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A Human-Centered Computing Framework to Enable Personalized News Video Recommendation

A Human-Centered Computing Framework to Enable Personalized News Video Recommendation. 오준혁 (Oh Jun- hyuk ). Questions. How to detect news topic from video? How to measure inter-topic association? How to measure interestingness of news topic?. Contents. Introduction Related Work

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A Human-Centered Computing Framework to Enable Personalized News Video Recommendation

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  1. A Human-Centered Computing Framework to Enable Personalized News Video Recommendation 오준혁(Oh Jun-hyuk)

  2. Questions • How to detect news topic from video? • How to measure inter-topic association? • How to measure interestingness of news topic?

  3. Contents • Introduction • Related Work • User-Adaptive News Topic Recommendation • News Topic Detection • Topic Association Extraction • Interestingness Scores of News Topics • Hyperbolic Topic Network Visualization • Personalized Topic Network Generation • Personalized News Video Recommendation • Algorithm Evaluation • Conclusions

  4. Contents • Introduction • Related Work • User-Adaptive News Topic Recommendation • News Topic Detection • Topic Association Extraction • Interestingness Scores of News Topics • Hyperbolic Topic Network Visualization • Personalized Topic Network Generation • Personalized News Video Recommendation • Algorithm Evaluation • Conclusions

  5. Introduction • News video recommendation in CNN • Not related to the current news topic and user profile • Users need to follow(subscribe) news topics manually

  6. Introduction • Video recommendation in YouTube • Related to the current video topic and user profile but not visualized

  7. Introduction • Topic Network • Visualize news videos and represent inter-topic association. • Hyperbolic visualization • Enables interactive topic network navigation(browsing). • Recommend the news topics of interest according to the personal preferences.

  8. Contents • Introduction • Related Work • User-Adaptive News Topic Recommendation • News Topic Detection • Topic Association Extraction • Interestingness Scores of News Topics • Hyperbolic Topic Network Visualization • Personalized Topic Network Generation • Personalized News Video Recommendation • Algorithm Evaluation • Conclusions

  9. Related Work 1. Automatic news topic detection • Identification of individual topics within a broadcast news video by detecting the boundaries where the topic of discussion changes. • Special program structures and styles can be used to detect boundaries. KBS news broadcast

  10. Related Work 2. News visualization • Existing visualization systems disclose all the available news topics to news seekers without considering interestingness of topics.  It will be better to provide a small number of interesting news!

  11. Contents • Introduction • Related Work • User-Adaptive News Topic Recommendation • News Topic Detection • Topic Association Extraction • Interestingness Scores of News Topics • Hyperbolic Topic Network Visualization • Personalized Topic Network Generation • Personalized News Video Recommendation • Algorithm Evaluation • Conclusions

  12. User-Adaptive News Topic Recommendation • Goal : recommend the news topics of interest by incorporating topic network and hyperbolic visualization. • To-do List • News Topic Detection • Topic Association Extraction • Interestingness Scores of News Topics • Hyperbolic Topic Network Visualization • Personalized Topic Network Generation

  13. Contents • Introduction • Related Work • User-Adaptive News Topic Recommendation • News Topic Detection • Topic Association Extraction • Interestingness Scores of News Topics • Hyperbolic Topic Network Visualization • Personalized Topic Network Generation • Personalized News Video Recommendation • Algorithm Evaluation • Conclusions

  14. News Topic Detection • Define a set of over 4,000 elemental news topics. • Three major sources are integrated • Audio, Video, Closed Caption

  15. News Topic Detection

  16. News Topic Detection – Closed Captions • Natural Language Processing(NLP) is conducted. • Closed Captions are segmented into a set of keywords. • Special text sentences are removed by syntax parser • ex) “CNN’s Andrew reports from Seoul”  not related to the topic • TreeTagger is used to extract the POS(part-of-speech) information • POS : a linguistic category of words (lexical category) • LingPipe is used to extract keywords.

  17. News Topic Detection – Closed Captions

  18. News Topic Detection

  19. News Topic Detection – Audio • Automatic Speech Recognition(ASR) system is used to translate the audio channel to a text transcription. • Audio  Text  processed in a similar way to closed caption. Hidden Markov Model for speech recognition

  20. News Topic Detection

  21. News Topic Detection – Video • Detect video objects(text area, human face) because they provide important clues about news story. • Confidence map is used to measure the importance of video objects in video.

  22. News Topic Detection

  23. Contents • Introduction • Related Work • User-Adaptive News Topic Recommendation • News Topic Detection • Topic Association Extraction • Interestingness Scores of News Topics • Hyperbolic Topic Network Visualization • Personalized Topic Network Generation • Personalized News Video Recommendation • Algorithm Evaluation • Conclusions

  24. Topic Association Extraction • Inter-topic association(inter-topic contextual relationship) • d(Ci , Cj ) : The length of the shortest path between the news topics by searching the relevant keywords for news topic interpretation from WordNet. • ψ(Ci, Cj) : the co-occurrence probability between the relevant news topics obtained in news topic detection process.  The frequency of co-occurrence of two news topic keywords in the same video. • ex) In a news video, “PSY”, “Music” co-occurs.

  25. Topic Association Extraction • WordNet : a lexical database for the English language. • Provide a graph representing semantic relationship between words.

  26. Topic Association Extraction • Topic network can be generated from topic association • News topics are organized according to the strength of their association • Allow news seekers to easily recognize global overview of large-scale news videos at the first glance.

  27. Contents • Introduction • Related Work • User-Adaptive News Topic Recommendation • News Topic Detection • Topic Association Extraction • Interestingness Scores of News Topics • Hyperbolic Topic Network Visualization • Personalized Topic Network Generation • Personalized News Video Recommendation • Algorithm Evaluation • Conclusions

  28. Interestingness Scores of News Topics • Interestingness Score • m(Ci) : the number of TV channels or news programs which have discussed the given news topic Ci. Popularity • k(Ci) : the number of news topics linked with the given news topic Ci on the topic network. Importance (similar to PageRank) • Used to highlight the most interesting news topics and eliminate the less interesting news topics for reducing the visual complexity for large-scale topic network visualization.

  29. Interestingness Scores of News Topics • PageRank Algorithm

  30. Summary & Answers • How to detect news topic from video? • Define a set of news topic. • Integrate multi-modal sources • Closed caption : Natural language processing • Audio : Automatic speech recognition • Video : video object extraction and classification • How to measure inter-topic association? • Keyword association : length of path between keywords in WordNet • Co-occurrence : the probability of co-occurrence obtained in news topic detection process. • How to measure interestingness of news topic? • Popularity : the number of TV channels or news programs which have discussed the given news topic • Importance : the number of news topics linked with the given news topic on the topic network.

  31. Thank you Q&A

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