EP1340179A2 - Systeme de recommandation de programme de television a profils bases sur des intervalles permettant de determiner des probabilites conditionnelles variables dans le temps . - Google Patents

Systeme de recommandation de programme de television a profils bases sur des intervalles permettant de determiner des probabilites conditionnelles variables dans le temps .

Info

Publication number
EP1340179A2
EP1340179A2 EP01991738A EP01991738A EP1340179A2 EP 1340179 A2 EP1340179 A2 EP 1340179A2 EP 01991738 A EP01991738 A EP 01991738A EP 01991738 A EP01991738 A EP 01991738A EP 1340179 A2 EP1340179 A2 EP 1340179A2
Authority
EP
European Patent Office
Prior art keywords
viewing
viewer
occurrences
features
behavior
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP01991738A
Other languages
German (de)
English (en)
Inventor
James D. Schaffer
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Arris Global Ltd
Original Assignee
Koninklijke Philips Electronics NV
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Koninklijke Philips Electronics NV filed Critical Koninklijke Philips Electronics NV
Publication of EP1340179A2 publication Critical patent/EP1340179A2/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04H—BROADCAST COMMUNICATION
    • H04H60/00—Arrangements for broadcast applications with a direct linking to broadcast information or broadcast space-time; Broadcast-related systems
    • H04H60/35—Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users
    • H04H60/46—Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for recognising users' preferences
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
    • H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
    • H04N21/4667—Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
    • H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
    • H04N21/4668—Learning process for intelligent management, e.g. learning user preferences for recommending movies for recommending content, e.g. movies

Definitions

  • the present invention relates to television program recommenders, and more particularly, to a method and apparatus for generating television program recommendations.
  • television viewers identified television programs of interest by analyzing printed television program guides.
  • printed television program guides contained grids listing the available television programs by time and date, channel and title.
  • EPGs electronic program guides
  • EPGs Like printed television program guides, EPGs contain grids listing the available television programs by time and date, channel and title. Some EPGs, however, allow television viewers to sort or search the available television programs in accordance with personalized preferences. In addition, EPGs allow for on-screen presentation ofthe available television programs. While EPGs allow viewers to identify desirable programs more efficiently than conventional printed guides, they suffer from a number of limitations, which if overcome, could further enhance the ability of viewers to identify desirable programs. For example, many viewers have a particular preference towards, or bias against, certain categories of programming, such as action-based programs or sports programming. Thus, the viewer preferences can be applied to the EPG to obtain a set of recommended programs that may be of interest to a particular viewer.
  • the TivoTM system for example, commercially available from Tivo, Inc., of Sunnyvale, California, allows viewers to rate shows using a "Thumbs Up and Thumbs Down" feature and thereby indicate programs that the viewer likes and dislikes, respectively. Thereafter, the TiVo receiver matches the recorded viewer preferences with received program data, such as an EPG, to make recommendations tailored to each viewer. While such currently available television program recommenders help a user to identify television programs of interest, they suffer from a number of limitations, which if overcome, could greatly improve the performance in the generated recommendations.
  • such television program recommenders typically record the viewer's prior viewing history in the form of a viewer profile, comprised of a number of program features and the corresponding number of occurrences ofthe feature in programs that were watched (and, optionally, not watched) by the viewer.
  • the number of occurrences of a feature in programs that were watched (and, optionally, not watched) by the viewer is often referred to as the "feature count.”
  • non-stationary viewing preferences changes in the television viewing habits of a viewer, often referred to as “non-stationary viewing preferences," are not easily identified. In fact, the aggregate nature ofthe feature counts in the viewer profile will mask any temporal patterns in the viewing history.
  • a method and apparatus for generating television program recommendations based on the prior viewing history of a viewer.
  • changes in the viewing preferences are automatically identified.
  • the viewing history is maintained as a series of viewing history windows, such as a series of consecutive periodic viewing history windows.
  • the present invention also allows the length of each individual window, and the overall length ofthe entire viewing history itself (i.e., the number of such smaller viewing windows) to be varied.
  • Another aspect ofthe invention identifies insignificant or non-reoccurring features that can be deleted from the viewer profile without losing significant information.
  • a feature i.e., an attribute-value pair
  • the disclosed television programming recommender can adapt the generated television program recommendations to such changes in viewing preferences.
  • the present invention allows the conditional probability of a feature (the likelihood or probability that programs in the class of programs watched (or not watched) by the viewer will have the feature) to be calculated as a function of time.
  • the television programming recommender adjusts the conditional probability of an attribute so that it more accurately reflects one or more similar earlier cycles or periods.
  • the television programming recommender could generate television program recommendations using the most recent window(s) ofthe viewing history, which are more likely to reflect the trend (current viewing preferences).
  • television program recommendations could be generated using values extrapolated from a strong trend.
  • FIG. 1 illustrates a television programming recommender in accordance with the present invention
  • FIG. 2 illustrates the maintenance of a time-windowed viewing history in accordance with the present invention
  • FIG. 3 is a sample table from the program database of FIG. 1 ;
  • FIG. 4 is a sample table from the viewer profile of FIG. 1;
  • FIG. 5 is a flow chart describing an exemplary viewer profile evaluation process embodying principles ofthe present invention.
  • FIG. 6 is a flow chart describing an exemplary viewer profile maintenance process embodying principles ofthe present invention.
  • FIGS. 7 A and 7b collectively, provide an example ofthe determination ofthe maximum likelihood estimates that are applied using linear trend testing and extrapolation techniques in accordance with the present invention.
  • FIG. 8 is a flow chart describing an exemplary profile window maintenance routine incorporating features of the present invention.
  • FIG. 1 illustrates a television programming recommender 100 in accordance with the present invention.
  • the television programming recommender 100 evaluates each ofthe programs in an electronic programming guide (EPG) 110 to identify programs of interest to a particular user.
  • EPG electronic programming guide
  • the set of recommended programs can be presented to the user using a set-top terminal/television 150, for example, using well known on-screen presentation techniques.
  • the television programming recommender 100 identifies programs that a viewer might like, based on their prior viewing history 200, discussed further below in conjunction with FIG. 2.
  • the television programming recommender 100 maintains the viewing history 200 as a series of viewing history windows that allow non-stationary viewing preferences to be identified.
  • the television programming recommender 100 can maintain the overall viewing history 200 as a series of consecutive periodic viewing history windows, such as one-month intervals.
  • the length of each individual window, and the overall length ofthe entire viewing history itself i.e., the number of such smaller viewing windows
  • a viewer profile typically indicates the number of occurrences of each of a number of program attributes in programs that were watched or not watched (or both) by the viewer.
  • a predefined minimum threshold such as an estimated noise level
  • the television programming recommender 100 can adapt the generated television program recommendations to such changes in viewing preferences.
  • the present invention allows the conditional probability of a feature (the likelihood that programs in the class of programs watched (or not watched) by the viewer will have the feature) to be calculated as a function of time.
  • the television programming recommender 100 adjusts the conditional probability of a feature so that it corresponds more accurately to the expected value at the current point in its cycle.
  • the time-history approach to viewing histories associated with the present invention reduces the negative impact on programs that do not contain a feature that does not apply in a dissimilar later time interval (e.g., the off-season of a sport).
  • the television programming recommender 100 For trends or permanent changes in viewing preferences, such as declining or increasing interest in a program, the television programming recommender 100 generates television program recommendations using the most recent window ofthe viewing history 200, which most likely reflects the trend (current viewing preferences), or adjusts the conditional probability to correspond to a value predicted by the trend.
  • the viewing habits of many viewers may change temporarily during the fall season with the start of a new television season, or during the playoffs of a given sport.
  • the viewing habits of a given viewer may be different in the morning hours as compared to the evening hours.
  • the present invention improves the performance ofthe television programming recommender 100 by detecting temporal patterns in the viewing history 200.
  • the television programming recommender 100 contains a viewing history 200, a program database 300 and a viewer profile 400, each discussed further below in conjunction with FIGS. 2 through 4, respectively.
  • the viewing history 200 identifies the programs that were watched (and/or not watched) by the viewer over a period of time.
  • the program database 300 records information for each program that is available in a given time interval.
  • the viewer profile 400 indicates the relative level of interest ofthe viewer in each corresponding program feature, based on viewing patterns.
  • the television programming recommender 100 includes a viewer profile evaluation process 500, a viewer profile maintenance process 600 and a profile window maintenance routine 800, each discussed further below in conjunction with FIGS. 5, 6 and 8, respectively.
  • the viewer profile evaluation process 500 performs statistical analysis on the conditional probabilities in the viewer profile 400 and identifies temporal patterns in the viewing history (e.g., trends or periodic behavior). Once temporal patterns are identified by the viewer profile evaluation process 500, the corresponding conditional probability values may be appropriately adjusted to take non-stationary viewing preferences into account in accordance with the present invention.
  • the viewer profile maintenance process 600 identifies and deletes insignificant features from the viewer profile 400.
  • the profile window maintenance routine 800 provides an exemplary process for creating and deleting time- windows in the viewer profile 400, as appropriate, over time.
  • the television program recommender 100 may be embodied as any computing device, such as a personal computer or workstation, having a processor 120 and memory 130.
  • the television programming recommender 100 may be embodied, for example, as the television program recommender described in United States Patent Application Serial No. 09/498,271, filed Feb. 4, 2000, entitled “Bayesian TV Show Recommender,” (Attorney Docket No. 700690), as modified herein to carry out the features and functions ofthe present invention.
  • FIG. 2 conceptually illustrates the processing ofthe viewing history 200 in accordance with the present invention.
  • the viewing history 200 is maintained as a series of viewing windows, VHN, that allow non-stationary viewing preferences to be identified.
  • the television programming recommender 100 can maintain the overall viewing history 200 as a series of consecutive periodic viewing windows, VHN, such as one-month intervals.
  • the viewing history 200 can be maintained in a small buffer or memory location until the corresponding viewer profile 400 is generated or updated.
  • the list of actual programs that were watched (or not watched) by the viewer only needs to be maintained until the corresponding conditional probabilities are determined for the viewer profile 400, as discussed below in conjunction with FIG. 4.
  • the length of each individual window, and the overall length ofthe entire viewing history itself may be varied.
  • the viewing history windows, VH] and VH K shown in FIG. 2 correspond to the sets of shows watched by the viewer during time spans Ti and K , respectively.
  • the illustrative viewing history sub-sets, VHi and VH K correspond to time spans that are less than the entire time period covered by the viewing history 200. If a viewer changed his or her viewing preferences from time span Ti to time span T R , the programs corresponding to the new preferences would appear in time span T K -
  • FIG. 3 is a sample table from the program database 300 of FIG. 1 that records information for each program that is available in a given time interval.
  • the program database 300 contains a plurality of records, such as records 305 through 320, each associated with a given program.
  • the program database 300 indicates the date/time and channel associated with the program in fields 340 and 345, respectively.
  • the title and genre for each program are identified in fields 350 and 355.
  • Additional well-known attributes (not shown), such as actors, duration, and description ofthe program, can also be included in the program database 300.
  • the program database 300 can also record an indication ofthe recommendation score assigned to each program by the television programming recommender 100 in field 370.
  • the numerical scores can be displayed to the user in the electronic program guide sorted according to the numerical scores, and/or with each program directly or mapped onto a color spectrum or another visual cue that permits the user to quickly locate programs of interest.
  • FIG. 4 is a table illustrating an exemplary viewer profile 400.
  • the viewer profile 400 contains a plurality of records 405-413 each associated with a different program feature.
  • the viewer profile 400 provides the corresponding number of occurrences ofthe feature in programs that were watched (and, optionally, not watched) by the viewer (positive and negative counts) for N different time windows.
  • the positive and negative counts for each of N different time windows are set forth in columns 445 through 470, respectively.
  • the positive and negative counts for the oldest time window 1 are set forth in columns 465 and 470, and the positive and negative counts for the most recent time window N are set forth in columns 445 and 450.
  • the oldest window (1) shown in columns 465 and 470 "falls off' or can be stored in a historical viewing history database (not shown). For example, if a given viewer watched a given sports program ten times on Channel 2 in the late afternoon in the most recent time window (N), then the positive counts associated with this features in the viewer profile 400 would be incremented by 10, and the negative counts would be 0 (zero).
  • the viewer profile 400 has N consecutive, adjacent, non-overlapping viewing windows of equal duration, such as one- month intervals.
  • the overall length ofthe entire viewing history (i.e., the number, N, of such smaller viewing windows) may be empirically determined or programmatically established by the user. It is noted that the size ofthe window interval and the number of windows will impose limits on the nature ofthe temporal patterns that can effectively be identified. These parameters also interact with the rate at which a viewer watches television programs.
  • FIG. 8, discussed below, provides an exemplary maintenance routine for the viewer profiles 400.
  • FIG. 5 is a flow chart describing an exemplary viewer profile evaluation process 500 embodying principles ofthe present invention.
  • the viewer profile evaluation process 500 performs statistical analysis on the conditional probabilities in the viewer profile 400 and identifies temporal patterns in the viewing history (e.g., trends or periodic behavior). Once temporal patterns are identified by the viewer profile evaluation process 500, the corresponding conditional probability values may be appropriately adjusted to take non-stationary viewing preferences into account in accordance with the present invention.
  • the viewer profile evaluation process 500 initially retrieves the viewer profile 400 during step 510. Thereafter, the viewer profile evaluation process 500 performs a statistical analysis on the conditional probabilities for each feature during step 520. A test is performed during step 530 to determine if the statistical analysis indicates any trends or periodic viewing behavior. If it is determined during step 530 that there are no trends or periodic viewing behavior indicated in the viewer profile 400, then the conditional probabilities are computed during step 570 as the mean ofthe set of conditional probabilities, before program control terminates in step 580.
  • conditional probabilities CP
  • step 540 a further test is performed during step 540 to distinguish between trends and periodic viewing behavior. If it is determined during step 540 that the viewing behavior includes one or more trends, then the conditional probabilities for such corresponding features are computed during step 550 as the maximum likelihood estimate ofthe conditional probabilities given the trend.
  • FIGS. 7A and 7B discussed below, provide an example that illustrates the determination ofthe maximum likelihood estimates that are applied during step 550 (and also during step 560, discussed below) using linear trend testing and extrapolation techniques.
  • the conditional probability for such corresponding features are computed during step 560 as the maximum likelihood estimate of the conditional probabilities given the periodic model (to more closely resemble those of corresponding earlier time periods). For example, if a particular viewer tends to watch a lot of baseball during the World Series, then the corresponding conditional probabilities should be adjusted in an upward manner for each time interval when the World Series is on. Likewise, the corresponding conditional probabilities should be adjusted in a downward manner for each time interval when the World Series is not on to thereby reduce the negative impact on programs that do not contain such corresponding features in a dissimilar later time interval (e.g., the off-season for baseball). Program control terminates during step 580.
  • FIG. 6 is a flow chart describing an exemplary viewer profile maintenance process 600 embodying principles ofthe present invention.
  • the viewer profile maintenance process 600 identifies and deletes insignificant attributes from the viewer profile 400.
  • the viewer profile maintenance process 600 initially retrieves the viewer profile 400 during step 610.
  • a test is performed during step 620 to determine if the profile 400 contains any features having a frequency count that falls below a predefined or dynamically determined noise threshold for a predefined consecutive number of windows.
  • the noise threshold may be determined, for example, by analyzing the frequency counts ofthe randomly sampled negative examples. If it is determined during step 620 that one or more features have a frequency count that falls below a predefined noise threshold for a predefined consecutive number of windows, then such insignificant features are deleted from the viewer profile 400 during step 630.
  • the feature "early afternoon programs” represents a feature that was identified once in the random sampling of programs not watched by the viewer. If the window size ofthe viewer profile 400 is one-month and a total of N months are maintained in the viewing history, then the oldest month will be deleted and the feature "early afternoon programs” can also be deleted for all months in the viewer profile 400 as not contributing to the current interests ofthe viewer. Program control terminates during step 640.
  • FIGS. 7 A and 7B collectively, provide an example ofthe determination ofthe maximum likelihood estimates that are applied during steps 550 and 560, using linear trend testing and extrapolation techniques.
  • FIG. 7A shows a portion of an exemplary viewer profile 700.
  • the viewer profile 700 contains a plurality of records 705- 707 indicating the total number of programs, feature counts for a representative feature and corresponding estimated conditional probabilities, respectively, for five different time windows.
  • the estimated conditional probabilities set forth in record 707 are computed only for the positive examples. In the illustrative example, however, there is no trend for the negative examples, and the estimated conditional probabilities would be zero.
  • FIG. 7A shows a portion of an exemplary viewer profile 700.
  • the viewer profile 700 contains a plurality of records 705- 707 indicating the total number of programs, feature counts for a representative feature and corresponding estimated conditional probabilities, respectively, for five different time windows.
  • the estimated conditional probabilities set forth in record 707 are computed only for the positive
  • FIG. 7B illustrates a regression line 770 that provides a best-fit ofthe five samples shown in FIG. 7 A.
  • FIG. 7B illustrates the prediction ofthe sixth (or any future) sample 780 using an extrapolation ofthe linear trend.
  • a linear regression analysis is performed on the sample data 761-765 using well-known techniques to identify the regression line 770 having the lowest sum-square-error.
  • the regression line 770 is obtained it can be extrapolated using known techniques to obtain the conditional probability 780 for the next (and any subsequent) interval.
  • the regression line 770 is identified by its slope (m) and intersection (b), in a known manner. Once m and b are obtained, any future value, y can be predicted as follows:.
  • FIG. 8 is a flow chart describing an exemplary profile window maintenance routine 800 for the viewer profiles 400.
  • the profile window maintenance routine 800 initially detects programs watched by the viewer during step 810. Thereafter, the profile window maintenance routine 800 performs a test during step 820 to determine if the current time is within the current window. In other words, the test performed during step 820 determines if it is time to create a new window in the profile 400.
  • step 820 If it is determined during step 820 that the current time is not within the current window, then the current window pointer is incremented during step 830, all frequency counts in the current window are set to zero during step 840, and the viewer profile maintenance process 600, discussed above in conjunction with FIG. 6, is executed during step 850 (to delete insignificant features). Program control then proceeds to step 860
  • step 820 If, however, it is determined during step 820 that the current time is within the current window, then the frequency counts in the current window of all features associated with the program are incremented during step 860. Thereafter, program control terminates. The negative counts would be processed in a similar manner.

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  • Accounting & Taxation (AREA)
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  • Marketing (AREA)
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  • Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
  • Testing, Inspecting, Measuring Of Stereoscopic Televisions And Televisions (AREA)

Abstract

La présente invention concerne un procédé et un appareil permettant de produire des recommandations de programme de télévision, sur la base d'un historique de visionnement à fenêtre temporelle d'un téléspectateur. Des changements des préférences de visionnement sont automatiquement identifiés. Le profil d'historique de visionnement est maintenu sous forme d'une série de fenêtres d'historique de visionnement. La longueur de chaque fenêtre de visionnement individuelle et la longueur totale de l'historique de visionnement complet lui-même (c'est-à-dire le nombre de telles fenêtres de visionnement plus petites) peuvent être fixes ou variables. Des caractéristiques peu importantes ou non récurrentes, qui peuvent être supprimées du profil du téléspectateur sans que des informations importantes ne soient perdues, sont également identifiées. Une caractéristique peut être supprimée du profil du téléspectateur lorsque la fréquence correspondant à cette caractéristique ne dépasse pas un seuil minimum pendant une période de temps prédéfinie. Le système de recommandation de programme de télévision selon cette invention adapte les recommandations de programme de télévision produites aux changements des préférences de visionnement. La probabilité conditionnelle d'une caractéristique (la probabilité que des programmes d'une classe connue, telle que la classe des programmes regardés, présentent ladite caractéristique) est calculée en fonction du temps. Pour des changements cycliques ou périodiques des préférences de visionnement, le système de recommandation de programme de télévision selon cette invention règle la probabilité conditionnelle d'un attribut de façon qu'il reflète plus précisément un ou plusieurs cycles ou périodes précédents identiques. Pour des tendances ou des changements permanents des préférences de visionnement, le système de recommandation de programme de télévision produit des recommandations de programme de télévision en utilisant la/les fenêtre(s) la/les plus récente(s) de l'historique de visionnement, qui est/sont le/les plus susceptibles de représenter la tendance (préférences de visionnement actuelles) ou extrapole une valeur à partir de la tendance identifiée.
EP01991738A 2000-11-22 2001-11-16 Systeme de recommandation de programme de television a profils bases sur des intervalles permettant de determiner des probabilites conditionnelles variables dans le temps . Withdrawn EP1340179A2 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US718256 1985-04-01
US71825600A 2000-11-22 2000-11-22
PCT/EP2001/013418 WO2002042959A2 (fr) 2000-11-22 2001-11-16 Systeme de recommandation de programme de television a profils bases sur des intervalles permettant de determiner des probabilites conditionnelles variables dans le temps .

Publications (1)

Publication Number Publication Date
EP1340179A2 true EP1340179A2 (fr) 2003-09-03

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Country Status (5)

Country Link
EP (1) EP1340179A2 (fr)
JP (1) JP2004515145A (fr)
KR (1) KR20020070496A (fr)
CN (1) CN1606746A (fr)
WO (1) WO2002042959A2 (fr)

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WO2002042959A2 (fr) 2002-05-30
KR20020070496A (ko) 2002-09-09
CN1606746A (zh) 2005-04-13
WO2002042959A8 (fr) 2002-07-25

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