Abstract: A system having a video display screen that provides video to a user. The position of the display screen is adjustable based upon the location of the user with respect to the display screen. The system includes at least one image capturing device trainable on a viewing region of the display screen and coupled to a control unit having image recognition software. The image recognition software identifies the user in an image generated by the image capturing device. The software of the control unit also generates at least one measurement of the position of the user based upon the detection of the user in the image.

Abstract: A system, method, and article of manufacture is disclosed suitable for displaying selectable time orderable options, such as television programs available for viewing on a television, using a tunnel interface. The tunnel interface displays concentric rings where each ring represents a different set of option data whose attributes are modified to reflect a user's preferences. The modified option data are further arranged such that each concentric ring is ordered by time. Additionally, choices within each ring are visually distinguishable by user preference. Users can navigate within and between the concentric rings and select one or more of the available options using the concentric rings.

Abstract: An apparatus and method for recommending a schedule of events to a user is disclosed. In the preferred embodiment of the system and method, each channel schedule is broken down into time slices. A novel fuzzy-now recommendation-time value is calculated for each time slice. This fuzzy-now recommendation-time value is a two dimensional value measured in units of recommendation-time, or “enjoyment minutes”. By means of the calculated fuzzy-now recommendation-time values, recommended schedules may be generated using a wide variety of selection methods.

Abstract: A method and apparatus are disclosed for recommending items of interest to a user, such as television program recommendations, before a viewing history or purchase history of the user is available. A third party viewing or purchase history is processed to generate stereotype profiles that reflect the typical patterns of items selected by representative viewers. A user can select the most relevant stereotype(s) from the generated stereotype profiles and thereby initialize his or her profile with the items that are closest to his or her own interests. A clustering routine partitions the third party viewing or purchase history (the data set) into clusters using a k-means clustering algorithm, such that points (e.g., television programs) in one cluster are closer to the mean of that cluster than any other cluster.

Abstract: A method for providing previous selection information to a user is provided that includes generating a list of possible selections based on a selection request received from the user. A selection history table is accessed to identify previous selections by the user. A determination is made regarding whether a selection in the list of possible selections matches a previous selection. The user is informed when a determination is made that a selection in the list of possible selections matches a previous selection.

Abstract: A method and apparatus are disclosed for recommending items of interest to a user, such as television program recommendations, before a viewing history or purchase history of the user is available. A third party viewing or purchase history is processed to generate stereotype profiles that reflect the typical patterns of items selected by representative viewers. A user can select the most relevant stereotype(s) from the generated stereotype profiles and thereby initialize his or her profile with the items that are closest to his or her own interests. A clustering routine partitions the third party viewing or purchase history (the data set) into clusters using a k-means clustering algorithm, such that points (e.g., television programs) in one cluster are closer to the mean of that cluster than any other cluster. The value of k is incremented in accordance with a measure of cluster compactness.

Abstract: The present invention provides a method, system and program product for locally analyzing (television) viewing behavior. Specifically, under the present invention, a single time interval of viewed programs is chunked into multiple time windows of viewed programs. Then, for each program within each time window, a conditional probability is calculated. The conditional probabilities are then compared to a noise threshold to determine recommended programs for each time window. The recommend programs can be added to a user profile and/or outputted to the viewer.

Abstract: In customizing a user profile employed by a recommendation system, users are prompted for feedback regarding content that is the subject of the recommendation system. Only feedback that does not degrade performance of the recommendation system, as measured by the error rate, is accepted and utilized to modify the user profile. Feedback that would degrade performance is discarded without being employed to alter the user profile. In this manner, error is continually driven toward a minimum by system changes based on feedback.

Abstract: Possible initial cluster sets for a clustering process deriving stereotypes from a sample population of viewing histories are compared by computing, for each candidate initial cluster set, a metric relating to the distance of each cluster within the candidate initial cluster set to every other cluster within the candidate initial cluster set. The metric, which is preferably a normalized average aggregate of the distances between clusters within a candidate initial cluster set, is then utilized to discard inferior candidates having clusters that are too close to each other.

Abstract: A method, system and program product for populating a user profile based on existing user profiles is provided. Specifically, under the present invention, base characteristics are designated for a new user profile. Based on the designated characteristics, the new user profile is associated with a particular Vornoi cluster region of existing user profiles. Once associated, the new user profile is populated with defined characteristics from the existing user profiles within the particular cluster region. After population, viewing recommendations can be made.

Abstract: A method and apparatus are disclosed for recommending items of interest to a user, such as television program recommendations. According to the principals of the invention, initial recommendations, which may be generated before a viewing history or purchase history of the user is available, are adapted or transformed to better capture a users viewing behavior using a feedback process. In particular, stereotypes are generated, which are used to build a stereotypical profiles. Stereotypical profiles are then generated that reflect the typical patterns of items selected by representative viewers. Recommendations are computed against a ground truth data using the stereotypical profiles, wherein distances are computed between each show in a so called ground truth data with the centroid of each stereotype in the stereotypical profile.

Abstract: A process for adaptive bookmarking of often-visited web sites, comprising the steps of (a) optionally determining the identity of a particular user, (b) determining whether a webpage has been detected, (c) if the webpage in step (b) has been detected, determining whether the webpage has been previously visited by a particular user, (d) performing one of (i) creating an initial record of the webpage visit by the particular user if it has been determined in step (c) that the webpage has not been previously visited by the particular user, and (ii) determining whether the webpage has been previously bookmarked if it has been determined in step (c) that the webpage has been previously visited by the particular user, (e) updating a visitation count if it has been determined in step (c) that the webpage has been previously visited by the particular user, (f) determining whether the visitation count has reached a predetermined threshold; and (g) recommending the bookmarking of the address of the webpage if it determi

Abstract: A method and apparatus are disclosed for recommending items of interest to a user, such as television program recommendations, before a viewing history or purchase history of the user is available. A third party viewing or purchase history is processed to generate stereotype profiles that reflect the typical patterns of items selected by representative viewers. A user can select the most relevant stereotype(s) from the generated stereotype profiles and thereby initialize his or her profile with the items that are closest to his or her own interests. A clustering routine partitions the third party viewing or purchase history (the data set) into clusters using a k-means clustering algorithm, such that points (e.g., television programs) in one cluster are closer to the mean of that cluster than any other cluster. A mean computation routine computes the symbolic mean of a cluster.

Abstract: A method and apparatus are disclosed for recommending items of interest to a user, such as television program recommendations, before a viewing history or purchase history of the user is available. A third party viewing or purchase history is processed to generate stereotype profiles that reflect the typical patterns of items selected by representative viewers. A user can select the most relevant stereotype(s) from the generated stereotype profiles and thereby initialize his or her profile with the items that are closest to his or her own interests. A clustering routine partitions the third party viewing or purchase history (the data set) into clusters, such that points (e.g., television programs) in one cluster are closer to the mean of that cluster than any other cluster. A distance computation routine evaluates the closeness of a television program to each cluster based on the distance between a given television program and the mean of a given cluster.

Abstract: A method and apparatus are disclosed for recommending items of interest to a user, such as television program recommendations, before a viewing history or purchase history of the user is available. A third party viewing or purchase history is processed to generate stereotype profiles that reflect the typical patterns of items selected by representative viewers. A user can select the most relevant stereotype(s) from the generated stereotype profiles and thereby initialize his or her profile with the items that are closest to his or her own interests. A clustering routine partitions the third party viewing or purchase history (the data set) into clusters using a k-means clustering algorithm, such that points (e.g., television programs) in one cluster are closer to the mean of that cluster than any other cluster.

Abstract: A method and apparatus are disclosed for recommending items of interest to a user, such as television program recommendations, before a viewing history or purchase history of the user is available. A third party viewing or purchase history is processed to generate stereotype profiles that reflect the typical patterns of items selected by representative viewers. A user can select the most relevant stereotype(s) from the generated stereotype profiles and thereby initialize his or her profile with the items that are closest to his or her own interests. A clustering routine partitions the third party viewing or purchase history (the data set) into clusters using a k-means clustering algorithm, such that points (e.g., television programs) in one cluster are closer to the mean of that cluster than any other cluster. A mean computation routine computes the symbolic mean of a cluster.

Abstract: A method and apparatus are disclosed for recommending items of interest to a user, such as television program recommendations, before a viewing history or purchase history of the user is available. A third party viewing or purchase history is processed to generate stereotype profiles that reflect the typical patterns of items selected by representative viewers. A user can select the most relevant stereotype(s) from the generated stereotype profiles and thereby initialize his or her profile with the items that are closest to his or her own interests. A clustering routine is disclosed to partition the third party viewing or purchase history (the data set) into clusters, such that points (e.g., television programs) in one cluster are closer to the mean of that cluster than any other cluster. A mean computation routine is also disclosed to compute the symbolic mean of a cluster.

Abstract: A data-class recommender, such an electronic program guide that recommends television programs, allows users to modify their implicit profiles using the profiles of other users. For example, if a user likes the programming choices made by a friend's profile, the user can have his/her profile modified by adding parts of the friend's profile to his own, either replacing parts or forming a union of the descriptors that indicated favored classes of data. According to an embodiment, features may be labeled to allow the modifying user to select the specific parts of the friend's profile to use in making the modifications. The labeling may be done based on feature-value scores or categories for which there is a high frequency of cross-correlation with other categories in a description that defines preferred subject matter, such as a specialized description of a version space.

Abstract: A data-class recommender, such an electronic program guide that recommends television programs, avoids users getting trapped in a rut when the users select the same programming material over and over again. In an embodiment, the recommender may be programmed automatically to leverage the profile of another user to broaden the user's profile. For example, the recommender may use the target descriptions of other users in a same household of the user as a guide for broadening the user's profile. Alternatively, the household profile may be used as a filter for source material for soliciting feedback from the user. In this way, rather than simply broadening the user's range arbitrarily, guidance from other profiles, related in some way to the user, is obtained and leveraged. Note that the “relationship” can include friends, published stereotypes representing interests of the user, and others.

Abstract: A program commercial based recommendation system employing a program commercial detection module, a facial estimation module, and a program recommendation module for implementing method for developing a viewing history of a viewer is disclosed. The program commercial detection module detects commercials within a transmission signal. In response to a detection of a program commercial, the facial estimation module generates a facial estimation of a viewer to thereby determine if the viewer is watching or not watching the program commercial. In response to a generation of the facial estimation, the program recommendation module stores the program commercial within a viewing history database. The stored commercial either has a positive rating when the facial estimation indicates the viewer is watching the program commercial or a negative rating when the facial estimation indicates the viewer is not watching the program commercial.