83 results about "Rank score" patented technology

A method and system for interactively and progressively processing and determining customer satisfaction from data gathered within an online community comprises of a group of participants connected via a computer network such as the Internet. A customer satisfaction and experience processing system comprises a participant or user interface (“front interface”), a database, a search engine, a customer satisfaction processing algorithm, an intelligent optimizer, and an optional report generator. The front interface is accessible via the network, thereby allowing any participant to interactively share experiences with respct to listed items (i.e., products or services). The database stores identification information and data pertaining to the participants' satisfaction and experiences with particular items. The algorithm processes the input data and produces ranking scores for individual items. The intelligent optimizer analyzes a participant's individual profile to predict and/or recommend one or more of the items from those offered by many different providers, which are the items most likely to provide the participant with the highest level of satisfaction and the best customer experience. The report generator conducts marketing analysis and produces electronic reports for customers and item providers. The search engine permits retrieval of data from the database upon participants' input queries.

A method and system for assigning ranking scores to Internet users of a networking site and a content site in which the networking site enables each user to become associated with other users as friends and in which the content site enables each user to rate content published by other users includes the following operations. Data indicative of the friends of each user is obtained from the networking site and a network popularity rating is assigned to each user based on this data. Data indicative of the ratings assigned to the published content of the users is obtained from the content site and a content popularity rating is assigned to each user based on this data. A ranking score is assigned to each user based on the network popularity and content popularity ratings of the user. The ranking scores are provided to a third party.

A system and method is disclosed for recommending items to individual users using a combination of clustering decision trees and frequency-based term mapping. The system and method of the present invention is configured to receive data based on user action, such as television remote control activity, or computer keyboard entry, and when a new item data is made available from sources such as television program guides, movie databases, deliverers of advertising data, on-line auction web sites, and electronic mail servers, the system and method analytically breaks down the new item data, compares it to ascertained attributes of item data that a user liked in the past, and produces numeric ranking of the new item data dynamically, and without subsequent user input, or data manipulation by item data deliverers, and is tailored to each individual user. A embodiment is disclosed for learning user interests based on user actions and then applying the learned knowledge to rank, recommend, and/or filter items, such as e-mail spam, based on the level of interest to a user. The embodiment may be used for automated personalized information learning, recommendation, and/or filtering systems in applications such as television programming, web-based auctions, targeted advertising, and electronic mail filtering. The embodiment may be structured to generate item descriptions, learn items of interest, learn terms that effectively describe the items, cluster similar items in a compact data structure, and then use the structure to rank new offerings. Embodiments of the present invention include, by way of non-limiting example: allowing the assignment of rank scores to candidate items so one can be recommended over another, building decision trees incrementally using unsupervised learning to cluster examples into categories automatically, consistency with “edge” (thick client) computing whereby certain data structures and most of the processing are localized to the set-top box or local PC, the ability to learn content attributes automatically on-the-fly, and the ability to store user preferences in opaque local data structures and are not easily traceable to individual users.

A method is provided for determining a ranking score for an image or group of related images among a plurality of images accessible by a search engine, such that the ranking score is usable to determine the order in which the images are presented in the results of a search conducted by the search engine. The method comprises monitoring the number of times the image (or the images in the group of related images) is presented for viewing by predetermined users in the results of searches conducted by the search engine. The level of active interest shown by the predetermined users in the image or images presented for viewing in the search results is then monitored, for example by determining the number of times that users select a thumbnail of the image for viewing on an enlarged scale. Finally a ranking score is assigned to the image or images based on the monitored level of active interest as a proportion of the number of times the image or images are presented for viewing by the predetermined users. This then enables an identified set of images to be ordered in such a manner as to take into account the ranking scores of the images, in order to enable users to access the images that they require more quickly and conveniently.

A question management system for an expert advice web site maintains a database of experts in different subject matter categories. Ranking scores associated with each expert are continually updated based on the timeliness of answers provided by the experts and answer rating feedback received from the question poser. According to another aspect of the invention, method and computer readable medium is disclosed for carrying out the above method.

An information retrieval system is provided for processing queries for video content. A server receives a query for video content and returns video articles, as received from broadcast systems or other content providers. Queries are formulated using text, video images, and/or visual content associated with a video article. Various video-oriented characteristics associated with the results of the queries are determined and a rank score is calculated for each. The ranked video articles are displayed in a representation to the user, from which the user can play the video article either within the representation or independent of it.

The predictive asset ranking system and process quantifies the condition of major components and sub-components of a property based on a systematic comparison of one major component against another utilizing analytical hierarchy analysis. The predictive asset ranking may be expressed as a value or score that may be used as a benchmark to compare one property to another property. The predictive asset ranking process applies multiple analytical hierarchies to the various attributes of a property. The analytical hierarchy may be based on an attribute weighing process and may include a series of qualitative questions indicative of attributes of the property. The predictive asset ranking system may be dynamic and may be customized for the particular asset being evaluated. When the system is customized, the process may automatically recalculate the weights and redistribute the points used in the predictive asset ranking analysis.

An embodiment of the invention provides a searching-ranking model training method and device and a search processing method. The searching-ranking model training method includes: acquiring multiple groups of labeled sample data, wherein each group of the sample data includes search terms and multiple search result entries correspondingly labeled as positive examples or negative examples; generating Gated RNN (recurrent neural network) based input layer, word vector layer, hide layer and output layer of a searching-ranking model, and training the searching-ranking model to learn parameters of the same. By the searching-ranking model training method and device and the search processing method, accuracy of ranking scores of the search result entries acquired by calculating can be improved, and more accurate searching-ranking results are provided for users.

Disclosed is a process for ranking semantic web resources, comprising the steps of; establishing an RDF knowledge base using diverse tools that support the establishment of ontologies; setting, by class, object and subject weights for an object type attribute and a weight for a data type attribute on the schema composed of classes that constitute a domain and of attributes that describe relationships between these classes; extracting from the RDF knowledge base an RDF triple composed of three portions, i.e., a subject, a predicate and an object; creating a weight matrix of class-oriented attributes based on a set weight and the extracted RDF triple; and operating the created weight matrix of class-oriented attributes to calculate a first eigenvector and obtain a vector for ranking scores of resources.

A method for ranking vehicles for sale, the method comprising (1) providing a first database of available vehicles, (2) creating a second database of similar vehicles by identifying vehicles in the first database that are similar to a subject vehicle, and (3) creating a third database of ranked vehicles by determining a vehicle rank score of each of the vehicles in the second database, wherein determining a vehicle rank score of a vehicle comprises (a) calculating a base score of the vehicle, (b) calculating a multiplier of the vehicle, and (c) determining the vehicle rank score of the vehicle by multiplying the base rank by the multiplier.

A system and method for generating a search ranking score for a web page. The system comprises a training data processor effective to receive training data including at least a first page, a first label, a second page and a second label. The system further comprises a feature extraction processor connected to the training data processor, the feature extraction processor is effective to receive the first page, identify first features in the first page and calculate first values relating to the first features; the feature extraction processor is further effective to receive the second page and identify second features and calculate second values relating to the second features. A machine learning processor is connected to the feature extraction processor. The machine learning processor is effective to receive the first features, the first values, the first label, the second features, the second values, and the second label and generate a ranking function for a search engine based on the first features, the first values, the first label, the second features, the second values, and the second label. A receiving processor is connected to the machine learning processor. The receiving processor is effective to receive a web page. A ranking processor is connected to the receiving processor. The ranking processor is effective to apply the ranking function to the web page to generate a score.

A method and system for ranking content and providing a user interface for viewing the content is provided. The content system ranks content in a search result based on a combination of relevance of the content to a query and quality of the content. The content system may derive the quality of content by analyzing ratings provided by various content forums. The content system may use metadata provided by a content forum when searching for content that matches a query. The content system generates a rank score that combines the relevance and quality of the content and ranks the content according to the rank scores.

An adjustable priority retailer ranking system that performs a method for generating an ordered comparison list of merchant sources is disclosed. A first unordered list of merchant sources is received from a queried search engine. A location value of an end user is derived. For the merchant sources in the unordered list, a plurality user independent merchant evaluation factors are retrieved, and a plurality of user-independent merchant evaluation factors are generated based upon the location value. Predetermined weights are assigned to the merchant evaluation factors. A merchant ranking score for the merchant sources are generated from a composite of the weights and the merchant evaluation factors. An ordered list of merchant sources arranged according to the ranking scores is outputted to the end user.

Determining a relevancy ranking score is disclosed. A query is received that includes one or more constraints. One of the one or more constraints includes an operator. A search result based on the query is received. The relevancy ranking score for the received search result is determined based at least in part on the operator associated with the one or more constraints of the query.

A method and system for ranking transaction channels, for enabling transactions between two or more real world identities within one or more networks is provided. The real world identities have various transaction channels associated with them. Relationships between the real world identities and contexts are asserted by the identities, and also inferred by the system. Thereafter, a channel ranking score for each transaction channel is computed, based on the availability of the channel; and contexts and the relationships. The channels are ranked dynamically, and the most appropriate channel for transaction is selected based on the channel ranking scores.

Passages in a digital corpus are scored and ranked based at least in part on characteristics of instances of the passages occurring in the corpus. Such characteristics include the popularity of the author, the characteristics of the words introducing and following the similar passage, frequency of appearance of the passage in the digital corpus, the length of the similar passage, the words of the similar passage, the usage of punctuation with the similar passage, and the diffusion of the similar passage within the digital corpus. The characteristics are scored and weighted to produce ranking scores for the associated passages. The ranking scores are used for purposes including selecting passages to display in association with a document and ranking passages displayed in response to a search.

A method, article, and system for managing document retrieval and ranking, and more particularly to providing a method, article, and system for utilizing not just the explicit metadata of a retrieved document, but also the extracted intrinsic metadata inside the content of the retrieved document, as well as the knowledge of the user-document relationship by relating the document implicit metadata to the user's information on the document's system database, as important parameters in calculating relevance or ranking score for retrieved documents.

The invention discloses a student physical health evaluation management system. The student physical health evaluation management system comprises a user registration module, which is used for inputting login information by a user; a data import module, which is used for acquiring physical health data of corresponding students according to the logined user information; a physique scoring module, which is used for comparing physical health data of the students with the standard of the grade they are in, scoring various indexes, and storing a comparison result in a physique database; a physique evaluation module, which is used for ranking scores according to the scores of the physique scoring module, and automatically generating evaluation languages and suggestions; a statistics module, which is used for making statistics of physical health indexes of a plurality of students according to the gender, grade, region and score; and a display module, which is used for displaying a statistical result at a terminal by means of an electronic map or a statistical chart. The student physical health evaluation management system and the method provided by the invention can generate a personalized physique evaluation report for the student individuals, and provide a group physique report for the relevant administrative departments.

The invention discloses an automatic generating method and device for Internet headlines. The method comprises the following steps: dividing the main body of a piece of news, keeping sentences with the length within a preset length range, and marking the sentences as kept sentences; respectively computing the Similarity (s) between each kept sentence and a headline of the news and the Weight (s) of each kept sentence; computing the rank score of each kept sentence according to a formula that Rank (s) is equal to Weight (s)/Similarity (s); selecting the kept sentence with the highest rank score as the headline, wherein Rank (s) is the rank score of the kept sentence. The method and the device automatically recognize a sentence which can best reflect the value of a piece of news, and take the sentence as the headline of the news.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for producing a ranking for pages on the web. In one aspect, a system receives a set of pages to be ranked, wherein the set of pages are interconnected with links. The system also receives a set of seed pages which include outgoing links to the set of pages. The system then assigns lengths to the links based on properties of the links and properties of the pages attached to the links. The system next computes shortest distances from the set of seed pages to each page in the set of pages based on the lengths of the links between the pages. Next, the system determines a ranking score for each page in the set of pages based on the computed shortest distances. The system then produces a ranking for the set of pages based on the ranking scores for the set of pages.

The invention discloses a zero training sample behavior identification method which comprises the following steps that a characteristic vector of each action video sample is extracted; a plurality of human motion attributes and a relation between action video pairs under each human motion attribute are set; the relations between the action video pairs serve as an input, and are trained by a ranking support vector machine; by using an output ranking score, each category of human behaviors with a training sample is fit, and a Gaussian mixture model is obtained; a Gaussian mixture model of the categories of the zero training sample human behaviors is acquired by transfer learning; a characteristic vector of a test video sample is extracted; and the categories of the zero training sample human behaviors in the test video sample are judged by using a maximum posterior probability principle. By using the fit ranking score of the Gaussian mixture model, a purpose of behavior identification is achieved, and the categories of the behaviors are judged by maximum posterior, so that the robustness of the behavior identification is improved.

A “Ranking Optimizer,” provides a framework for directly optimizing conventional information retrieval (IR) measures for use in ranking, search, and recommendation type applications. In general, the Ranking Optimizer first reformats any conventional position based IR measure from a conventional “indexing by position” process to an “indexing by documents” process to create a newly formulated IR measure which contains a position function, and optionally, a truncation function. Both of these functions are non-continuous and non-differentiable. Therefore, the Ranking Optimizer approximates the position function by using a smooth function of ranking scores, and, if used, approximates the optional truncation function with a smooth function of positions of documents. Finally, the Ranking Optimizer optimizes the approximated functions to provide a highly accurate surrogate function for use as a surrogate IR measure.

The invention provides a zero sample image classification method based on a relative attribute random forest. An attribute ranking score model is built for an image with an unknown class according to the relative relationship between image classes and image attributes, the attribute ranking score models for all images serve as training samples to train a random forest classifier, and finally, according to an attribute ranking score for a tested image and the random forest classifier obtained through training, the label of the tested image is predicted. The method of the invention can realize zero sample image classification and has the advantages of high classification recognition rate, strong model stability and the like.