541results about How to "Improve recommendations" patented technology

Computer-implemented processes are disclosed for clustering items and improving the utility of item recommendations. One process involves applying a clustering algorithm to a user's collection of items. Information about the resulting clusters is then used to select items to use as recommendation sources. Another process involves displaying the clusters of items to the user via a collection management interface that enables the user to attach cluster-level metadata, such as by rating or tagging entire clusters of items. The resulting metadata may be used to improve the recommendations generated by a recommendation engine. Another process involves forming clusters of items in which a user has indicated a lack of interest, and using these clusters to filter the output of a recommendation engine. Yet another process involves applying a clustering algorithm to the output of a recommendation engine to arrange the recommended items into cluster-based categories for presentation to the user.

The invention discloses an information recommendation method based on graph convolution and neural collaborative filtering. In combination with the advantages of a graph convolution neural network model, fusion processing can be carried out on various information in an intuitive manner, so that not only feature information of a user but also attribute information of the user can be received, and relatively good recommendation performance can be achieved for sparse score data; and input and parameters of the model are subjected to optimization modeling by using multiple skills, so that the detail problems encountered possibly are solved. In addition, a nonlinear neural network-based collaborative filtering method is introduced as a decoder part of the model, so that user and article codes output by a graph convolution encoder can be well utilized, and through an end-to-end model, all processes run in the same framework without being trained separately. Through the processing of input data and the training and prediction of the model, a complete score prediction matrix can be obtained.

Computer-implemented processes are disclosed for clustering items and improving the utility of item recommendations. One process involves applying a clustering algorithm to a user's collection of items. Information about the resulting clusters is then used to select items to use as recommendation sources. Another process involves displaying the clusters of items to the user via a collection management interface that enables the user to attach cluster-level metadata, such as by rating or tagging entire clusters of items. The resulting metadata may be used to improve the recommendations generated by a recommendation engine. Another process involves forming clusters of items in which a user has indicated a lack of interest, and using these clusters to filter the output of a recommendation engine. Yet another process involves applying a clustering algorithm to the output of a recommendation engine to arrange the recommended items into cluster-based categories for presentation to the user.

The application provides a software recommending method and a system for solving the problems of complex operation steps for downloading a software by a user and consumption of a large amount of time for looking up the required software. The software recommending method comprises the steps of: detecting an installed software and / or a run web application at a client terminal; analyzing the installed software and / or the run web application at the client terminal, and obtaining personalized data of the user at the client terminal, wherein the personalized data includes age data, and / or character data, and / or sex data; and according to the personalized data of the user at the client terminal, providing a corresponding software recommending mechanism. In such a software recommending way special for different personalities of different users, as the software or the web application installed or ran by the user is subjected to user preference analysis, the recommendation is more purposeful, and the recommended software is more useful for the user; in addition, the user does not need to look up the required software in numerous softwares by complex operations.

The invention discloses an article information recommending method and device. The method comprises the steps of obtaining attribute information and user behavior data of an access user when an article access request is received; obtaining a corresponding candidate article set; determining articles satisfying preset conditions in the candidate article set based on a similarity matrix, the attribute information and the user behavior data, wherein the similarity matrix is used for indicating the similarities among the candidate articles, and the similarities between the candidate articles and the attribute information; and recommending the information of the articles satisfying the preset conditions to the access user. Through adoption of the similarity matrix, the attribute information of the access user and recent different click and consumption behaviors to the articles, intention prediction is carried out on user access; and therefore, the articles suitable for the user are determined and recommended to the user. Compared with the mode of carrying out recommendation through prediction of the click-through-rate scores of the user to the articles based on a linear model, the method and the device have the advantages of improving individuation of the recommendation results and improving the accuracy of the recommendation results.

Computer-implemented processes are disclosed for clustering items and improving the utility of item recommendations. One process involves applying a clustering algorithm to a user's collection of items. Information about the resulting clusters is then used to select items to use as recommendation sources. Another process involves displaying the clusters of items to the user via a collection management interface that enables the user to attach cluster-level metadata, such as by rating or tagging entire clusters of items. The resulting metadata may be used to improve the recommendations generated by a recommendation engine. Another process involves forming clusters of items in which a user has indicated a lack of interest, and using these clusters to filter the output of a recommendation engine. Yet another process involves applying a clustering algorithm to the output of a recommendation engine to arrange the recommended items into cluster-based categories for presentation to the user.

The invention discloses a method and a device for communication between a mobile terminal and a background server. The method provided by the invention comprises the following steps that a first bar code image is generated and is issued onto at least one region capable of being touched by a user of a mobile terminal, the first bar code image at least comprises service information, and the unique relevance relationship between the first bar code image and the mobile terminal is built; the first bar code image is subjected to photo taking through a camera arranged in the mobile terminal, and the mobile terminal decodes the first bar code image; and the background server generates a second bar code image and sends the second bar code image to the mobile terminal, and the second bar code image at least comprises the service information. The method and the device disclosed by the invention can realize the identity verification of the user, so the safety of user information is ensured.

The invention discloses a music recommendation method, which comprises: an association value among music is obtained by acquiring and analyzing related data sources of the music beforehand, and is saved in music-associated network; when needed to recommend the music to a user, the music related with the interest of the user are acquired to serve as a recommendation seed; an association value of the recommendation seed and the music is obtained in the music-associated network; and the music which has a maximum association value with the recommendation seed is selected and recommended to the user. The invention also discloses a music recommendation device. The method and the device enlarge the selectable range of music recommendation and are convenient to recommend to a user more music possibly loved by the user. In addition, the method and the device can feedback preference information of the user to the music-associated network to improve the grasp of the user interest, thereby realizing the personalized music recommendation to specified users to a certain extent and enhancing the effect of the recommendation.

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.

An apparatus for content item recommendation, such as a Digital Video Recorder, comprises a grouping unit (105) for grouping user ratings for content items into rating groups in response to a content item match criterion. A receiver (109) receives content item data for a plurality of content items and a first recommendation unit (107) generating a first set of content item recommendations. An association unit (111) then determines an associated rating group of the rating groups for each content item recommendation of the first set and a second recommendation unit (113) generates a second set of content item recommendations from the first set in response to a rating group distribution measure for the second set. The invention may allow improved recommendation of content items which is aligned with user preferences yet provide a desired diversity of the provided recommendation. The invention may in particular provide improved performance for multi-user devices.

A recommendation system comprises a recommendation server (107) which generates a first recommendation set of recommended content items in response to a user profile associated with a first user and stored on the recommendation server (107). Content item identification data identifying the content items of the first recommendation set are transmitted to a first recommendation device (101). The first recommendation device (101) comprises a network interface (301) which receives the content item identification data from the recommendation server (107). A content list processor (303) determines the first recommendation set in response to the content item identification data. The first recommendation device (101) furthermore comprises application processors (309-313) which can execute different recommendation applications. A device recommender (307) generates a second set of recommended content items from the first recommendation set in response to a characteristic of the recommendation application being executed. The application then provides recommendations in response to the second set.

Computer-implemented processes are disclosed for clustering items and improving the utility of item recommendations. One process involves applying a clustering algorithm to a user's collection of items. Information about the resulting clusters is then used to select items to use as recommendation sources. Another process involves displaying the clusters of items to the user via a collection management interface that enables the user to attach cluster-level metadata, such as by rating or tagging entire clusters of items. The resulting metadata may be used to improve the recommendations generated by a recommendation engine. Another process involves forming clusters of items in which a user has indicated a lack of interest, and using these clusters to filter the output of a recommendation engine. Yet another process involves applying a clustering algorithm to the output of a recommendation engine to arrange the recommended items into cluster-based categories for presentation to the user.

The present invention relates to a computerized system for and method of providing precision healthcare services such as consultation, education, assessment, diagnosis, intervention, or treatment at a distance via encrypted real-time image and audio presence where the healthcare professional's assessment, diagnosis, and intervention activities are informed by patient feedback, smart objects, and artificial intelligence and patient outcomes are optimized through recursive system feedback. The present invention is unlimited with regard to the type of patient entity or healthcare professional entity.

A recommendation system comprises a plurality of recommenders for generating recommendations in accordance with a user preference profile having a recommender specific representation. The recommender specific representation is different for different recommenders and each recommender comprises translation data relating the recommender specific representation to a shared ontology. A recommender comprises a translation unit which generates first user preference data in accordance with the shared ontology in response to a user preference profile and translation data relating the recommender specific representation to the shared ontology. In addition, a confidence indication for at least part of the user preference data is generated. A transmitter transmits the user preference data and the confidence indication to a second recommender. The second recommender may translate the received data into its recommender specific representation and combine the received data with locally generated user preference data. The invention may facilitate and / or improve sharing of user preference data.

Computer-implemented processes are disclosed for clustering items and improving the utility of item recommendations. One process involves applying a clustering algorithm to a user's collection of items. Information about the resulting clusters is then used to select items to use as recommendation sources. Another process involves displaying the clusters of items to the user via a collection management interface that enables the user to attach cluster-level metadata, such as by rating or tagging entire clusters of items. The resulting metadata may be used to improve the recommendations generated by a recommendation engine. Another process involves forming clusters of items in which a user has indicated a lack of interest, and using these clusters to filter the output of a recommendation engine. Yet another process involves applying a clustering algorithm to the output of a recommendation engine to arrange the recommended items into cluster-based categories for presentation to the user.