US20100199295A1

US20100199295A1 - Dynamic video segment recommendation based on video playback location

Info

Publication number: US20100199295A1
Application number: US12/457,429
Authority: US
Inventors: Ravi Reddy Katpelly; Kunal Kandekar; Mike Helpingstine; Scott Curtis
Original assignee: Napo Enterprises LLC
Current assignee: Concert Technology Corp
Priority date: 2009-02-02
Filing date: 2009-06-10
Publication date: 2010-08-05
Also published as: US20100199218A1; US20160299970A1; US9824144B2; US8200602B2; US20120041967A1; US20100198767A1; US9367808B1

Abstract

A system and method for dynamically providing video segment recommendations to a user based on video metadata, current playback position within the video, and/or the user's previous viewing history or patterns. The system/method also provides the user with a simple mechanism to enable/disable receipt of these recommendations with the click of a button within the display interface.

Description

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims priority from U.S. Provisional Application No. 61/149,216 filed on Feb. 2, 2009, the disclosure of which is incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates to a media system and, more particularly, to a media system for and method of dynamically and intelligently recommending video segments to a user.

BACKGROUND OF THE INVENTION

In general, video sharing sites such as YouTube allow users to browse/search for videos, and view the videos as and when they find anything interesting. Users can even browse through similar/related videos while watching a certain video.
However, if a user likes a certain segment/scene in a video and would like to find/view any similar scenes, current systems provide no search feature (to find “Related Video Segments”). In such cases, users are required to recreate and re-submit a new search query, which may not be easy for users to formulate (or describe in words) especially when a user is interested in a particular scene.
More specifically, one related art system that enables users to browse through similar/related videos utilizes an MPEG-7 based metadata structure for associating/grouping video segments with similar/related events taking place. The events can be, for example, all touchdown passes in a football game, etc. These events may be part of the same video or may belong to different videos.
Several extensions have been proposed to the model. For example, a sports reporter who is trying to create a reportage on touch down passes in a football game may look for all the scenes where touchdowns took place, and associate them together as a “touchdown” event. As an another example, for creating a reportage on “touchdown” events from several games by a single player (e.g., Peyton Manning), the reporter can identify a “touchdown” event and a “Manning” object, and find all scenes where Manning threw touchdown passes, and associate all such events found to the “Manning” object.
The related art system offers several extensions to the model to provide a lean-back viewing experience for an end-user. However, the users are required to tell the system via a preference profile page, of the events that they are interested in and would like to receive the recommendations. For example, a user may indicate his preference to “touchdown” events, and explicitly mention in his preference profile that he would like to receive “touchdown” events either at the end of the event or at the end of the video. With this model, users are required to explicitly subscribe to the system of desired events. Next, while watching another or the same video, if the user likes another event, he is required to again update his preference profile of that event.
Furthermore, describing/representing an event in plain text itself might not be intuitive to the users. For example, say if the user is watching a football game and is interested in a “goal” event (meaning that while the football is near the goal line), such events cannot be easily represented in a way that the recommendation engine can interpret it correctly. The “goal” event can be understood as “goals scored in a soccer game”. In such a case, the recommendation engine would supply video segments containing “goals scored in a soccer game”.

SUMMARY OF THE INVENTION

The present invention provides a system that can dynamically show similar segments to a user based on the current playback location, and, for example, based on the user's previous viewing history (patterns).
The present invention makes recommendations that are implicit in nature, i.e., users are not required to explicitly indicate their interest in certain events. These recommendations are based on the current playback location of the user in the video and utilize a user's profile, preferences and previous viewing history behavior to determine which of the recommendations best suit the user's interest. Unlike related art systems where a user's preference profile is used by the system to check if the user is interested in a given video, or the current event (within the video), the present invention uses a user's preference profile to select the best of all the available (or dynamically generated) recommendations that would suit the user's interest with respect to current activity (or event) in the video. The present invention can therefore provide continuous video segment recommendations to the user (i.e., as the current segment being viewed keeps changing, the recommendations keep changing).
The present invention provides a system and method that identifies and displays video segments that are similar to a user's currently viewed video segment. The system dynamically and continuously updates the similar segments depending on the user's current playback position. As and when the video progresses or depending on the user's playback events (such as skip, fast forward, rewind, etc), the similar segments are updated. The similar segments can be displayed on user request (such as when a user clicks on video player to view similar segments), or can be displayed/updated periodically (such as every ten seconds), or can be displayed/updated based on changes or events in the viewed video (such as at the beginning of a new scene). The users may optionally specify how frequently they wish to receive the recommendations, at what point within the scene (i.e., the offset from the start of the scene) they may want to receive them, and so forth. The system/method can be used in any distributed as well as centralized system, and can be used as a notification service to receive video scene recommendations.
According to one aspect, the present invention provides a method of dynamically recommending media segments to a user, comprising: analyzing and gathering metadata of a currently viewed media segment of a main media content based on a current playback location; identifying other media segments, from the main media content or other media content, that are similar to the currently viewed media segment; and dynamically recommending at least some of the identified other, similar media segments to the user.
According to another aspect, the media segments may comprise video segments.
According to another aspect, the method may further comprise matching the metadata of the currently viewed video segment against metadata of all video segments available in a collection of videos such as a video database; and storing a list of all the video segments that match. The database also may be a centralized database or a distributed database or a peer-to-peer (P2P) system. Thus, a query may be issued containing the current segment metadata across a distributed system to find a list of matching segments. Also, the database may store video metadata instead of the videos, and the metadata may include a pointer to the actual videos.
According to another aspect, the method may further comprise gathering user information including one of a previous viewing history of a user, a user profile, or a user preference; and finding video segments identified that most closely match with the gathered user information. The user's previous browsing history may also be used to filter matching segments, such as, for example, those segments that the user has already watched.
According to another aspect of the present invention, a media system for dynamically recommending video scenes to a user is provided, comprising: means for detecting metadata of a currently viewed video scene of a video content; means for identifying other video scenes, from the video content or other video content, that are similar to the currently viewed video scene; and means for dynamically recommending at least some of the identified other, similar video scenes to the user based on the user's previous viewing history.
The present invention also contemplates a computer readable medium comprising software for instructing a media system to: detect and gather metadata of a currently viewed media segment of a primary media content based on a current playback location; identify other media segments, from the primary media content or additional media content, that are similar to the currently viewed media segment; and dynamically recommend at least some of the identified additional, similar media segments to a user based on previous viewing patterns of the user.
A media system for dynamically recommending video segments to a user, comprising: a media player which detects metadata of a currently viewed video scene of a video content based on a current playback location within the video content; a segment similarity analyzer which identifies other video scenes, from the video content or other video content, that are similar to the currently viewed video scene, and dynamically recommends at least some of the identified other, similar video scenes to the user based on a previous viewing history of the user; and a display device which displays the recommended, similar video scenes that most closely match with the previous viewing history of the user.
Those skilled in the art will appreciate the scope of the present invention and realize additional aspects thereof after reading the following detailed description of the preferred embodiments in association with the accompanying drawing figures.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the invention, and together with the description serve to explain the principles of the invention.

FIG. 1 illustrates a media system according to an exemplary embodiment of the present invention;

FIG. 2 depicts an illustrative embodiment of a method operating in the media system of FIG. 1;

FIG. 3 depicts a further illustrative embodiment of a method operating in the media system of FIG. 1;

FIG. 4 depicts a still further illustrative embodiment of a method operating in the media system of FIG. 1;

FIGS. 5A and 5B illustrate examples of the operation of the present invention; and

FIG. 6 illustrates a further example of the operation of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The embodiments set forth below represent the necessary information to enable those skilled in the art to practice the invention. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the invention and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure and the accompanying claims.
Note that at times the system of the present invention is described as performing a certain function. However, one of ordinary skill in the art would know that the program is what is performing the function rather than the entity of the system itself.
Although aspects of one implementation of the present invention are depicted as being stored in memory, one skilled in the art will appreciate that all or part of systems and methods consistent with the present invention may be stored on or read from other computer-readable media, such as secondary storage devices, like hard disks, floppy disks, and CD-ROM, a carrier wave received from a network such as the Internet, or other forms of ROM or RAM either currently known or later developed. Further, although specific components of the system have been described, one skilled in the art will appreciate that a system suitable for use with the methods and systems consistent with the present invention may contain additional or different components.
FIG. 1 illustrates a media system according to an exemplary embodiment of the present invention. In general, the media system for dynamic video segment recommendations 10 includes an input device 12, such as but not limited to a keyboard, keypad, smartphone, or remote control for operation by an associated user 14, and a media playback system 16. In this exemplary embodiment, the media playback system 16 includes a media player 18 and a display device 20.
The media player 18 may be, for example, a personal computer, a set-top box (STB) for playing digital television content received from a television content provider, a Digital Video Recorder (DVR) for playing previously recorded video content such as previously recorded television content received from a television content provider, an Apple TV® device for playing downloaded content that has been purchased or rented from a remote media distribution service such as the Apple®D iTunes® store, a Digital Versatile Disc (DVD) player, or the like. The media player 18 may be connected to the display device 20 via any desired audio/video connection such as, for example, a High Definition Multimedia Interface (HDMI) connection, a Digital Video Interface (DVI) connection, a coaxial cable connection, or the like. The display device 20 may be, for example, a computer display screen, a television (TV), or the like. In an alternative embodiment, the display device 20 may be incorporated into the media player 18.
The media player 18 includes a media playback function 24 and a media segment playback function 26, each of which may be implemented in software, hardware, or a combination thereof. The media playback function 24 generally operates to provide playback of media items obtained from a content source 28. In the exemplary embodiment, the media items are video items. As such, the media playback function 24 provides playback of the video items and presentation of the video items to the user 14 and any other nearby users via the display device 20. The content source 28 varies depending on the particular implementation of the media player 18. For example, if the media player 18 is a STB, then the content source 28 may be a television content distribution network such as a Cable Television (CATV) network. As another example, if the media player 18 is a DVD player, then the content source 28 is a DVD. As a final example, if the media player 18 is a device such as an Apple TV® device, then the content source 28 may be a remote media distribution service such as the Apple® iTunes® store, where the media player 18 has access to the remote media distribution service via a network such as, for example, the Internet.
With reference to FIGS. 1 and 2, in one exemplary embodiment of the present invention, while a user 14 is watching a video (step S100), the media player 18 notifies a Segment Similarity Analyzer (SSA) 30 of the current segment the user is viewing (step S102). Along with this is sent information such as user's current playback location, metadata information associated to that segment, metadata of the video scene for that segment, and so forth. Once the SSA 30 receives the information, it looks for video scenes that have metadata similar to the current segment/scene from, for example, a video database (step S104). The video database may be part of the SSA 30, or an external source. The metadata similarities can be identified in terms of characters, speech, semantics, events in the scene, timing, scene location, audio effects, soundtrack and so forth. Any model that uses MPEG-7 framework for associating metadata similarities among videos can be used. An example of one such model is disclosed in the related art document entitled, “A Video Metadata Model Supporting Personalization & Recommendation in Video-based Services” by Tsinaraki et al. (July 2001), the disclosure of which is incorporated herein by reference in its entirety.
Moreover, for each segment, the metadata information identifying and describing the segment may include information describing the content of the segment of the media item. For example, the information may describe the segment as containing an action scene, a romantic scene, or the like. As another example, if the media item is one of the Star Wars movies, the information may describe the content of the segment more specifically as containing a Princess Leia scene, a Darth Vader scene, a droid scene, a space-fighting scene, or the like. As another example, the information describing the segment may include a list of actors or actresses appearing in the segment and/or a description of activities that take place in the segment. The information describing the content of the segments of the media item may be information provided by a producer or creator of the media item, information such as annotations provided by one or more users that have previously viewed the media item, or the like, or any combination thereof.
Also consistent with the present invention, the metadata may be, for example, tags, annotations, a script or lyrics for the media item, closed-captioning information, sub-titles, or the like. Moreover, the media segment playback function 26 may also utilize a combination of audio and frame analysis techniques. For example, in addition to using frame analysis techniques to detect violent content such as, for example, smoke or blood pixels, the system may also utilize audio analysis techniques for detecting audio content such as in the form of, for example, gunshot sounds.
Once a matching video scene is found, the SSA 30 adds that scene to its list of matches found (step S106). Note that this can be a one-time process, and does not need to be repeated for every other user. However, there are scenarios where it may be desired to repeat this process. One example is a distributed scenario, where each device has its own SSA 30 which performs this operation for its own user. Another example is where new content is being continually added to the video database, and newer results may be wanted in the recommendations. For example, in a database or search engine, the video metadata may be indexed, and the corresponding scenes are then associated with the appropriate entry into the index. Thus, segments from new videos would be appended to the appropriate entries in the index. For a distributed system or a P2P system, the process may need to be repeated multiple times.
Next, the system decides as to which of the scenes are to be displayed to the user depending on a user's profile, preferences and viewing history. The system collects metadata of the scenes that the user had not preferred to watch or had skipped earlier. In addition, the system may also collect metadata of the scenes that the user has already watched, and hence need not be recommended again. For instance, the system recommends a segment from the movie “Rambo 4” when a user is watching the movie “UHF”, and the user then watches that segment. However, the same segment from “Rambo 4” shows up as a potential recommendation for a segment in another movie “Hot Shots 2”. The system notices that the user has already watched the recommended segment before, so it does not recommend it again. Alternatively, it may be programmed to do the opposite and recommend the segment from “Rambo 4” as well as the segment from “UHF” (discovered from the user's viewing history) for that segment in “Hot Shots 2”. Any/all of such scenes are filtered out at this stage. Also, scenes that the user has already watched may be filtered out. Then, from among the rest of the scenes that survive and match with the user's profile, the system identifies the scenes that have been rated highly by other users or recommended by other users. For example, this may include users from a user's social network, or users that have a profile similar to the user, or the users whose previous video uploads are similar in characteristics to the user's video uploads, or users who match in terms of the type of videos that the user watches. Finally, the scenes are displayed to the user by the media segment playback function 26 of the media device 18 in the order that best matched and/or were rated highly (step S108). For example, if 100 scenes match a video segment, a user X may see a scene 10 as the first matching video scene, whereas a user Y may see a different scene 50 as his first match. Note that the user can at any time opt not to see the “Similar Scenes”, by turning off a “Show Similar Scenes” button (not shown).
Once the scenes to be displayed are identified, the system can display them to the user in several ways, such as video clips, links to clips, or pop-ups. The recommendations may also be provided to a supplementary mobile device (e.g., a smartphone or a remote control equipped with a display).
In step S110, if the main video is over, the user can end the session, or if the user has viewed one of the matching scenes or clips, the user can then return to the main video and continue to watch it until completion.
Consider, for example, that a user is watching a football game on Youtube. When the user reaches a video segment with metadata identified as “touchdown”, other segments with metadata that includes “touchdown” are discovered and filtered based on his previous history, and displayed to the user as recommendations, for instance in the form of keyframes in the right-end of the browser for viewing. The user at any point may continue to watch the “touchdown” video segment from the current video or from the recommendations made, i.e., the user is not required to wait until the end of the event or the video to receive recommendations and the recommendations will be updated as soon as his playback position and the associated metadata changes (e.g., the current metadata changes to “coach swearing” and the recommendation keyframes get updated to show other instances of the same (or other) coaches losing it).
With reference to FIG. 3, in another exemplary embodiment of the present invention, the media player 18 may decide as to when (or not) to notify the SSA 30 of the current segment (that the user is watching (step S200)) based on some threshold parameters. For example, the media player 18 may take into consideration the user's trick play mode behavior (such as skip, fast forward, etc.) on the current scene (step S202). Certain trick play modes on a scene are indicative of the user finding the scene undesirable (e.g., user skips the scene), and hence no segment information is sent to SSA 30 for scene analysis. For example, if the user 14 skips over a particular segment of the media item, the media player 18 may ensure that similar segments of the media item are not selected as segments of interest during trick play mode, or may reduce the priority assigned to similar segments. As another example, the media player 18 may wait for the user to watch for a certain time period within the scene (e.g., half the length of the scene (step S204)), before which it notifies SSA 30 of the scene and playback information. Thus, if the preset time period threshold has been met, the media player 18 sends the SSA 30 the metadata of the scene (step S206). The SSA 30 then analyzes the user profile and displays recommendations to the user back at the media player 18 (step S208).
With reference to FIG. 4, in another exemplary embodiment of the present invention, the recommendations can be sent to the user based on user preference of how and/or when he wants to receive them. Such preferences can be indicated via the preference profile page of the user with the media player 18. The media player 18 may include a simple option (in a form of a button) as “Enable/disable recommendations”, and the user may simply click on the button to turn the recommendations on/off (see, for example, button 60 discussed below). While the user is watching a video segment (step S300) and selects to enable (to receive) the recommendations (step S302), the media player 18 may look through user's preference as to when and at what frequency he may wish to receive them (step S304). For example, a user may wish to have the recommendations “only once at the start of a new scene”. Several other options include “user can specify the frequency rate for receiving recommendations (e.g., twice within a scene) and within what part of the scene (such as start/end/certain offset from the start of the scene, etc.)”. If no preferences are set by the user, a default action can be to send recommendations at the beginning of every new scene.
The SSA 30 then analyzes the user profile for matching recommendations and displays recommendations to the user at the media player 18 (step S306).
Referring to FIG. 5A, consider, for example, that a user (Bob) admires Barack Obama and is searching for “Barack Obama” videos on his favorite online video service. The media system 10 returns a set of videos and Bob chooses one that looks interesting and he watches it. While watching the video he comes across a particular segment of the video that he enjoys—it features Mr. Obama discussing the energy policy with his opponent John McCain. Bob pauses the video on this segment 50 and then clicks on a “Find Similar Segments” button 60 in the display of the media player 18. In this case, Bob has indicated that recommendations be sent to him only upon his request.
Clicking the button 60 instructs the SSA 30 of the media system 10 to find other segments or scenes that contain content that is similar to the current segment-content that is related by metadata and/or by Bob's previous viewing patterns. For example, the metadata may be in the form of segments containing key words such as “Obama”, “McCain” and “energy policy” thereby showing a related segment of Mr. Obama. Alternatively, Bob's previous viewing patterns could be based on Bob watching a particular Obama segment five times. As shown in FIG. 5B, the media system 10 quickly returns several segments 70A, 70B, 70C that have been bookmarked for the appropriate segment to be queued for easy access by the user.
Because the media system 10 has “watched” his behavior in previous viewings of videos, it has determined that Bob is more likely to prefer segments in which Obama is portrayed favorably. Thus, it usually rules out segments provided by Fox News and favors segments from MSNBC. Bob watches a returned segment (a segment “recommended” by the system based on tags and Bob's preferences) that features Obama speaking about “Change” in Germany. Bob again pauses the player and clicks on “Find Similar Segments” button 60. The media system 10 immediately returns a set of segments that are uncannily similar to the speech in Germany segment.
With reference to FIG. 6, as an another example, consider that a user (Bob) is interested in watching “Dog Show” videos on Youtube. He has, however, explicitly indicated in his preferences profile page that he would like to receive video segment recommendations for any scenes that he has watched for at least 1 minute, and that the recommendations be presented to him only once during that scene. He finds some videos on Youtube, but is particularly interested in a video and starts watching it. He however starts to skip some of the scenes in that video and comes across a segment 80 with “Dogs Jumping” all around. Bob starts to watch it and just when he is finished watching the first 1 minute of that segment, he immediately finds a collection of scenes 90A, 90B, 90C, 90D that contain “Dog Jumping Clips” displayed to him (as shown in FIG. 6 at the lower right).
He quickly scrolls to one of these clips, but this time Bob would like to receive recommendations after he has watched a scene for just 30 seconds. He would also like to continue to receive recommendations repetitively at a fixed interval of 15 seconds from that point on. He makes these changes in his preference profile page, and starts to watch the clip. He comes across a segment with “Boxer Hopping”, and after watching the first 30 seconds of that segment, he is presented with a collection of “Boxer Hopping clips”. None of those clips interests him, and after 15 seconds, he next receives a new set of recommendations. As he finds them interesting, he continues to watch the rest of the clips.
The present invention has substantial opportunity for variation without departing from the spirit or scope of the present invention. For example, while the embodiments discussed herein are directed to personal or in-home playback, the present invention is not limited thereto. Further, while the examples refer to video segments or scenes, the present invention is not limited thereto and other forms of media content are contemplated herein.
Those skilled in the art will recognize improvements and modifications to the preferred embodiments of the present invention. All such improvements and modifications are considered within the scope of the concepts disclosed herein and the claims that follow.

Claims

1. A method of dynamically recommending media segments to a user, comprising:

analyzing and gathering metadata of a currently viewed media segment of a main media content based on a current playback location;

identifying other media segments, from the main media content or other media content, that are similar to the currently viewed media segment; and

dynamically recommending at least some of the identified other, similar media segments to the user.

2. The method of claim 1, wherein the media segments comprise video segments.

3. The method of claim 2, wherein only after the currently viewed video segment has been played back for a time period greater than a preset time period threshold are other, similar video segments identified.

4. The method of claim 2, wherein the metadata is identified as at least one of characters, speech, audio effects, soundtrack, semantics, events in a scene, timing, or a scene location.

5. The method of claim 4, further comprising:

matching the metadata of the currently viewed video segment against metadata of all video segments available in a video database; and

storing a list of all the video segments that match.

6. The method of claim 2, further comprising:

gathering user information including one of a previous viewing history of the user, a user profile, or a user preference; and

finding video segments identified that most closely match with the gathered user information.

7. The method of claim 6, further comprising:

identifying the found video segments that have been determined to match one of a viewing history, profile, or preference of other users.

8. The method of claim 6, further comprising:

displaying the video segments that most closely match with the previous viewing history of the user.

9. The method of claim 8, wherein the video segments are displayed as at least one of video clips, links, pop-ups or notifications.

10. The method of claim 2, wherein the recommended, similar video segments are displayed upon request by the user.

11. The method of claim 2, wherein the recommended, similar video segments are displayed upon subsequent video segment changes.

12. The method of claim 1, further comprising continuously updating the similar media segments depending on the current playback location of the user.

13. The method of claim 1, further comprising clicking a button on a display interface to permit the user to enable/disable receipt of the recommended, similar media segments.

14. A video scene similarity assessment method, comprising:

analyzing and gathering metadata of a currently viewed video scene of a main video content based on a current playback location;

matching the metadata of the currently viewed video scene against metadata of all video scenes available in a collection of videos, the metadata being identified as at least one of characters, speech, audio effects, soundtrack, semantics, events in a scene, timing, or a scene location; and

storing a list of all the video scenes that match.

15. The method of claim 14, further comprising:

identifying other video scenes, from the main video content or other video content, that are similar to the currently viewed video scene;

gathering user information including one of a previous viewing history of a user, a user profile, or a user preference; and

finding video scenes identified that most closely match with the gathered user information.

16. A media system for dynamically recommending video scenes to a user, comprising:

means for detecting metadata of a currently viewed video scene of a video content;

means for identifying other video scenes, from the video content or other video content, that are similar to the currently viewed video scene; and

means for dynamically recommending at least some of the identified other, similar video scenes to the user based on a previous viewing history of the user.

17. The media system of claim 16, wherein the metadata is detected based on a current playback location.

18. The media system of claim 16, wherein only after the currently viewed video scene has been played back for a time period greater than a preset time period threshold are other, similar video scenes identified.

19. The media system of claim 16, wherein the metadata is identified as at least one of characters, speech, audio effects, soundtrack, semantics, events in a scene, timing, or a scene location.

20. The media system of claim 19, further comprising:

means for matching the metadata of the currently viewed video scene against metadata of all video scenes available in a video database; and

means for storing a list of all the video scenes that match.

21. The media system of claim 16, further comprising:

means for gathering user information including one of a previous viewing history of the user, a user profile, or a user preference; and

means for finding video scenes identified that most closely match with the gathered user information.

22. The media system of claim 21, further comprising:

means for identifying the found video scenes that have been determined to match one of a viewing history, profile, or preference of other users.

23. The media system of claim 22, wherein the other users include users from a social network of the user, or users that have a profile similar to the user, or users whose previous video uploads are similar in characteristics to video uploads of the user, or users who match in terms of type of videos that the user watches.

24. The media system of claim 21, further comprising:

means for displaying the video scenes that most closely match with the previous viewing history of the user.

25. The media system of claim 24, wherein the video scenes are displayed as at least one of video clips, links, pop-ups or notifications.

26. The media system of claim 16, wherein the recommended, similar video scenes are displayed upon request by the user.

27. The media system of claim 16, wherein the recommended, similar video scenes are displayed upon subsequent video scene changes.

28. The media system of claim 17, wherein the means for dynamically recommending continuously updates the recommended, similar video scenes depending on the current playback location of the user.

29. The media system of claim 16, further comprising means for enabling/disabling receipt of the recommended, similar video scenes.

30. A computer readable medium comprising software for instructing a media system to:

detect and gather metadata of a currently viewed media segment of a primary media content based on a current playback location;

identify other media segments, from the primary media content or additional media content, that are similar to the currently viewed media segment; and

dynamically recommend at least some of the identified additional, similar media segments to a user based on previous viewing patterns of the user.

31. A media system for dynamically recommending video segments to a user, comprising:

a media player which detects metadata of a currently viewed video scene of a video content based on a current playback location within the video content;

a segment similarity analyzer which identifies other video scenes, from the video content or other video content, that are similar to the currently viewed video scene, and dynamically recommends at least some of the identified other, similar video scenes to the user based on a previous viewing history of the user; and

a display device which displays the recommended, similar video scenes that most closely match with the previous viewing history of the user.

32. The media system of claim 31, wherein the recommended, similar video scenes are displayed as at least one of video clips, links, pop-ups or notifications.

33. The media system of claim 31, wherein the media system continuously updates the recommended, similar video scenes depending on the current playback location of the user.

34. The media system of claim 31, further comprising a button disposed on a display interface of the display device which permits the user to enable/disable receipt of the recommended, similar video scenes, by clicking thereon.

35. The media system of claim 31, wherein only after the currently viewed video scene has been played back for a time period greater than a preset time period threshold are other, similar video scenes identified.

36. The media system of claim 31, wherein other, similar video scenes are identified and one of selected, not selected, or prioritized based on whether a trick play mode, including at least one of a skip mode or a fast forward mode, has taken place with respect to the currently viewed video scene.

37. The method of claim 7, wherein the other users include users from a social network of the user, or users that have a profile similar to the user, or users whose previous video uploads are similar in characteristics to video uploads of the user, or users who match in terms of type of videos that the user watches.