TELEVISION PROGRAM RECOMMENDER WITH INTERVAL-BASED PROFILES FOR DETERMINING TIME-VARYING CONDITIONAL PROBABILITIES

摘要

A method and apparatus are disclosed for generating television program recommendations based on a time-windowed viewing history of a viewer. Changes in the viewing preferences are automatically identified. The viewing history profile is maintained as a series of viewing history windows. The length of each individual window, and the overall length of the entire viewing history itself (i.e., the number of such smaller viewing windows) can be fixed or varied. Insignificant or non-reoccurring features that can be deleted from the viewer profile without losing significant information are also identified. A feature can be deleted from the viewer profile when the corresponding frequency count for the feature fails to exceed a minimum threshold for a predefined period of time. The disclosed television programming recommender adapts the generated television program recommendations to changes in viewing preferences. The conditional probability of a feature (the likelihood that programs in a known class, e.g., the class of programs watched, will have the feature) is calculated as a function of time. For cyclical or periodic changes in viewing preferences, the disclosed television programming recommender adjusts the conditional probability of an attribute so that it more accurately reflects one or more similar earlier cycles or periods. For trends or permanent changes in viewing preferences, the television programming recommender generates television program recommendations using the most recent window(s) of the viewing history, which are more likely to reflect the trend (current viewing preferences), or extrapolates a value from an identified trend.