189 results about "Degree of interest" patented technology

A system is described for displaying images of a virtual three-dimensional book having one or more virtual pages. The system comprises a display system capable of executing a display program wherein images of the virtual three-dimensional book are produced on the display system. A degree of interest (DOI) function is provided for allocating available space on the display system. A page selection function may provide a user-selectable scale along a long axis of the virtual book for selecting virtual pages. Virtual chapter tabs may also be provided for navigating within the virtual book. The display system may also include a page flipping function with several different page flipping speeds and several different page flipping animations for moving between virtual pages in the virtual book. Moreover, a scroll bar function provided to facilitate page navigation wherein space on the scroll bar is allocated using a DOI function. Finally, a computer program product comprising a computer usable medium having computer readable code embodied therein for producing images of a virtual three-dimensional book is also disclosed.

The invention discloses a method for recommending personalized information, which is suitable for the wireless Internet. The method comprises the following steps of: recording user behaviors of accessing the wireless Internet and transmitting to a server by using a behavior recording module on a mobile phone; analyzing the contents of pages accessed by a user to obtain a page set interesting to the user; analyzing the user behaviors of accessing the pages to obtain the interestingness of the user for the pages; combining the interested page set and the interestingness of the pages to establish a user interest model; and carrying out dimensional simplification to a user term matrix and calculating the similarity of term sets to generate recommended personalized information. By adopting the technical scheme, the method can automatically record and analyze the user behavior and excavate user attributes and preferences to realize the matching of the user attributes and information contents and actively recommend the information to the user.

A method and apparatus for selecting additional content to display to a user when the user requests base content is provided. A user profile of the user having user interest scores of categories or keywords is received, each user interest score reflecting the degree of interest the user has in the category or keyword. Performance scores reflecting the probability that a user having particular user interest scores will select additional content associated with particular categories or keywords is also received. In addition, revenue amounts associated with each category or keyword of the user profile is received. The user interest scores, performance scores, and revenue amounts are used to produce an expected revenue amount for each category or keyword in the user profile. Additional content to be sent to the user is then selected using the determined expected revenue amounts.

A system and method are disclosed for providing metric recommendations by a cloud event log analytics system. The log analytics system includes a user interface which allows users to view metric recommendations, view, modify, annotate, delete, or create log metrics. In a first embodiment, centroid vectors are created from metadata associated with user access of log metrics. The centroid vectors are compared to metrics vectors created from log metrics and the results are ranked and provided to users as metric recommendations. In a second embodiment, classification rules are inferred for metric matrix tables containing metadata about log metric usage. Classification rules are assigned to a decision tree used to calculate composite probabilities of interest of log metrics. A recommendation matrix incorporate the composite probabilities of interest to predict the degree of interest an analytics user may have in a log metric for a given role.

The invention discloses a user interest modeling method based on user browsing behavior, comprising the steps of constructing a user interest model in an apparent mode and updating the user interest model in a concealed mode. The step of constructing the user interest model in the apparent mode is a process of primarily establishing and initializing the user interest model through user registration, and the step of updating the user interest model in the concealed mode is realized by analyzing and researching the user access preference according to the condition that users accesses Web pages under the condition of no need of user participation. By using the method, new interests of users can be automatically discovered, and feature items with low interestingness in the user interest model can be eliminated. Therefore, one the one hand, the interest change of users can be better monitored, and on the other hand, the unlimited increase of the user interest model can be controlled in time, and the stability of the interest model is improved.

An interest degree estimating device, which estimates a degree of interest of a user in a video displayed on a screen, includes: an eye gaze detecting unit for detecting an eye gaze direction of the user; a saliency information obtaining unit for obtaining saliency information on a saliency area having salient attractiveness in the video; and a user response analyzing unit for calculating a correlation between the saliency area specified from the obtained saliency information and the detected eye gaze direction, and estimate the degree of interest of the user in the video, the degree of interest being higher as the calculated correlation is higher.

Described are a system and method for notifying electronic meeting participants of information of interest. For an electronic meeting, a relationship is determined between a first participant and one or more second participants having a relationship with the first participant. A degree of interest by the second participants of a portion of the electronic meeting is determined. A notification is generated for the first participant in response to the determined degree of interest.

The present invention provides a method for estimating a user's interest degree in a playing program, the method includes the steps of: monitoring the user's behavior on the program including at least two segments, and each of them correspond to different time weight; determining the length and the time weight of the segment played corresponding to the user's behavior; and acquiring the user's interest degree in the program according to the length and the time weight of segment played.

The invention discloses a video recommendation method and system. The method comprises the steps that an initial score of a user on a video is calculated according to the length of time of the video and the length of time spent by the user in watching the video; the initial score is standardized in a minimum-maximum standardization mode to obtain a standardization score of the user on the video; according to the difference value between a current moment and the moment when the user watches the video last time, calculating a final score of the user on the video. According to the video recommendation method and system, the influences of the playing time of the video, the user watching frequency and time interval factors on user interestingness are comprehensively considered, and the final recommendation quality is improved.

The invention relates to a commodity recommendation method and device based on an electronic business platform and a server. The commodity recommendation method and device based on the electronic business platform and the server are used for improving the accuracy of individual commodity recommendation. The method comprises the steps that adhesive user groups are screened; user interestingness is acquired; a neighbor set closest to a target user is generated; a commodity recommendation list is generated. According to the technical scheme, the obtained adhesive user groups can be more accurate and efficient; due to the fact that only the interestingness of each user in the adhesive user groups is recorded in an obtained scoring matrix, an evaluation matrix can be filled, and the density of the scoring matrix is improved; the closest neighbor set between the target user and the other users in the adhesive user groups is calculated through the scoring matrix, the similar neighbors of the target user can be searched for more accurately, and then the accuracy of recommending commodities to the target user is improved.

The invention discloses a method for personalized meta-search, which comprises the following steps: establishing an interest database in advance; extracting a use log from the interest database according to a determined keyword to be searched and preprocessing search results returned by a search engine; using the use log to calculate the preprocessed search results and the interest of uniform resource locators (URL) corresponding to the searched keyword in the interest database, and ordering and displaying according to calculation results; and updating the interest database. The invention additionally discloses an application terminal for personalized meta-search. By adopting the method and the application terminal, users can obtain more pertinent and satisfactory search results and the user experience is improved.

The present invention provided a method for updating user profile, and the user profile includes like-degree of the user for at least one content feature. The method includes the following steps of: (a).monitoring the user's behavior to a playing program; (b).acquiring a interest degree of the user for the program according to the behavior to the predetermined content features of the program; (c).adjusting contains like degree and weight of a the interest degree correspondingly according to the like degree for the corresponding content feature in the user profile; (d).modifying the like degree in the user profile for the corresponding content feature according to the adjusted interest degree to the predetermined content features of the program.

The invention provides a method and a system for intelligent push of an individualized advertisement of a retail store. The method comprises the steps of acquiring a face image of a customer in a viewregion of an advertisement putting device; carrying out characteristic extraction on the face image to obtain characteristic information of a gender and an age of the face of the customer in the viewregion; and pushing a product advertisement with a highest comprehensive attention index corresponding to the characteristic information of the face of the customer by the advertisement putting device. The comprehensive attention index is formed by fusing a product transaction index, a product category attention index and a product advertisement attention index; each index is obtained by collecting images with cameras at multiple places and dynamically analyzing the images with a computer; and the indexes are updated at set intervals. According to the method and the system for intelligent push of the individualized advertisement of the retail store, a customer group is divided according to age and gender; the product advertisement with the highest attention is put according to the type ofcustomers; a putting strategy summarizes various types of information such as long-term purchase behaviors of customers, the interestingness to the product and the advertisement and the like; the advertisement putting effect is effectively improved, and the comprehensive attention index of the product can be updated according to the behaviors of the customers.

The invention discloses a live broadcast room recommending method and system based on weighted k nearest neighbor scores and relates to the field of popularization of live broadcast rooms. The method comprises the steps of obtaining a live broadcast room watching vector of each user in a live broadcast platform; calculating a similarity between every two users according to the live broadcast room watching vectors; selecting the appointed number of neighbor users for each user according a similarity descending sort method; calculating degree of interest of each user for each watched live broadcast room according to the similarities and the neighbor users of each user; and selecting the appointed number of recommended live broadcast rooms for each user according to a degree of interest descending sort method. According to the method and the system, the recommended live broadcast rooms are evaluated and selected according to the degree of interest calculated according to the similarities; the recommended live broadcast rooms are more consistent with the hobbies, interests and individual demands of the users; the recommending accuracy is relatively high; and the recommending effect is relatively good.

A server delivering the degree of interest in broadcast programs and a method thereof and a computer program of the method are provided. The server utilizes information, which is being transmitted and has topics on the Internet, and delivers the information to users who use the information when the users select a broadcast program. Further, the server delivers a message list including reference information for programs to the users. With this, the users can easily collect the reaction of other users for programs. The server provides a message storing means for storing messages supplied from viewers, a message counting means for counting the number of messages for each program and stores the counted number of message for each program in a database of the degree of interest in broadcast programs. And the server delivers the number of message for each program, as the degree of interest in each program, to the users by obtaining from the database of the degree of interest in broadcast programs.

The invention specifically relates to an individualized learning-oriented educational resource recommendation method and system. The method comprises the following steps of: S1, acquiring basic characteristic data of learners and establishing a resource interestingness model and a learning style model of the learners according to the basic characteristic data, wherein the basic characteristic data comprises identity information, learning behavior records and educational resource operation records of the learners; S2, establishing a learner characteristic model according to the resource interestingness model and the learning style model; S3, calculating the similarity between different learners or the similarity between different educational resources according to the learner characteristic model; and S4, generating a target learner-oriented educational resource recommendation result according to the similarity between different learners or the similarity between different educational resources. Through the technical scheme provided by the invention, the target learner-oriented education resource individualized recommendation can be realized.

A method and apparatus is disclosed herein for enhancing accuracy of jumping by incorporating interestingness estimates. In one embodiment, the method comprises serially displaying a sequence of page images; receiving a user input to stop sequencing through the page images; determining a location of a page image in the sequence of page images representing a point at which the user intended to stop in the sequence of page images but for display speed of the sequence of images and user reaction time in providing an indication of the point at which the user intended to stop, the location being selected based on visual distinctiveness of the page image at the location and the reaction time associated with the user; and automatically jumping back to the page image at the location in the sequence of page images.

The invention provides a large-scale mixed heterogeneous storage system-oriented node fault prediction system and method. A time sequence-based association rule mining algorithm is adopted to construct a node fault prediction system architecture, and a main process of node fault prediction includes: collecting state data and log information of each storage node; carrying out data preprocessing, and generating sequence modes on the basis of a sliding window; using the sequence modes and fault sequences, which are extracted in a fault identification process, together as input of an association rule algorithm, and outputting output results as typical fault sequences; carrying out matching on the typical fault sequences and sequence modes generated in real time; and if a matching result meetsan established rule, issuing early warning to notify a system administrator, and giving feedback to a prediction result by the administrator according to a subjective interest degree. According to thesystem and method, real-time online fault prediction is carried out for nodes of a large-scale mixed heterogeneous storage system, and accuracy and recall which are better than those of existing fault prediction algorithms and better scalability can be obtained.

A data fusion based video program recommendation method comprises the following steps: 1) collection of behavioral data of users: collecting the behavioral data of the users in browsing video websites and storing the behavioral data in a database; 2) data fusion: converting the behavioral data of a user in browsing a video program into the interest value of the user in the video program; and 3) program recommendation: according to the interest value of the user in a few programs, recommending the programs in which the user has high interest value to other users having the same favorites, wherein the behavioral data in browsing comprise behavioral data in playing. The duration ratio r is obtained by dividing the duration of the programs played by the users by the total duration of the programs. The higher the duration ratio r is, the higher the interest value is. As long as the users play the programs and other behavioral data of the users related to the interest value are fused, the program lists to be recommended can be generated more accurately and the degree of satisfaction of the users with the recommended programs is greatly 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.