804results about How to "Accurate recommendation" patented technology

The invention discloses a commodity recommendation method which comprises a step of obtaining the historical behavior data of a user browsing commodity, a step of using a pre-generated commodity attribute value prediction model to obtain the currently interested commodity attribute value combination of a user according to the user information of a current user, the historical behavior data and a recommended commodity attribute value combination, and a step of obtaining the commodity set corresponding to the currently interested commodity attribute value combination of the user and recommending at least a part of the commodity in the commodity set to the current user. The invention also discloses a commodity recommendation device. By using the method provided by the invention, the currently interested commodity information can be accurately recommended to the user who browses the commodity at present in real time, the time of browsing the commodity of the user can be saved, the user experience can be effectively improved, and the commodity sale volume of a site can be improved.

The invention discloses a commodity information recommending method based on user historical behaviors. The method comprises the following steps of S11, acquiring historical behavior data of a user in an E-commerce website; S12, establishing a user commodity probability prediction characteristic vector according to the historical behavior data; S13, training a model according to the user commodity probability prediction characteristic vector, and obtaining a user recommendation commodity prediction model; S14, inputting to-be-predicated user data into the user recommendation commodity prediction model, and calculating a predicated purchasing probability of a behavior commodity; and S15, according to the predicated purchasing probability, calculating the predicated purchasing probabilities of correlated commodities, and combining the behavior commodities and the correlated commodities for obtaining a commodity recommending list. According to the commodity information recommending method and the commodity information recommending system, behavior data of the user are accurately analyzed; an individualized commodity recommending list is supplied to a user; and furthermore higher accuracy in commodity recommending is realized.

The invention discloses a position recommendation system based on a knowledge base. The position recommendation system based on the knowledge base comprises a human resource proprietary knowledge base construction module, a talent information crawling module, an information extraction, fusion and comparison module and a position recommendation module, wherein the human resource proprietary knowledge base construction module is used for constructing a proprietary knowledge base relevant to human resources; the talent information crawling module is used for crawling and integrating personal information related to an applicant; the information extraction, fusion and comparison module is used for analyzing and processing data obtained by fetching and is used for mapping and comparing talent requirements and positions so as to carry out modeling analysis on user demands; and the position recommendation module is used for carrying out position query on a user to be recommended and carrying out recommendation according to relevancy, interest and the information of a circle of friends. The proprietary knowledge base is established by aiming at a specific field, i.e. the human resources, and the knowledge base is effectively utilized to realize on-line precise position recommendation for users.

The invention provides an intelligent review expert recommending method for science and technology projects. The method includes the following steps that (1) the science and technology projects to be reviewed and expert information main texts are segmented into substring sequences, ICTCLAS segmentation of Chinese academy of sciences is carried out on the substring sequences, and stop word filtering is carried out on a segmentation result to obtain a term set; (2) a term network of project information is built, feature words are extracted on the basis of statistical characteristics and aggregation characteristics, and if expert information is relatively concise, the term set obtained in the step (1) directly serves as the feature words; (3) a knowledge representation model is built on the basis of fields and weights of the feature words, and a relative information index is built; (4) experts are recommended in groups to carry out feature merging operations between the fields and between the projects on the knowledge representation model; (5) similarity of the experts and the science and technology projects or groups to be viewed is calculated on the basis of semantics, threshold truncation is set, and a final recommended expert list is generated. By means of the method, the problems that recommending workload is large and review decisions lack scientificity are greatly alleviated.

The invention discloses an information recommending method, an information recommending device and a mobile terminal. The information recommending method comprises the following steps of providing an information display page in which a plurality of classifying labels are displayed; respectively acquiring user preference marks of a plurality of information contents corresponding to each classifying label; acquiring user preference information corresponding to each classifying label according to the corresponding user preference mark; and managing the labels of the information displaying page according to the user preference information of the classifying labels; and performing information recommendation according to the classifying labels displayed in the information displaying page after the labels are managed. By the information recommending method of an embodiment, information contents which attract a user can be accurately determined, so that personalized information can be accurately recommended for the user. Moreover, the user does not need to set marks when the user preference information corresponding to the classifying labels is acquired, and the user experience is smooth and natural.

The invention discloses a method and device for application recommendation. The method comprises the steps of obtaining behavior data, relating to applications, of a user; determining attribute data relating to the applications of the user according to the behavior data, relating to the applications, of the user; carrying out matching between labels in detail pages of applications to be recommended and the attribute data relating to the applications of the user, and ranking according to matching degrees; recommending a preset number of applications ranked first to the user. According to the technical scheme, intelligent, personalized and automatic recommendation of the applications is achieved, the defect that the recommendation scheme based on mass behaviors is lacking in personalization is overcome, and user requirements are met.

Systems and methods for providing recommendations directed to cultural events, such as films, theatre shows, concerts, etc., via an online site are described. The systems and methods may also be used to provide recommendations for restaurants, books, television shows or other subjects. The system and method include a trainable recommendation engine that collects, aggregates, and combines trusted recommendations to create a personalized list of recommended events. Algorithms are used to gather and aggregate individual reviews of events and event listings, restaurants, books or other subjects from the Internet, aggregate the likes and dislikes of individuals who provide reviews to generate customized recommendations for the user, and constantly improve each user's experience by identifying likeminded individuals within the system.

The invention discloses a real-time goods recommendation method based on customer shopping intention exploration. The method comprises the following steps: 1, constructing a tree-shaped customer shopping intention model; 2, detecting user behaviors in real time and identifying user intention; 3, detecting the change of the user intention in real time; 4, tracking the change track of the user intention and predicting the deep intention of the user: predicting the deep intention of the user based on the discrete intention of the user; and 5, determining a goods recommendation strategy: determining the goods recommendation strategy according to the shopping intention of the customer. The method disclosed by the invention automatically identifies the shopping intention of the customer, performs precise individualized recommendation, and improves the customer satisfaction; and moreover, the method automatically detects the change of the shopping intention of the customer and adjusts the goods recommendation strategy. The method adapts to the changing interest of the customer in real time, deeply explores the shopping intention of the customer based on the user intention model and provides professional recommendation service to the user.

The invention discloses a method for recommending an opposite sex friend in an SNS community. The SNS community at least comprises a first user and a second opposite sex user. The method comprises the following steps of: (a) collecting personal characteristic information of the first user and the second opposite sex user in opposite sex friends-making appeals; (b) storing the information; (c) extracting the information and judging whether the first user and the second opposite sex user pass the opposite sex friend threshold standard set by the two parties simultaneously when the first user requests to acquire an opposite sex friend or the first user logs in the SNS community; (d) performing matching operation based on the personal complex characteristic information of the two parties; and (e) when the personal complex characteristic information between the first user and the second opposite sex user is matched, recommending the second opposite sex user to the first user serving as an opposite sex friend. The method and the system for recommending the opposite sex friend specially aiming at the SNS community are expanded, and the friend-making accuracy and pertinence in the SNS community are improved.

The invention discloses an intelligent insurance recommendation method, a device and an intelligent insurance robot device, belonging to the technical field of artificial intelligence. The method comprises the following steps of: preprocessing the user voice information obtained through voice interaction; the preprocessed text information being classified into intention information and entity identification information, and the intention information and entity identification information being obtained; combining the intention information, entity identification information and insurance user portrait information, corresponding insurance recommendation being performed; at the voice interaction scene, preprocessing the acquired voice of the acquired user, then combining intention recognitionclassification and entity recognition, at last, combining the result of intention classification, entity recognition result and user portrait information, the insurance recommendation being intelligently carried out through big data combination analysis, so as to meet the requirement of intelligent insurance service of users in the prior art. is the method and the device are applicable to the technical fields of intelligent recognition and recommendation of similar insurance, and have good application prospect.

The invention relates to a social association cloud media collaborative filtering and recommending method. The method includes the following steps that micro blogs sent by multiple micro blog users and associated users of the micro blog users are obtained; a user program rating matrix for reflecting the corresponding relation between different users and grading of different programs is built; influence grading of the associated users on the programs is calculated; the feature vector of the micro log users is calculated; feature similarity of the micro log users is calculated; the influence grading of similar users similar to the micro log users on the programs is calculated; the user program grading matrix is updated according to the influence grading of the associated users on the programs and the influence grading of the similar users on the programs; network resources are explored, and the updated user program grading matrix is expanded; cluster is conducted on the user program grading matrix based on the users and the programs respectively; class cluster obtained through the cluster serves as a neighbor search domain, and grading is predicted through collaborative filtering and recommending. By means of the method, network information content which interests the users can be accurately recommended for the users.

The invention discloses a method and a device for information pushing. The method includes acquiring client-related consumption information of a client terminal by an information pushing platform; acquiring current position information of the client terminal by the information pushing platform; pushing corresponding business information to the client terminal by the information pushing platform according to the current position information and the client-related consumption information of the client terminal. By the method and the device, business information suitable for clients is pushed to the clients according to current time, pushing objects and pushed business information are more accurate, and the corresponding business information is accurately recommended to the clients to enable the clients to be capable of choosing from according to own needs.

The invention discloses a network music aggregation recommendation method based on label digraphs, and belongs to the field of network music aggregation. The network music aggregation recommendation method based on the label digraphs can effectively solve the problem that a user classification preference sequence cannot be reflected in an existing traditional label classification recommendation method. According to the network music aggregation recommendation method based on the label digraphs, a quaternary relation formed by users, labels, music and a cognition sequence is taken into adequate consideration; the relational network is utilized for further improving music recommendation accuracy and the degree of satisfaction of the network users. According to the technology, network music features and user interest features are described through the digraphs, and a music feature digraph set is divided into a plurality of digraph clusters, so that the digraphs in each cluster are isomorphic to the greatest extent while the digraphs in different clusters are different to the greatest extent (representing the difference between the digraphs). At the time of similarity matching, it is not needed that the whole music feature digraph set is searched, most of suitable digraphs of a target can be found through inquiries from a plurality of digraph clusters with the highest similarity to a target user interest digraph, and therefore the purpose of recommending music to the music network users quickly and accurately can be achieved.

The invention discloses a line customization method and a line customization system based on travel planning. The method comprises the following steps: grabbing POI data in a WEB server, and finding out the key words in the POI data; building a tag library according to a set dimension, and establishing the corresponding relationship between the keywords and user preferences; getting a travel library according to the travel of a user, and decomposing the travel library into travel fragments, scoring POI attribute tags contained in the travel fragments to get the score of the user preference, and getting the routes of the travel fragments according to the score of the user preference; and combining the routes in the travel fragments through a POI combination algorithm to get a customized line. According to the invention, the fragments are combined into a finished recommended line based on POI attributes and tags, and thus, a travel fitting the personalized travel preference of a user can be recommended to the user.

A water monitoring device monitors and maintains swimming pool chemistry. The device includes sensors that detect water chemistry. The water monitoring device can communicate with computers and servers. This system can be used to determine if corrections to the water chemistry are required to maintain water sanitation. The device can monitor: pH, air temperature, water temperature, free chlorine levels, oxidation reduction potential, alkalinity, oxygen demand, water movement and velocity, and electrical conductivity.

The invention discloses a direct broadcasting room recommending method and system based on broadcaster styles, and relates to the network technical field; the method comprises the following steps: collecting characteristic parameters and user data of direct broadcasting rooms from a server in a set time period; using characteristic parameters of each direct broadcasting room as characteristic constants to build a characteristic vector of the direct broadcasting room; selecting two random direct broadcasting rooms with broadcasters of different personal information, calculating similarity between characteristic vectors of the two direct broadcasting rooms, and determining direct broadcasting rooms with similarities; recommending other direct broadcasting rooms similar to the direct broadcasting room to all users in the direct broadcasting room according to user data of each direct broadcasting room; calculating a characteristic vector evaluate index according to the visiting rate and / or return visiting rate of the recommended direct broadcasting room, using the evaluate index to screen the characteristic vector characteristic constant, and using the screened characteristic vector to determine similar direct broadcasting rooms. The method and system can precisely recommend direct broadcasting rooms with similar styles to users, thus improving recommending efficiency, and improving user experiences.

The embodiment of the invention discloses a video recommendation method and a device. The method comprises the following steps: according to a target video triggered and selected on a home page of a terminal, skipping to a video page associated with the target video by the terminal; obtaining recommendation tags for performing video recommendation on the target video, wherein the recommendation tags are obtained by sorting candidate tags contained in the target video according to historical watching behavior data of a user and screening the candidate tags according to a sorting result; displaying the recommendation label on the video page; and jumping to a recommended video page associated with the recommended label according to the recommended label triggered and selected on the video page. According to the method, video recommendation is carried out on the target video according to the candidate label sequence of the target video, user interest is considered, and accurate personalized recommendation can be carried out on the target video.

The invention provides a scenic area tourist chain travel integration providing method, and relates to the technical field of tourism service software development. The problem of poor travel experience caused by lack of effective real-time data guidance during the travel of a tourist is solved. The method comprises the steps of real-time prediction of the crowding degree of queuing in a scenic spot, accurate optimal travel route pushing and mining and analysis of feedback data of tourists. The method specifically comprises the steps that the information of a scenic area and the number of tourists in each scenic spot are recognized and calculated; the crowding degree of the scenic spot is determined according to an evaluation index, and the crowding degree is visually displayed on a map toprovide tourists with intuitive judgment; and at the same time, according to the user geographic coordinate and user demand data of a system platform, a path optimization algorithm is used to accurately recommend a reasonable route. The queuing waiting time of a user is minimized, and the traffic experience is improved. In order to form benign feedback, data used by tourists are analyzed to acquire scenic spots prone to serious queuing crowding and the preference for different recommended routes.

The invention discloses an artificial intelligence teaching system and method for science teaching. The system comprises an input device, an output device, a calculation device and a data storage device formed by a plurality of databases, wherein the data storage device is internally provided with a knowledge chapter database, a knowledge topic database, a test question database and a test question answer database; a keyword extraction module, a learning path recommendation module are arranged in the calculation device; the data storage module furthermore comprises an evaluation test paper database, a student evaluation report database and a student individualized test paper database; and the calculation device furthermore comprises a management module, a statistical analysis module and acorrection module. The system and method are capable of effectively managing knowledge points and test questions, and continuously correcting, improving and recommending learning paths in allusion tolearning conditions of student individuals, so as to realize accurate recommendation.

The invention relates to a commodity individual recommendation method for new users and a system. The method comprises the following steps: specific to the new users having no corresponding attributive character of a purchase commodity, acquiring a relationship between the attributive character of the commodity and the sales volume of the commodity by taking the sales volume of the commodity as a forecast target according to the recorded historic attributive character of the commodity; forecasting the sales volume of the to-be-recommended commodities according to the relationship between the attributive character of the commodity and the sales volume of the commodity; ranking the to-be-recommended commodities; performing individual recommendation according to a ranking result. The forecast target is arranged as the sales volume, so that the quantity of the new users purchasing the commodities is considered instead of the sales volume contributed by the new users; for the new users contributing to the webpage visiting volume, the motivation for arranging the forecast target is clearer; accurate recommendation can be performed and a better recommendation effect is achieved.

The invention discloses a multimedia data recommendation method and server. The method includes the steps of: generating regional feature vectors of each region; receiving a recommendation content obtaining instruction; obtaining user information, historical access data and position information of a target user; forming an alternative multimedia data set; performing target user interest hot degree scoring on multimedia data in the alternative multimedia data set; extracting the regional feature vectors related with the position information of the target user; performing region information scoring on the multimedia data in the alternative multimedia data set; based on target user interest hot degree scores and region information scores, obtaining comprehensive scores of the multimedia data in the alternative multimedia data set; and recommending to the target user a plurality of multimedia data with comprehensive scores which rank high. The multimedia data recommendation method and server proposed by the invention can well recommend to a specific user multimedia data that well satisfy real demand thereof.

The invention discloses an item-based explicit and implicit feedback mixing collaborative filtering recommendation algorithm. The method comprises the following steps of obtaining the information of interest of users on every item and establishing the score matrix of every user on all the items; calculating the average score of every user, the quantity of the scoring users of every item and the average score of every item; calculating a common comment user quantity matrix; calculating the Pearson similarity and the modified cosine similarity of between any two items; calculating the similarity based on explicit feedback; calculating the cosine similarity based on implicit feedback; calculating a final similarity; obtaining the nearest neighbor set I of a current item; when providing a recommendation list to a target user u, according to the score matrix, obtaining the scored items and the unscored items of the target user u; calculating the prediction scores of the unscored items of the target user u and selecting N items with the highest scores inside the unscored items of the target user u to the user. The item-based explicit and implicit feedback mixing collaborative filtering recommendation algorithm can effectively improve the accuracy of prediction recommendation.

A remembrance agent is proposed, to be hosted on a wearable computer. The remembrance agent employs a first database (WK) of knowledge about the world in the form of concept maps. Events (“episodes”) experienced by a user are each classified as relating to one or more of the concepts in WK. The episodes are used to produce a second database (episodic memory, EM), and the classification is also used to update a third database (semantic memory, SM) organised using the concepts. The semantic memory thus summarizes the user's interaction with the concepts during the episodes. A current situation of the user is classified according to the concepts, and the classification is used, with the EM and SM, to provide to the user information relevant to the current situation.

The invention provides a knowledge document recommendation method based on user historical behavior features. According to the knowledge document recommendation method, the word frequency of each word in an article is calculated, the words and the word frequencies are used as items and support degrees respectively, and the article which is most relevant to the article which is uploaded by a user is excavated by means of the FP-Tree method. The knowledge document recommendation method comprises the steps that a knowledge base word library is extracted through word segmentation of articles which the user has read and are stored in a knowledge base; word tables in the user word library are scanned and optimized, the support degrees of the FP-Tree method is replaced by TF word frequencies so as to establish a FP tree, and a frequent item set containing user reading features is excavated; the most relevant articles are determined finally, the most relevant articles are ranked according to the importance degrees, and the ranked most relevant articles are recommended to the user. According to the knowledge document recommendation method based on the user historical behavior features, the words in the articles are used as excavation features, modeling is conducted on the historical reading behavior of each user, dependence on the reading behaviors of other users is avoided, and therefore the problems that a great number of valuable articles in an enterprise knowledge base are not read by people and the users cannot find the articles containing relevant knowledge at the same time are solved.

The invention discloses a user commodity information pushing method based on image attribute extraction. The user commodity information pushing method is characterized by comprising the steps of: acquiring an instruction for triggering a recommendation; extracting commodity attributes according to the instruction, and searching target commodities comprising the commodity attributes in a user interest library; and forming a recommended commodity list and pushing commodity information to a user. The invention further discloses user commodity information pushing equipment using the user commodityinformation pushing method. The user commodity information pushing method is simple and convenient, the user commodity information pushing equipment is compact in structure, a relatively perfect userpersonalized database is established by combining picture attributes of the commodities with a knowledge graph, and precise pushing of similar commodities is realized by adopting collaborative filtering based on the user and the commodities, thereby bringing the fresh and comfortable shopping experience to the user.

A data fusion based video program recommendation system comprises a user behavioral data collection module, a data fusion module and a program recommendation module, wherein the user behavioral data collection module is used for collecting and storing the behavioral data of the uses in browsing video websites; the data fusion module is connected with the user behavioral data collection module and is used for converting the behavioral data of a user in browsing a video program into the interest value of the user in the video program; and the program recommendation module is used for recommending the programs in which the user has high interest value to other users having the same favorites according to the interest value of the user in a few programs. The behavioral data in browsing comprise behavioral data in playing. The duration ratio r is obtained by dividing the duration of the programs played by the users by the total duration of the programs. The higher the duration ratio r is, the higher the interest value is. As long as the users play the programs and other behavioral data of the users related to the interest value are fused, the program lists to be recommended can be generated more accurately and the degree of satisfaction of the users with the recommended programs is greatly improved.

The invention discloses an information individualized recommendation method based on Web log data, and belongs to the technical field of electronic information. The method is used for an information mode of a server plus a broadband network plus a multimedia thin client side. The method includes the steps that users have access to internet sources through the multimedia thin client side, and theserver records the behaviors of the users into server log files; clean, regular and accurate data sources are extracted through analysis and preprocessing of data of the Web log files in the server; a user interest matrix is built through a collaborative filtering technology, similarity between the users is calculated, and the users with large similarity are selected as similar users; a recommendation resource pool is built according to hobbies and interests of the similar users; the server selects and recommends pages of which the recommendation value is larger than the threshold value in the recommendation resource pool to the users. The method has the advantages that the data in the Web log files are preprocessed to acquire the clean and regular data sources, and accurate and individualized information recommendation is provided for the users by the cooperation of the hobbies and the interests of the similar users.

The invention provides a media file recommending method and system based on a vehicle-mounted terminal and the vehicle-mounted terminal. Comprehensive analysis is performed according to obtained driving behavior information, voice and mood information, facial expression information and / or posture information, and emotion information is obtained; according to the emotion information, the satisfied conditions are matched from a plurality of preset conditions, and media files related to the satisfied conditions in advance are obtained to be recommended. The media files can be precisely recommended for a user, operation is intelligent and user-friendly, and the user experience is good.