Automatic Video Recommendation

• US 20090006368A1
• Filed: 06/29/2007
• Published: 01/01/2009
• Est. Priority Date: 06/29/2007
• Status: Abandoned Application

• Current Assignee: Rovi Technologies Corporation (Adeia Inc.)
• Original Assignee: Microsoft Corporation
• Inventors: Yang, Bo, Mei, Tao, Hua, Xian-Sheng, Li, Shipeng, Yang, Linjun
• Application Number:
  US11/771,219
• Publication Number:
  US 20090006368A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/735 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/78 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/7844 using original textual content or text extracted from visual content or transcript of audio data
• CPC Class Codes: G06F 16/7847 using low-level visual features of the video content
• CPC Class Codes: H04N 21/466 Learning process for intelligent management, e.g. learning user preferences for recommending movies services using the results of monitoring in broadcast systems H04H60/61
• CPC Class Codes: H04N 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
• CPC Class Codes: H04N 21/472 End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification, for manipulating displayed content end-user interfaces for retrieving video data from a database G06F16/739; network services for supporting unicast streaming H04L65/612
• CPC Class Codes: H04N 7/17318 Direct or substantially direct transmission and handling of requests

Abstract

Automatic video recommendation is described. The recommendation does not require an existing user profile. The source videos are directly compared to a user selected video to determine relevance, which is then used as a basis for video recommendation. The comparison is performed with respect to a weighted feature set including at least one content-based feature, such as a visual feature, an aural feature and a content-derived textural feature. Multimodal implementation including multimodal features (e.g., visual, aural and textural) extracted from the videos is used for more reliable relevance ranking. One embodiment uses an indirect textural feature generated by automatic text categorization based on a set of predefined category hierarchy. Another embodiment uses self-learning based on user click-through history to improve relevance ranking.

Claims

1. A method for video recommendation, comprising:

obtaining a feature set of a user selected video object, the feature set including at least one content-based feature;

determining or assigning a relevance weight parameter set including a relevance weight parameter associated with the at least one content-based feature;

determining a relevance of each of a plurality of source video objects to the user selected video object with respect to the feature set and the relevance weight parameter set; and

generating a recommended video list of at least some of the plurality of source video objects according to a ranking of the relevance determined for each source video object.

2. The method as recited in claim 1, wherein the at least one content-based feature comprises a visual feature.

3. The method as recited in claim 2, wherein the visual feature comprises at least one of color histogram, motion intensity and shot frequency.

4. The method as recited in claim 1, wherein the at least one content-based feature comprises an aural feature.

5. The method as recited in claim 4, wherein the aural feature comprises at least one of an average aural tempo and a standard deviation of aural tempos.

6. The method as recited in claim 1, wherein the at least one content-based feature comprises a textual feature.

7. The method as recited in claim 6, wherein the textural feature comprises at least one of a text caption, a text generated by automated speech recognition, and a text generated by optical character recognition.

8. The method as recited in claim 6, wherein the textual feature comprises an indirect text generated by automatic text categorization based on a set of predefined category hierarchy.

9. The method as recited in claim 1, wherein the feature set comprises an indirect text generated by automatic text categorization based on a set of predefined category hierarchy, and wherein determining or assigning the relevance weight parameter set comprises:

determining a common ancestor of the user selected video object and the source video object in the predefined category hierarchy; and

determining an indirect text relevance at least partially based a distance information measuring hierarchical separation from the common ancestor to the user selected video object and the source video object.

10. The method as recited in claim 1, wherein the feature set is multimodal comprising a textural modality, a visual modality an aural modality, and wherein the content-based feature belongs to at least one of the textural, visual and aural modalities.

11. The method as recited in claim 1, wherein the feature set comprises multiple features each corresponding to one of a plurality of modalities.

12. The method as recited in claim 11, wherein determining or assigning the relevance weight parameter set comprises:

for each modality, adjusting relevance weight parameters within the modality.

13. The method as recited in claim 11, wherein determining or assigning the relevance weight parameter set comprises:

adjusting relevance weight parameters among the plurality of modalities.

14. The method as recited in claim 1, wherein determining or assigning the relevance weight parameter set comprises:

providing a user click-through history;

determining or adjusting the relevance weight parameter set according to the user click-through history.

15. The method as recited in claim 1, wherein generating the recommended video list is performed dynamically whenever a change has been detected with respect to the user selected video object.

16. The method as recited in claim 15, wherein the change with respect to the user selected video object comprises selection by a user a video object different from the current user selected video object.

17. The method as recited in claim 15, wherein the change with respect to the user selected video object comprises detection of a new now-playing content of the user selected video object, the new now-playing content being substantially different from a previously played content of the user selected video object such that a different recommended video list would be generated based on the new now-playing content.

18. A user interface used for automatic video recommendation, the user interface comprises:

a now-playing area for displaying a user selected video object;

a video content recommendation area for displaying a video recommendation list comprising a plurality of indicia each corresponding to a recommended source video object, wherein the video recommendation list is displayed according to a ranking of relevance determined for each recommended source video object relative to the user selected video object with respect to a feature set and the relevance weight parameter set, the feature set including at least one content-based feature; and

means for making a user selection of a recommended source video object among the displayed video recommendation list, wherein upon selecting the recommended source video object, the user interface dynamically updates the now-playing area and the video content recommendation area.

19. The user interface as recited in claim 18, further comprising:

a supplemental display area for displaying information related to the user selected video object.

20. One or more computer readable medium having stored thereupon a plurality of instructions that, when executed by one or more processors, causes the processor(s) to:

extract from a user selected video object a feature set including at least one content-based feature;

determine or assign a relevance weight parameter set including a relevance weight parameter associated with the at least one content-based feature;

determine a relevance of each of a plurality of source video objects to the user selected video object with respect to the feature set and the relevance weight parameter set; and

generate a recommended video list of at least some of the plurality of source video objects according to a ranking of the relevance determined for each source video object.