Source: http://www.google.com/patents/US7958119?ie=ISO-8859-1&dq=6,049,612
Timestamp: 2014-07-10 04:58:43
Document Index: 473635187

Matched Legal Cases: ['art 208', 'art 208', 'art 208', 'art 208', 'art 504', 'art 504']

Patent US7958119 - Method for content recommendation - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign in<nobr>Advanced Patent Search</nobr>PatentsA method for content recommendation for a user, wherein a song or a video is recommended to the user and a confidence measure is determined for the recommended song. The confidence measure is displayed to the user, so the user may get more confidence into the recommendation of the system....http://www.google.com/patents/US7958119?utm_source=gb-gplus-sharePatent US7958119 - Method for content recommendationAdvanced Patent SearchPublication numberUS7958119 B2Publication typeGrantApplication numberUS 12/025,425Publication dateJun 7, 2011Filing dateFeb 4, 2008Priority dateMar 31, 2007Also published asCN101276375A, CN103838858A, EP1975813A1, EP2343660A1, US8688699, US20090013002, US20110196865Publication number025425, 12025425, US 7958119 B2, US 7958119B2, US-B2-7958119, US7958119 B2, US7958119B2InventorsJana Eggink, Thomas Kemp, Wilhelm Hagg, Taufan Zimmer, Tomasz FeduszczakOriginal AssigneeSony Deutschland GmbhExport CitationBiBTeX, EndNote, RefManPatent Citations (15), Non-Patent Citations (3), Referenced by (8), Classifications (24), Legal Events (1) External Links: USPTO, USPTO Assignment, EspacenetMethod for content recommendationUS 7958119 B2Abstract A method for content recommendation for a user, wherein a song or a video is recommended to the user and a confidence measure is determined for the recommended song. The confidence measure is displayed to the user, so the user may get more confidence into the recommendation of the system.
1. A method, implemented by an information processing apparatus, for content recommendation, comprising:
determining, by the information processing apparatus, a content recommendation from a data base of content items based on a profile, wherein said content recommendation includes at least one content item;
determining, by the information processing apparatus, a confidence measure for said content recommendation, said confidence measure being descriptive of an estimated quality of the determined content recommendation;
communicating said confidence measure or a derivative thereof; and
requesting feedback for at least a part of said content recommendation, wherein the feedback indicates a liking or disliking of said content recommendation and includes different options that change based on the confidence measure.
said feedback relates to meta data attributes of said content recommendation, and
a level of detail of the meta data attributes depends on the confidence measure.
3. The method according to claim 1, wherein, when the confidence measure is above a threshold and the feedback indicates a disliking of said content recommendation, the feedback includes feedback relating to meta data attributes.
4. The method according to claim 1, wherein, when the confidence measure is below a threshold, the feedback only includes feedback indicating a liking or disliking of said content recommendation and does not include meta data attributes.
5. The method according to claim 2, wherein, when the confidence measure is below a threshold and the feedback indicates a disliking of said content recommendation, the feedback includes feedback relating to said meta data attributes, and, when the confidence measure is below a threshold and the feedback indicates a liking of said content recommendation, the feedback includes feedback relating to said meta data attributes.
receiving feedback for at least a part of said content recommendation;
inquiring for further feedback; and
adapting said profile based on said feedback and/or said further feedback.
7. The method according to claim 6, wherein, when said further feedback indicates a dislike of said content recommendation and the feedback indicates a general liking of said content recommendation but not at the moment, the profile is not adapted.
8. The method according to claim 1, wherein, said determining of said confidence measure is based on an estimated quality of the profile.
9. The method according to claim 8, wherein said estimated quality is determined depending on an amount of interactions.
10. The method according to claim 8, wherein said estimated quality is determined depending on coherence of the profile.
determining a virtual best matching content item based on said profile, said virtual best matching content item matching all meta data attributes given feedback for as close as possible; and
12. The method according to claim 1, wherein a green light is lighted or a green feedback button is displayed, when said confidence measure has a high value, and
a red light is lighted or a red feedback button is displayed, when said confidence measure has a low value.
13. The method according to claim 1, wherein said confidence measure is used to explain why a certain content recommendation was selected.
14. The method according to claim 1, wherein the feedback is indirect feedback.
15. A device for content recommendation, comprising:
a data processor configured to determine a content recommendation from a data base of content items based on a profile, wherein said content recommendation includes at least one content item, and
to determine a confidence measure for said content recommendation, wherein said confidence measure is descriptive of an estimated quality of the determined content recommendation;
a display configured to display said confidence measure or a derivative thereof; and
a feedback part configured
to input feedback for at least a part of said content recommendation,
the feedback indicating a liking or disliking of said content recommendation, and
to input further feedback, the further feedback including different options that change based on said confidence measure,
wherein said data processor updates said profile based on said feedback and/or said further feedback.
16. A non-transitory computer-readable medium storing computer readable instructions thereon that when executed by a content recommendation device cause the content recommendation device to perform a method, comprising:
displaying a graphical user interface that includes
a selectable negative feedback button associated with a first changeable text field, wherein said first changeable text field indicates that normal negative feedback can be input for a currently played content item,
wherein, in response to a normal negative feedback being input, a confidence measure for the currently played content item being above a threshold, and said feedback indicating a disliking of said currently played content item,
the first changeable text field changes and indicates that a further refined feedback can be input for the currently played content item.
17. The non-transitory computer-readable medium according to claim 16, wherein the graphical user interface further includes a selectable negative feedback button which upon selection causes a negative feedback to be output to a data processor.
18. A non-transitory computer-readable medium storing computer readable instructions thereon that when executed by a content recommendation device cause the content recommendation device to perform a method comprising:
at least one negative feedback button associated with a second text field, said negative feedback button allowing input of negative feedback for said currently played song, wherein an appearance of said first and second text field is based on a confidence measure for said currently played song, said confidence measure being descriptive of an estimated quality of the currently played song.
19. A non-transitory computer-readable medium storing computer readable instructions thereon that when executed by a content recommendation device cause the content recommendation device to perform a method comprising:
determining a content recommendation from a data base of content items based on a profile, wherein said content recommendation includes at least one content item;
requesting for feedback for at least a part of said content recommendation, wherein the feedback indicates a liking or disliking of said content recommendation and includes different options that change based on the confidence measure.
20. A device for content recommendation, comprising:
means for determining a content recommendation from a data base of content items based on a profile, wherein said content recommendation includes at least one content item;
means for determining a confidence measure for said content recommendation, wherein said confidence measure is descriptive of an estimated quality of the determined content recommendation;
means for communicating said confidence measure or a derivative thereof; and
means for requesting feedback for at least part of said content recommendation, wherein the feedback indicates a liking or disliking of said content recommendation and includes different options that change based on the confidence measure.
21. A method, implemented by an information processing apparatus, for content recommendation, comprising:
determining, by the information processing apparatus, a confidence measure for said content recommendation, said confidence measure being descriptive of an amount of data about a user's preferences;
requesting for further feedback, wherein the feedback indicates a liking or disliking of said content recommendation and includes different options that change based on the confidence measure.
23. A method, implemented by an information processing apparatus, for content recommendation, comprising:
requesting feedback for at least a part of said content recommendation, wherein a type of the feedback depends on the confidence measure, wherein said confidence measure or said derivative thereof is communicated simultaneously with requesting feedback for at least part of said content recommendation.
a green light is lighted or a green feedback button is displayed, when said confidence measure has a high value, and
25. A device for content recommendation, comprising:
a data processor configured
to determine a content recommendation from a data base of content items based on a profile, wherein said content recommendation includes at least one content item, and
a graphical user interface configured to display said confidence measure or a derivative thereof; and
at least one positive feedback button associated with a first text field, said at least one positive feedback button allowing input of positive feedback for a currently played song; and
at least one negative feedback button associated with a second text field, said at least one negative feedback button allowing input of negative feedback for said currently played song,
an appearance of said first and/or second text field depends on a confidence measure for said currently played song, said confidence measure being descriptive of an estimated quality of the currently played song,
said graphical user interface displays said confidence measure or said derivative thereof simultaneously with said at least one positive and said at least one negative feedback button.
an icon associated with a positive result or feeling is displayed when the confidence measure of the currently recommended at least one content item is high, and
an icon associated with a negative result or feeling is displayed when the confidence measure of the currently recommended at least one content item is low.
27. The device according to claim 25, wherein the graphical user interface further includes a graphical item configured
to emit green light when said confidence measure is high, and
to emit red light when said confidence measure is low.
28. The device according to claim 27, wherein said graphical item is configured
to emit green light when said confidence measure is above a first threshold,
to emit red light when said confidence measure is below a second threshold, and
to emit yellow light when said confidence measure lies in between said first and second threshold.
BACKGROUND OF INVENTION Today, large databases of content items, e.g. music databases comprising one million songs or more or video databases comprising several thousands of videos are widely available. Users, however, often have difficulties browsing such large databases and finding e.g. a piece of music they like to listen to. Thus, there is a need for a method for content recommendation that automatically selects one or more content items for a user, wherein the selected content item(s) should match the user's taste as close as possible.
SUMMARY OF THE INVENTION It is an object of the invention to provide a method and device for content recommendation enabling a high rate of user satisfaction. Further, it is an object of the invention to provide a respective graphical user interface.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 shows possible steps of a method for content recommendation;
FIGS. 7A, 7B and 7C shows diagrams for elucidating how the coherence may be determined;
FIGS. 9A, 9B and 9C shows different types of user feedback depending on a confidence measure.
DETAILED DESCRIPTION In the following, embodiments of the method and device for content recommendation are described. It should be noted that the described embodiments may be combined in any way, i.e. features described in connection with a certain embodiment may be combined with features described in connection with another embodiment.
Communicating the confidence measure to the user may help to increase the user's understanding of the system and may subsequently increase the user's trust and acceptance of the system. This may also allow the introduction of �surprise recommendations�. Such surprise recommendations may be recommendations that deliberately do not match the user profile and, thus, in general may have a low confidence measure. However, because the confidence measure is communicated to the user, the user may be made aware of this fact. For example, the system may display a sentence like the following to the user: �This is a surprise recommendation that does not exactly match your user profile.� Thus, the user may be made aware of the fact that the current recommendation does not match his user profile in the best possible way. However, because the user is informed about this fact, the user may still accept the system's behavior.
Meta data attribute
Matching score:
Confidence measure:
pop, slow, romantic, 1990s
User dislikes:
punk, aggressive, 1980s
pop, slow, melancholic, 1990s
punk rock, fast, 1980s
soul, slow, 1960s.
For computing the matching score, it would be possible to count all matching like attributes positive (incrementation) and subtract matching attributes labeled with �dislike� (negative feedback attributes).
In a further embodiment, it is also possible that if (or only if) the confidence measure is above a threshold and the user feedback indicates a disliking of the content recommendation, the user feedback may comprise feedback relating to the meta data attributes. For example, the system may recommend a pop song from the 1980s with strong guitars and a high male voice. If the confidence measure is high, e.g. lies above a threshold, there could e.g. be a plurality of buttons provided. For example, there could be one button being labeled �like� and a plurality of buttons relating to disliking, wherein the plurality of buttons could be labeled with �dislike because of strong guitars�, �dislike because of type of voice� or �dislike at the moment�. This way, if the confidence measure is high and the feedback indicates a disliking, the feedback relates to meta data attributes (see also FIG. 9A further explained below).
Still further, if the confidence measure is low, e.g. below a threshold, the user feedback may only indicate a liking or disliking of the content recommendation without being related to the meta data attributes (see also FIG. 9B further explained below). This may be useful, if the system has not been in use for a long time and further general feedback is necessary to better learn the user's behavior or preferences. Thus, if the confidence measure is low, e.g. the confidence measure is below a threshold, there may be only two buttons, one button labeled with �like� and one button with labeled �dislike�.
Also, if the confidence measure is low, e.g. below a threshold, there could be multiple buttons having labels like e.g. �like because of 1980s�, �like because of pop�, �like because of the combination of 1980s and pop�, �dislike because of 1980s�, �dislike because of pop�, and/or �dislike because of the combination of 1908s and pop�. Thus, according to this embodiment shown in FIG. 9C and further explained below, the feedback may be related to meta data attributes if the confidence measure is low and the feedback indicates a liking or disliking of the content recommendation.
The method may also comprise receiving user feedback for at least a part of the content recommendation, inquiring the user for further user feedback, e.g. refined user feedback, depending on the confidence measure, and adapting the user profile based on the user feedback and/or the further user feedback. Thus, it may be possible to obtain further valuable feedback depending on the confidence measure. For example, if the confidence measure is high, e.g. above a certain threshold, this may indicate that the system is rather sure that the recommendation matches the user's taste. Therefore, the system may take the opportunity to ask the user e.g. what exactly the user likes about the song. For example, the user may be asked a question like �This is a romantic song. What type of romantic songs do you like: sweet romantic or soft romantic?�. On the other hand, if the confidence measure is rather high, i.e. the system is rather sure that the recommendation matches the user's taste but the user feedback nevertheless indicates that the user does not like the song, the system may ask if the user does generally not like this recommendation or only at present. It is also possible that the system asks if the user generally does not like the genre, mood or era of the song.
In a further embodiment, if the user feedback indicates that the user dislikes the content recommendation and the further feedback indicates that the user generally likes the content recommendation but not at the moment, the user profile may not be adapted or may be adapted differently than normally, i.e. without further feedback. If no further feedback would be available, it can be very difficult to obtain a precise user profile. It is also possible that the further feedback indicates that the user generally likes the genre of a currently played song, but does not like a specific song. For example, the user may like pop songs, but not the song �Sorry� by Madonna, e.g. because he has negative feelings or memories of this particular song.
In a further embodiment, the estimated quality may be determined depending on the coherence of the user profile. The coherence refers to the overall sense or understandability of the user profile. Coherence may also refer to the logical interconnections, congruity or consistency of the user profile. In general, if the coherence is high, than the confidence measure may also be high. For example, a first user may consistently have rated fast songs of the 1950s with �like� and slow songs of the 2000s with �dislike�. A second user may have rated some fast songs of the 1950s with �like� and some with �dislike�. Further, the second user may have rated some slow songs from the 2000s with �like� and some with �dislike�. In this example, the user profile of the first user would have a high coherence, because the user's ratings may be easily classified/explained by �The first user likes fast songs from the 1950s and dislikes slow songs from the 2000s�. Contrary, the user profile of the second user has a low degree of coherence because it is more difficult to explain the strategy of ratings of the second user.
In a further embodiment, it may be possible to determine a virtual best matching content item based on the user profile, the virtual best matching content item matching all meta data attributes that the user has given feedback for as close as possible. �As close as possible� may mean that only attributes are considered for which the user has given positive feedback, since in general it is not intended to recommend items with a large number of negative feedback attributes. Thus, negative feedback attributes may not need to be considered.
Probability of like
In a possible embodiment, if a song from a database is to be determined based on the virtual best matching content item, then it may be possible to only compare attributes of the song with the attributes labeled with �1� of the virtual best matching content item. Therefore, computation time may be reduced.
Further, it may be possible to allow the incorporation of new labels that have so far not been used in the database where the content items are stored. For example, a new label �melancholic� may be used that has so far not been used. In order to achieve a compatibility with an existing database and/or search algorithms and/or user profiles, it may be possible to create a reference database comprising an assignment between old and new labels. For example the new label �melancholic� may be assigned to attribute �romantic� that has already been used before.
It may also be possible to attribute matching values to the new labels. The matching values may describe a degree of similarity to the old labels (labels that have been used so far). For example, the new label �melancholic� may be labeled with the following similarity values:
When matching a song with a new label, e.g. with the new label �melancholic�, then, as described above, the concept of virtual best matching content item may help to save computational time, because no complex computations need to be performed that would be necessary when the probabilities of like or other measures stored together with a user profile would be used.
Alternatively or additionally, it may also be possible to have three different icons, e.g. a smiley or heart for a high confidence measure �like�, a skull for a high confidence measure for �dislike� (although such a content item may normally not be recommended), and a question mark or something indicating a surprise e.g. an avatar with an open mouth for a low confidence measure or unknown confidence measure.
Then, in a receiving step S110, user feedback may be received. The user feedback received in receiving step S110 may indicate whether the user likes or dislikes a currently played content item. The user feedback may be given on a gradual scale or binary, i.e. the only feedback values are �like� and �dislike�.
FIG. 2 shows a device 200 comprising a database 202, central processing unit (CPU) 204, display 206 and feedback part 208. In the example of FIG. 2, display 206 displays what song is currently being played. In the example of FIG. 2, the song �Sorry� by Madonna is played. Further, it is displayed that the genre of this song is �pop�. Still further, device 200 of FIG. 2 displays that the confidence for the currently played song �Sorry� is 80%. This means, that the system is rather sure (with a probability of 80%) that the user will like the song �Sorry�.
FIG. 3 shows the device of FIG. 2 after having received the dislike rating. Upon entering the feedback, the feedback part 208 e.g. a graphical user interface may be changed. In the example of FIG. 3, three buttons 300, 302 and 304 may be displayed on feedback part 208. Each button may have an associated text field. In the example of FIG. 3, the first button 300 is associated with the text field indicating the text �I like it but not now�. Further, the text field of the second button 302 indicates the text �I like pop, but not this song�. Further, the text of the third button 304 is �I don't like it�. By selecting/pressing one of buttons 300, 302 or 304, the user may input refined feedback, i.e. further feedback, for the currently played song �Sorry�. This refined feedback may then be used for adapting the user profile.
FIG. 4 shows a further embodiment of the device of FIG. 2 after having received the dislike rating. In this embodiment, after the user has either input a like rating or dislike rating by pressing the like button 210 or dislike button 212, the feedback part 208 changes, wherein the text associated with like button 210 and/or dislike button 212 changes. In the embodiment shown in FIG. 4, the text field associated with the like button 210 changes from �like� (see FIG. 2) to �I like it but not now� in FIG. 4.
If the user presses button 210, the system receives feedback indicating that the user generally likes the currently played song, in the example �Sorry�, but not at the moment. Thus, the system may skip the currently played song �Sorry�, however, no negative feedback value will be associated with the song or its meta data attributes, i.e. the user profile is adapted differently.
FIG. 5 shows still another embodiment of a device 500 for content recommendation. The device 500 comprises a display 502, a feedback part 504, a central processing unit (CPU) 506, a data storage 508, a colored diode 510 and a receiver/transmitter 512. In the example of FIG. 5, device 500 currently plays the song �Beautiful day� by U2. On feedback part 504, the user can rate the song �Beautiful day� by pressing a like or dislike button. The colored diode 510 may change its color from red to yellow or to green. The colored diode may emit green light if a confidence measure for the currently played song is above a first threshold. If the confidence measure is below a second threshold, the colored diode may be controlled by the CPU 506 to emit red light. If the confidence measure lies in between the first and second threshold, the colored diode 510 may be controlled by CPU 506 to emit yellow light.
FIG. 7A shows the ratings of the first user. The diagram shown in FIG. 7A shows the ratings of different content items of different years and different tempo, e.g. measured in beats per minute. Positive ratings are denoted by a �+� sign and negative ratings with a �−� sign. As may be seen in FIG. 7A, the user has rated fast songs of the 1980s with like and slow songs of the 1980s with dislike. Further, the user has rated all songs of the 1990s with dislike. Still further, the first user has rated some songs of the 2000s having a low tempo with like or dislike and fast songs of the 2000s with like.
Thus, the ratings of the second user may be simply explained by a statement like �As newer a content item the slower the content item�. The ratings of the first user, however, is much more difficult to explain.
If the confidence measure is high, e.g. the confidence measure is above a threshold, a graphical user interface 901-A or other feedback means may have a form as shown in FIG. 9A. As seen in FIG. 9A, there is a plurality of buttons 900-1, 900-2, 900-3, 900-4, 900-5 provided allowing the user to input different kind of feedback. Each of buttons 900-1 to 900-5 is labeled with a different kind of label 902-1, 902-2, 902-3, 902-4, 902-5 indicating the respective following types of feedback: �like� (902-1), �dislike because of strong guitar� (902-2), �dislike because of genre� (902-3), �dislike because of type of voice� (902-4), �like but not now� (902-5).
FIG. 9B shows a graphical user interface 901-B in case of a low confidence measure, i.e. a confidence measure below a threshold. As seen, graphical user interface 901-B only has two buttons, a first button 904-1 labeled with label 906-1 �like� and a second button 904-2 with label 906-2 �dislike�.
Alternatively, if the confidence measure is low, as shown in FIG. 9C, there may be provided a plurality of feedback buttons 908-1, 908-2, . . . having respective labels 910-1, 910-2. The labels of the buttons may e.g. say something like: �like because of 1980s�, �like because of pop�, �like because of the combination of 1980s and pop�, �dislike because of 1980s�, �dislike because of pop�, �dislike because of the combination of 1980s and pop�.
The confidence indicator may also be used to deliberately introduce �surprise recommendations� that do not match the user profile very well. This may be used to avoid boredom caused by to many similar recommendations. Without a confidence indicator, such surprise recommendations will seem like errors to the user. However, by displaying the confidence measure, i.e. by using a confidence indicator, the user may immediately see that a certain recommendation is a surprise recommendation and may decide that he wants to explore something new.
It is also possible to hypothesize a virtual best matching content item which matches the user profile exactly. The difference in score between this virtual �ideal� item and any real recommended item may indicate the confidence of the system into the recommendation, i.e. the smaller the difference, the higher the confidence. This confidence may be optionally be weighted by the �quality� of the current user profile.
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