144 results about "Item Collection" patented technology

Information presentation system comprises a computer device having a display terminal for providing a graphical user interface (GUI), capable of generating a cluster of items comprising graphical or textual elements for display thereof according to a fractal appearance. Each item in a cluster represents information having a degree of relatedness with information represented by other items in a cluster. A control mechanism is provided for controlling the spatial extent, nesting and arrangement of items within a cluster according to a specified criteria, which spatial extent and arrangement is controlled to provide optimal display and conserve screen display space on the display terminal. Users may manually enter a criteria for organizing and adjusting the spatial extent of a cluster in order to provide continuous display space on the computer display terminal. The invention may be implemented in text editors to provide a multiresolution annotation feature for portable and lap / palm-top computer devices having pen-based or keyboard based inputs.

The present invention relates to an improved order fulfillment system. The system is provided with improved data entry system for selecting items for purchase by a customer, and an improved item collection terminal and order delivery system. The portable terminal to be used for collecting of items is provided with an audio as well as video presentation means which are used to provide assistance to the to terminal user.

An improved user interface and method are provided for presenting recommendations to a user when the user adds an item to a shopping cart. In response to the shopping cart add event, a page generation process generates and returns a page that includes a recommendations portion and a condensed view of the shopping cart. The recommendations portion preferably includes multiple recommendation sections, each of which displays a different respective set of recommended items selected according to a different respective recommendation or selection algorithm (e.g., recommendations based on shopping cart contents, recommendations based on purchase history, etc.). The condensed shopping cart view preferably lacks controls for editing the shopping cart, and lacks certain types of product information, making more screen real estate available for the display of the recommendations content. A link to a full shopping cart page allows the user to edit the shopping cart and view expanded product descriptions.

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.

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 relates to a recommendation system and a method. The system comprises a determination module of a set to be recommended, a score forecasting module, a recommendation generation module and a recommendation control module. The method comprises the steps: a neighbor project set of scored project of target users and the scored project set of the neighbor users of the target users are obtained, and then the intersection of the neighbor project set and the project set is calculated so as to obtain the project set to be recommended; a forecasting score, made by the target users, of each project in the project set to be recommended is acquired; relevant project is recommended to the target users according to the forecasting scores. By taking projects and users into comprehensive consideration, various factors are considered more comprehensively, thus improving the accuracy of project recommendation.

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.

A method and apparatus for selecting items from a collection of items are indexed by a list of item identifiers. The item identifiers may be in the form of text, symbols, graphics, etc. An initial display is generated which includes on or more parts of the item identifiers. Selection of the one or more parts may be made and results in the generation of a display of further one or more parts for selection. The further one or more parts may be selected in order to the selected on or more parts to build a larger part or whole of an item identifier. Accordingly, selection from a large list of item identifiers may be carried out in a relatively short time period.

Methods and apparatus for a recommendation system based on collaborative filtering is provided. Explicit and implicit ratings of items by network users are used to create a contextual model. The explicit ratings comprise different rating types regarding different item attributes. The implicit ratings comprise different rating types derived from different user events and may include recency, intensity, or frequency ratings. The contextual model may be optimized for a specific objective function, such as click-through-rate or conversion rate. In other embodiments, item information is used to produce a content model where item information for an item is encoded as metadata into a document that represents the item. The contextual or content model is used to recommend one or more items to a current user. The basic unit of the recommendation system may be an item set of two or more items or a particular sequence of two or more items.

A method (and system) for associating an item with only less than ten categories from a collection of more than hundred categories. Each category includes a model, which determines a process. The process determines a degree of closeness to a set of features of items. The method includes receiving an item (e.g., document); and determining a set of features (e.g., word, number, author) associated with the item. The method also uses the set of features with two or more processes in respective categories out of the collection of more than one hundred categories to determine how well the set of features matches the two or more categories. A step of forming a blended model from two or more categories to derive a blended process is included. The blended process yields a closer fit to the set of features of the item than that of any single process of a category from the collection of categories. The method associates the item to each of the two or more categories that were used to form the blended process.

Methods and apparatus for a recommendation system based on collaborative filtering is provided. Explicit and implicit ratings of items by network users are used to create a contextual model. The explicit ratings comprise different rating types regarding different item attributes. The implicit ratings comprise different rating types derived from different user events and may include recency, intensity, or frequency ratings. The contextual model may be optimized for a specific objective function, such as click-through-rate or conversion rate. In other embodiments, item information is used to produce a content model where item information for an item is encoded as metadata into a document that represents the item. The contextual or content model is used to recommend one or more items to a current user. The basic unit of the recommendation system may be an item set of two or more items or a particular sequence of two or more items.

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.

Methods and apparatus for a recommendation system based on collaborative filtering is provided. Explicit and implicit ratings of items by network users are used to create a contextual model. The explicit ratings comprise different rating types regarding different item attributes. The implicit ratings comprise different rating types derived from different user events and may include recency, intensity, or frequency ratings. The contextual model may be optimized for a specific objective function, such as click-through-rate or conversion rate. In other embodiments, item information is used to produce a content model where item information for an item is encoded as metadata into a document that represents the item. The contextual or content model is used to recommend one or more items to a current user. The basic unit of the recommendation system may be an item set of two or more items or a particular sequence of two or more items.

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.

Methods and apparatus for a recommendation system based on collaborative filtering is provided. Explicit and implicit ratings of items by network users are used to create a contextual model. The explicit ratings comprise different rating types regarding different item attributes. The implicit ratings comprise different rating types derived from different user events and may include recency, intensity, or frequency ratings. The contextual model may be optimized for a specific objective function, such as click-through-rate or conversion rate. In other embodiments, item information is used to produce a content model where item information for an item is encoded as metadata into a document that represents the item. The contextual or content model is used to recommend one or more items to a current user. The basic unit of the recommendation system may be an item set of two or more items or a particular sequence of two or more items.

A tag recommendation for an item to be tagged is generated by: selecting a set of candidate neighboring items in an electronic social network based on context of items in the electronic social network respective to an owner of the item to be tagged; selecting a set of nearest neighboring items from the set of candidate neighboring items based on distances of the candidate neighboring items from the item to be tagged as measured by an item comparison metric; and selecting at least one tag recommendation based on tags of the items of the set of nearest neighboring items. The item comparison metric may comprise a Mahalanobis distance metric trained on the set of candidate neighboring items to correlate the trained Mahalanobis distance between pairs of items of the set of candidate neighboring items with an overlap metric indicative of overlap of the tag sets of the two items.

Methods and apparatus for a recommendation system based on collaborative filtering is provided. Explicit and implicit ratings of items by network users are used to create a contextual model. The explicit ratings comprise different rating types regarding different item attributes. The implicit ratings comprise different rating types derived from different user events and may include recency, intensity, or frequency ratings. The contextual model may be optimized for a specific objective function, such as click-through-rate or conversion rate. In other embodiments, item information is used to produce a content model where item information for an item is encoded as metadata into a document that represents the item. The contextual or content model is used to recommend one or more items to a current user. The basic unit of the recommendation system may be an item set of two or more items or a particular sequence of two or more items.

The invention discloses a recommendation method based on singular value decomposition and classifier combination. The recommendation method comprises the steps of computing average score and probability distribution of an item through data preprocessing; training a singular value decomposition model through a stochastic gradient descent method, computing an entropy set of a scored item set of the user in item classification through a computing method of entropy, and determining an uncertainty critical value of the item; and comparing and predicting uncertainty and critical value of the item to determine whether to use a classifier, and recommending N items with highest scores in all non-scored items of the user through a Top-N method. According to the method, individual recommendation is produced on the basis of analysis of historical score data of the user; predicting score of a designated item i is acquired through a singular value decomposition algorithm, information entropy of the item for each user is calculated so as to determine whether to classify, and final prediction score of the item is acquired through the classifier, so that the accuracy of the recommendation method is improved.

The application discloses a user involving project recommendation method. The method comprises: acquiring a project set to be recommended to target users; acquiring a user set formed by users that have been involved in projects in the project set to be recommended to the target users; acquiring an influence value of each user in the user set ; according to projects involved in participation behaviors of the users, acquiring similarity degree between each user of the user set and the target users; considering the influence value of each user in the user set and the similarity degree between each user of the user set and the target users, calculating predicted values of degree of interest the target users have on each project in the project set to be recommended to the target users; and considering the predicted values, selecting at least one project from the project set to be recommended to the target users, and recommending the selected at least one project to the target users. According to the user involving project recommendation method and apparatus, the recommendation accuracy and the quality of recommended projects are improved, and the recommended projects are higher in representativeness and effectiveness.

A rules-based software architecture that provides the infrastructure for the definition and execution of end-user rules logic. This enables end-user automation of data in a unified storage platform using simple IF-THEN rules. The architecture includes a monitoring component that tracks items associated with the data, and a rules component interfaces to the rules component, and employs metadata associated with the tracked items to provide for automated handling of a subset of the items. The system further provides for defining virtual collections and items in the system using content based logic. The system further comprises one or more trigger components that sets items and collections of items dynamically active as a function of trigger logic. Additional components can include a constraint component for imposing constraint logic on items, and a decision component that supports enabling application customization logic at decision points.

A computer implemented method is performed at an electronic device adapted to receive user input and having a display. The method comprises displaying a subset of a set of items in a first display area of the display, maintaining a first marker corresponding to a position of the currently displayed subset within the set of items, receiving user input to navigate through the set of items, receiving a user initiated indication of an item within the set of items to be allocated a marker, maintaining a second marker corresponding to the position of the indicated item within the set of items, and notifying the user of the position of the indicated item during subsequent navigation through the set of items. An electronic device and computer program product are also provided.

The present invention is directed towards methods and computer readable media for annotating and ranking user reviews on social review systems with inferred analytics. A reference framework is provided by creating context according to previous activity, bias, or background information of a given reviewer. The method of the present invention comprises receiving a first query identifying a given content item, generating a collection of content items based on one or more identical objective attributes associated with the given content item, identifying one or more subjective attributes associated with a given item in the collection of items, and providing a reference framework to interpret the subjective attributes associated with each item in the collection.

The embodiment of the invention discloses a method and device for displaying virtual items. The method comprises the steps that the state of a virtual combined item collection is detected, and the virtual combined item collection comprises at least one virtual item; when the state of the virtual combined item collection is detected to be the activated state, animation data corresponding to the virtual combined item collection are acquired; the animation data are sent to at least one target client end to be played. Through the method and device for displaying the virtual items, the display content of the at least one virtual item can be enriched, and the interactivity with a user is provided.

The present invention is directed towards methods and computer readable media for annotating and ranking user reviews on social review systems with inferred analytics. A reference framework is provided by creating context according to previous activity, bias, or background information of a given reviewer. The method of the present invention comprises receiving a first query identifying a given content item, generating a collection of content items based on one or more identical objective attributes associated with the given content item, identifying one or more subjective attributes associated with a given item in the collection of items, and providing a reference framework to interpret the subjective attributes associated with each item in the collection.

The invention discloses a speech quality detection method and device, which relates to the technical field of speech detection, and mainly aims at improving the comprehensiveness and accuracy of speech quality detection. The main technical scheme of the invention is characterized by converting the speech to be quality-checked into the text to be quality-checked; extracting quality inspection itemsone by one from a quality inspection item collection, wherein the quality inspection items are used for detecting whether the text to be inspected conforms to requirements; using a pre-trained machine learning model to detect whether the text to be inspected contains content matching with the quality inspection item; according to the matching result and the detection rule corresponding to the quality inspection entry, determining whether the speech to be inspected is qualified or not. The invention is used for the normative detection of the voice data content.

Classification of collections of items such as words, which are called “document classification,” and more specifically explaining a classification of a document, such as a web-page or website. This can include exemplary procedure, system and / or computer-accessible medium to find explanations, as well as a framework to assess the procedure's performance. An explanation is defined as a set of words (e.g., terms, more generally) such that removing words within this set from the document changes the predicted class from the class of interest. The exemplary procedure system and / or computer-accessible medium can include a classification of web pages as containing adult content, e.g., to allow advertising on safe web pages only. The explanations can be concise and document-specific, and provide insight into the reasons for the classification decisions, into the workings of the classification models, and into the business application itself. Other exemplary aspects describe how explaining documents' classifications can assist in improving the data quality and model performance.