139 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.

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.

A method for managing a group of owned and for-purchase media items in response to a single user action. The method treats the owned media items and the for-purchase media items similarly with respect to the user's interaction with them, facilitating a user paradigm-shift from thinking about two stores of media items to a single store of media items available for use. A media item grouping generic to both types of media items is provided, permitting sharing of media item collections between users having different access rights with respect to the media 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.

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.

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 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 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.

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.