541results about How to "Improve recommendations" patented technology

A search and recommendation system employs the preferences and profiles of individual users and groups within a community of users, as well as information derived from categorically organized content pointers, to augment Internet searches, re-rank search results, and provide recommendations for objects based on an initial subject-matter query. The search and recommendation system operates in the context of a content pointer manager, which stores individual users' content pointers (some of which may be published or shared for group use) on a centralized content pointer database connected to the Internet. The shared content pointer manager is implemented as a distributed program, portions of which operate on users' terminals and other portions of which operate on the centralized content pointer database. A user's content pointers are organized in accordance with a local topical categorical hierarchy. The hierarchical organization is used to define a relevance context within which returned objects are evaluated and ordered.

CCRM is a business method and computer software system, to be used by Enterprises selling portfolios of products / services, aiming to optimize the expected value of transactions (or contracts) with consumers or business customers. At a transaction level, CCRM estimates the probability of choice of potential offers by the customer. These offers may be presented alone with possible variation of their attributes (such as price), or in combinations / sets, or in sequences. CCRM calculates the probability of consequent conversion and realization of the sale. Probabilities of choice and conversion are forecasted based on a disaggregated customer choice model, taking into account customer characteristics and stated preferences as well as product / service attributes such as price. Offers are then scored and ranked by expected value based on their revenue, cost and choice probability. Finally, CCRM recommends which offer(s) to present to the customer, at which price(s) and in which display / sequence order, to maximize a business objective function such as the expected value of the transaction / contract.

A novel system and method of predicting a user's rating of a new item in a collaborative filtering system is described. The invention incorporates social network information in addition to user ratings to make recommendations. The distance between users in the social network is used to enhance the estimate of user similarities for collaborative filtering. The social network can be constructed explicitly by users or deduced implicitly from observed interaction between users.

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.

A process, system and computer software are rovided to facilitate at least one recommendation to one or more users (420) and / or one or more applications (430) using multidimensional data (410). In particular, the multidimensional data (410) which define a multidimensional space (400) is accessed. The multidimensional space (400) can have more than two dimensions, each capable of providing variable information. Then, information from the multidimensional space is retrieved. Further, the recommendation is generated based on the retrieved information. In addition, it is possible to generate a recommendation-specific query (440). This can be performed by generating the recommendation-specific query (440) to access a multidimensional cube and retrieve data stored therein. Then, the recommendation can be issued based on teh data retrieved from the multidimensional cube.

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.

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.

A process for investment in intellectual property, comprising providing an accounting for an intellectual property investment; providing a valuation with respect to the intellectual property investment; performing financial analysis related to the intellectual property investment and making a recommendation based upon the financial analysis; and managing the investment based upon the accounting, valuation, and analysis for the investment. The financial analysis includes computing one or more financial ratios or indices, comparing financial information relating to the IP investments to one or more other IP investments, and making a financial transaction recommendation with respect to the IP investment. The analysis and recommendation process may stand alone or may be used in conjunction with pre-existing, non-IP-based securities analysis and recommendation / estimation processes.

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 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 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.

An avatar having artificial intelligence for identifying and providing relationship or wellbeing recommendations is provided. The avatar acts as an electronic representation of a user. The avatar searches available information and makes recommendations to the user based on information received from the user or other sources regarding the user's relationship with another person or the user's wellbeing. In this way, the avatar continually learns more about the user to improve future recommendations to enhance the user's wellbeing and relationship with the other person.

An invention for displaying patterns of utilization of a resource wherein the resource includes a plurality of correlated or uncorrelated objects. The method consists of providing a number of graphical formats from which a format is selected for display of patterns of utilization. Additionally, a query is submitted regarding a particular utilization of the resource. Along with the selected format and query inputs, the invention receives information regarding utilization of the resource from a system. Once the invention receives the inputs and the utilization information from the system, the invention processes the information to a format that conforms with the inputs. The invention then displays the processed information in the selected graphical format. The invention also allows the selection of a second graphical format from the graphical formats for altering the display to show the processed information in the second selected format.

The invention provides a mobile data traffic package recommendation algorithm based on user historical data according to data mining analysis technology. The mobile data traffic package recommendation algorithm comprises the following steps of: 1) a target user finding period comprising the processes of a, acquiring a processed generated data set which comprises a training set and a prediction set, b, executing a random forest classification algorithm for finding a latent data traffic package improving user as a target user, and c, ending; 2), a data traffic package recommendation period comprising the process of a, acquiring a processed generated prediction set, b, executing a K-means clustering algorithm for obtaining a slightly similar user cluster, c, obtaining the target user obtained in the process 1)-b, d, executing a TopN recommendation algorithm on the target user in a same cluster according to a similarity function of the user, and e, ending. The mobile data traffic package recommendation algorithm is used for finding the latent user with a latent data traffic improvement requirement according to data mining technology and executing a recommended plan on the user. Compared with a traditional method, the mobile data traffic package recommendation algorithm has advantages of higher accuracy, higher efficiency, simple realization, low cost, etc.

A decision support system includes a measurement device configured to continuously measure a physiological parameter of a patient. An insulin delivery device provides insulin to the patient per an initial basal profile and the parameter measurements. A storage device holds historical data of insulin delivery to the patient. A processor determines deviations of the delivery of insulin from the basal profile for one or more time period(s) using the historical data, computes a respective first basal-profile adjustment for each of the one or more time period(s) using the determined deviations, and annunciates the computed first basal-profile adjustment(s). A method of recommending a basal-rate adjustment includes measuring the parameter, infusing the patient with insulin and storing the historical data, determining the deviations from the basal profile, computing the first basal-profile adjustments, and annunciating the computed first basal-profile adjustment(s).

The invention discloses a method for friend recommendation based on similar associations in a social network and belongs to the field of data mining and information retrieval. Social associations in the social network are found through social information of users, a rule for calculating association similarity is raised from the two aspects of the structure and the attribute, the users are classified through the topological structure of user nodes in the associations, the roles of the nodes in the network are defined by analyzing the roles and the status of the user nodes in the belonging associations and other associations, and the users of the same type or various types corresponding to the similar associations are recommended to the users of various types in the target association to serve as friend recommendation sets. By the adoption of the method, traditional similarity calculation between single users is expanded into similarity calculation between the associations, and the traditional friend recommendation operation between the users is expanded into the operation that the users are classified through role discovery and the users of the same type or various types are recommended to the users of various types in the target association. The method can be widely applied to the related fields of the social network, electronic commerce and the like.

The invention discloses an electronic book recommendation method, electronic equipment and a computer storage medium. The electronic book recommendation method is used for recommending electronic books to a first user. The method comprises the steps of searching for at least one second user similar to attributes of the first user; determining an electronic book set used for complementary recommendation according to reading history data of at least one second user; selecting the electronic books from the electronic book set to be recommended to the first user. As can be seen, considering similarities among attributes of the similar users, provided is the novel electronic book complementary recommendation method in which the electronic books are screened from the reading history data of thesimilar users to be recommended to the first user, and the types of the recommended electronic books are richer.

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.

An apparatus, system and method for a neural engine for recommending picks in an on-line fantasy sport draft and in on-line fantasy trades. The apparatus, system and method may include at least one computing memory having associated therewith a plurality of rules, wherein at least ones of the plurality of rules include historical performance information for at least two fantasy sports teams in at least two fantasy sports leagues, wherein one of the at least two fantasy sports leagues is associated with the on-line fantasy sports draft, and expert information regarding the picks in the on-line fantasy sports draft. The present invention may additionally include at least one computer processor that applies ones of the plurality of rules drawn from the at least one computing memory to arrive at a recommendation.

A system, method, and computer program product discover relationships among items and recommend items based on the discovered relationships. The recommendations provided by the present invention are based on user profiles that take into account actual preferences of users, without requiring users to complete questionnaires. An improved binomial log likelihood ratio analysis technique is applied, to reduce adverse effects of overstatement of coincidence and predominance of best sellers. The invention may be used, for example, to generate track lists for a personalized radio station.

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.