model.

The invention relates to a collaborative filtering method for personalized recommendation fusion content and behavior. The method comprises the following steps of, (1) characteristic input, includinga project-attribute matrix representing a project content and a user behavior matrix representing user behaviors; (2) content-based project clustering for calculating the similarity of projects and clustering the projects; (3) score prediction and feature filling including carrying out score prediction on the non-scoring projects, and filling a user-project scoring matrix; (4) behavior-based userclustering including clustering users according to a project clustering result and a user-project scoring matrix; (5) score predication and project recommendation including determining the clusteringcluster where the target users are located, finding a nearest neighbor user set, performing score prediction on the non-scoring projects of the target users, and finally recommending the first N projects with the highest prediction scores to the target users. Compared with the prior art, the collaborative filtering method effectively solves the problems of data sparsity and cold start, and ensureshigh recommendation efficiency.

The invention discloses an item recommendation method for combining user comment content and grades. A model for combining the user comment content with user grades is provided for solving the cold start problem and the poor interpretability problem in a recommendation system. By means of the rich information contained in comments, the prediction accuracy is greatly improved; particularly, when data is quite sparse, the cold start problem and the poor interpretability problem can be well solved. The method mainly considers that descriptions of item characteristics are contained in user comment information, and potential characteristics in numerical value grades correspond to item characteristics in comment information through a mapping function. A model can be well built for user preferences, and therefore prediction and recommendation can be well carried out even if data is quite sparse.

The invention discloses a recommendation method and system integrating user behaviors and object content. The recommendation method integrating the user behaviors and the object content comprises the steps that through correlation between behavior data of each user and content data of all the objects, a list of user interest of each user to all the objects is obtained; the similarity of the list of user interest and the content data of all the objects is calculated, the recommendation object weight of each user to the content data of each object is obtained, and the objects recommended to each user are obtained through ranking of the recommendation object weights. According to the recommendation method and system integrating the user behaviors and the object content, the main problems that how an existing recommendation conducts cold start and how the existing recommendation smoothly transmits to the normal operation state from cold start are solved, the accuracy, the coverage rate, the novelty and the like of the system are greatly improved, the accuracy and the personalization of the recommendation system can be well improved, and more users are attracted and use the recommendation system.

The replication of large amounts of data from a database source to a database target is often prone to failure when the amount of data exceeds the transaction capacity of the database. In other words, if the amount of data transfer during initial start-up fills the transaction log before the database changes are committed, then the transaction does not complete and the changes are rolled back. For a transaction to be completed and database changes to be made permanent, the transaction must be completed in its entirety. A method for initializing data propagation execution for a large data source according to an embodiment of the invention uses standard DBMS copy, rename and log clearing / cleaning utility applications to successfully complete an initial load process so that all necessary initialization fields are primed. The method steps create temporary database table names and structures and execute an initialization / cold start using a single data record to set a synchronization point marker. Upon validation of the single record initialization, the replication of the large data table proceeds in a continue / warm mode from the set synchronization point marker. Executable instruction steps and computer readable media incorporating the executable instruction steps are disclosed.

The invention discloses a job recommending method, and belongs to the technical field of recommending systems. The job recommending method has the advantages that the Matthew effect is avoided, the problem of cold start is solved, and the populations are well utilized to realize personalized recommending. The job recommending method comprises the following steps of obtaining user data and job data; establishing a user preference vector space model and a job vector space model; according to the user preference vector space model and the job vector space model, calculating multi-domain scoring values based on contents, obtaining first scoring values of jobs, and sequencing, so as to obtain a job set; when one job is submitted and belongs to the job set, calculating the scoring valves of the corresponding job based on the similarity of user background information according to the user preference vector space model and the job data, and obtaining second scoring valves of the corresponding job; according to the first scoring valves and the second scoring valves, obtaining the mixed scoring valves of the corresponding job, and sequencing, so as to obtain a recommending list.

The invention provides a user portrait based personalized precision marketing method, a server and a storage medium. The method comprises the steps of obtaining label information of a user, building auser portrait according to the label information, obtaining the current Internet surfing behavior of the user, performing prediction and analysis on the behavior, screening out contents which the user may like or be interested in based on the user portrait when the user is predicted and analyzed to have a purchase or consumption intention, and carrying out personalized recommendation according tothe contents which the user may like or be interested in. Each user has a unique user portrait, and a list of contents which the user may like or interested in can be screened out through performingdeep analysis by combining the user portrait and the current Internet surfing behavior, thereby being capable of well performing personalized recommendation according to the condition of each user, improving the recommendation accuracy, and also being capable of well solving a problem of cold start of new users at the same time.

The invention relates to a recommendation algorithm based on an information security professional social network platform. The recommendation algorithm mainly comprises the following steps: (1) collecting user preferences, namely discovering laws from user behaviors and preferences, recommending based on the laws, wherein the process of collecting user preference information is the decisive factor of a system recommendation effect basis and a user provides the preference information for a system in various ways; (2) analyzing content characteristics, namely after analyzing the user behaviors to obtain the user preferences, calculating similar users and articles according to the user preferences, and recommending based on the similar users and articles; and (3) calculating the similarity. The recommendation algorithm has the benefits as follows: a better recommendation mode is created, so that the user can experience that the recommended content in a talent community concerned by the user is more personalized; and simple collaborative filtering and content-based methods are mixed, so that the performance is improved, a more accurate recommendation can be provided, and the common problems of cold start and sparsity in the recommendation system can be solved.

A microprocessor and method for branch prediction selection provides the capability to select among multiple direction based history arrays for a single branch. A global selection counter when used in conjunction with a Prediction Method Comparison Table (PMCT) allows for branch direction accuracy to be improved on cold starts and context switches while maintaining high accuracy on long running code while minimizing silicon area and reducing power requirements.

The invention belongs to the technical field of personalized recommendation, and particularly relates to a method and system for solving a cold start problem in a collaborative filtering technology. The method for solving the cold start problem in the collaborative filtering technology comprises the steps that a data set is selected; an initial user or project clustering model is built through an optimized genetic algorithm; clustering is conducted on the basis of the initial user or project clustering model, and a user or project clustering model is obtained; entropy values of new users or new projects to all kinds of clusters in the clustering model are calculated, and the new users or the new projects are subjected to class cluster dividing; the new users or the new projects are recommended. The invention further provides a system for solving the cold start problem in the collaborative filtering technology. The system comprises a selection module, an initial model building module, a clustering module, a class cluster dividing module and a recommendation generation module. Accordingly, an improved genetic algorithm is utilized for conducting K-Means clustering, the initial user or project clustering model is generated, and recommendation is generated for the new users or the new projects.

The embodiment of the invention discloses a television program recommending method and device for a digital television. The method comprises the following steps: collecting basic information of digital television subscribers; constructing an initial predictive scoring matrix according to the basic information of the subscribers; establishing a similar television program list according to the initial predictive scoring matrix; and acquiring the recommendation result according to the similar television program list. In the method and device provided by the embodiment of the invention, a content-based recommendation method is added on the basis of a collaborative filtering recommendation method so as to solve the problem of cold start up of a collaborative filtering recommendation system in the prior art, and the two methods are combined to construct the similar television program list, and recommendation service is provided for the subscribers by use of the similar television program list. Therefore, the method and device provided by the invention can be used for assisting the digital television subscribers to find the possible interested television programs, can ensure that a new digital television subscriber can obtain more accurate recommendation service, and can be used for solving the problem that a new television program has less possibility of being recommended by the system.

The invention discloses a video recommendation method and system based on Web mining. The method comprises the steps that a data mining algorithm is applied in clicking behavior data when users watch videos through Web mining, a user interest model is built through a classification and regression tree, a traditional collaborative filtering algorithm is adopted to recommend an individualized video to the users, the defect that in a traditional recommendation system, the data sparsity is brought due to the fact that user comment information is little is overcome, the problem of recommendation cold start due to the fact that a new user or a new project has no scores is solved, the satisfaction degree of the users to watch the video is improved, the users having the same interest and hobbies generate a recommendation, and friend recommendation is achieved in the video recommendation system.

The invention discloses a recommending method of a knowledge map based on the location service field, which includes: extracting a location entity and obtaining an entity set as a seed set of the knowledge map, corresponding the seed set with the entities in the knowledge map, forming an entity correspondence table, in a knowledge map triple in that knowledge map, using Word2Vec model to embed vocabulary into n-dimensional space, generating corresponding vectors, obtaining a position or domain entity vector set E and a relation vector set R, translating the entity vector set E and the relationvector set R by using a TransE algorithm, and obtaining a triple vector set capable of quickly calculating semantic similarity between entities; according to the location or domain entity vector setE, calculating respectively the semantic similarity simA, B (A, B) between the searching locations or domains to generate the semantic similarity matrix of the tourism location, using Semantic Similarity Matrix for Top-K Recommendation List, clustering the recommendation list according to machine learning clustering algorithm, and recommending the clustering result to the user. The method has highprecision and can solve the problems of cold start and sparsity.

The invention discloses an anonymous cross-domain recommendation method based on block chain technology, A technology base on block chain adopts heterogeneous multi-link data structure to carry out data storage, Users, merchandise and transaction relationship data are stored on users, merchants and platforms respectively. Transaction relationship does not store specific user and commodity information. Recommendation center can only obtain the relationship chain stored by each platform to ensure that an attacker cannot associate a user with a commodity. Aiming at the cold start problem caused by data sparseness, the invention performs data aggregation and similarity calculation based on accurate transaction data, and adopts a mixed recommendation strategy based on users and commodities to perform cross-platform recommendation, so as to achieve a safe and accurate win-win recommendation effect.

The invention provides a recommendation system cold start solving method based on user feedback. The method comprises the following steps: selecting data samples; constructing a time sequence sample matrix, dividing a user-commodity real score matrix into a plurality of sub matrixes according to a time sequence, simulating emergence of new users, taking sub matrixes in the top of time rank as training sub matrixes and taking other sub matrixes as test sub matrixes; and establishing a user-commodity characteristic matrix by using a latent semantic model, introducing new users into a confidence interval upper bounded UCB algorithm model and iterating and updating user characteristics and commodity characteristics. The recommendation system cold start solving method does not need extra information and is capable of rapidly screening commodities interested by the users according to limited frequency of user feedback interaction.

The invention relates to a dialogue strategy-optimized cold start system and a method. The system comprises a user input module, a dialogue state tracking module, a teacher decision-making module, a student decision-making module, an action selection module, an output module, a strategy training module and a reward function module. The action selection module randomly selects one final reply action from all reply actions generated by the teacher decision-making module and the student decision-making module. The output module converts the final reply action into a more natural expression and displays the more natural expression to a user. The strategy training module stores the dialogue experience (transition) in an empirical pool, samples a fixed number of experiences, and updates network parameters according to a depth Q network (DQN) algorithm. The reward function module calculates the reward of the dialogue at each round of the dialogue, and outputs the reward to the strategy training module. According to the invention, the performance of the dialogue strategy during the strengthened learning on-line training initial stage can be remarkably improved. The learning speed of the dialogue strategy is increased, and the number of dialogues used for achieving certain performances is reduced.

A method performed by a recommender system to recommend items to a new user includes calculating reward estimates from multiple multi-armed bandit models of a user and her social network friends. The new user's social network friends have multi-armed bandit models that are well established. The mixed multi-armed bandit estimates are processed to select the arm that maximizes the estimated reward to the new user. The multi-armed bandit arm of the greatest reward estimate is played and the new user responds by providing feedback so that the new user's multi-armed bandit model is updated as time progresses.

The invention provides a convolution neural network-based music recommending system and method. The system comprises a music user modeling module for collecting historical behavior data of a music user and constructing a preference model of the music user; a music feature modeling module for obtaining a regression model; and a recommendation algorithm module for finding music objects matched withthe preference of the music user through the regression model and recommending the music objects to the music user. According to the system provided by the invention, deep learning is applied to the recommending system, semantic differences between song features and audio signals are effectively compensated and the problems such as "cold start" and the like in collaborative filtering are avoided at the same time, so that the accuracy of the recommending system is increased; and the contradiction between low training efficiency and a high timeliness requirement is solved by adopting a convolution neural network and the historical behavior information of the user and audio acoustic features are added to the model, so that the recommendation results are more in line with the preference requirements of the user and the user experience of the recommending system is improved.