Internet New Web2.0 Trends-Recommendation Systems Lecture For Vtlv

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Internet New Web2.0 Trends-Recommendation Systems, Lecture For Vtlv conference
Presentation the new internet media recommendation systems principles collaborative filtering and content based filtering.
examples of recommendation services for music and video like:
pandora, lastfm,
video:
flixter...etc
more at www.dsp-ip.com

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Internet New Web2.0 Trends-Recommendation Systems Lecture For Vtlv

  1. 1. MEDIA RECOMMENDATION SYSTEMS Yossi Cohen CTO DSP-IP
  2. 2. AGENDA <ul><li>Why Do we need it? </li></ul><ul><li>What are recommendation systems? </li></ul><ul><ul><li>Content based </li></ul></ul><ul><ul><li>Collaborative </li></ul></ul><ul><ul><li>Hybrid </li></ul></ul><ul><li>Examples </li></ul><ul><ul><li>Music </li></ul></ul><ul><ul><li>Video </li></ul></ul><ul><li>Summary & Trends </li></ul>
  3. 3. WHY RECOMMENDATION ENGINE?
  4. 4. RECOMM. SYSTEM DEFINITION <ul><li>Recommender systems are a specific type of information filtering (IF) technique that attempt to present to the user information items ( movies , music , books , news , web pages ) the user is interested in. </li></ul><ul><li>To do this the user's profile is compared to some reference characteristics. These characteristics may be from the information item (the content-based approach) or the user's social environment (the collaborative filtering approach). </li></ul>
  5. 5. TA XONOMY
  6. 6. COLLABORATIVE FILTERING <ul><li>Collaborative filtering (CF) is the method of making automatic predictions (filtering) about the interests of a user by collecting taste information from many users (collaborating). The underlying assumption of CF approach is that those who agreed in the past tend to agree again in the future. For example, a collaborative filtering or recommendation system for music tastes could make predictions about which music a user should like given a partial list of that user's tastes (likes or dislikes). </li></ul>Source: TrustedOpinion
  7. 7. MUSIC RECOMMENDATION ENGINE
  8. 8. MUSIC RECOMMENDATION SERVICES
  9. 9. LAST.FM
  10. 10. MEEMIX <ul><li>Try to analyze the user taste in order to provide the best personalized music channel </li></ul>
  11. 11. VIDEO RECOMMENDATION ENGINE
  12. 12. MOVIELENS <ul><li>University research project </li></ul><ul><li>Web 0.5 GUI </li></ul><ul><li>You rate </li></ul><ul><li>Get Predictions </li></ul>
  13. 13. FLIXSTER <ul><li>Commercial version of MovieLens with better features & GUI </li></ul>Collaborative /Peer based Content based
  14. 14. SUMMARY
  15. 15. RECOMMENDATION SYSTEM IN <ul><li>Content Creators : </li></ul><ul><ul><li>Disney, HBO </li></ul></ul><ul><li>Social Web sites : </li></ul><ul><ul><li>FaceBook (MyTV/Video apps), MySpace </li></ul></ul><ul><li>Content Aggregators </li></ul><ul><ul><li>Magnify, Dabble, SuTree(Israel), Nebo </li></ul></ul><ul><li>Internet (Content) Recommenders </li></ul><ul><ul><li>BFN(Alpha-Israel), Mogad, MyStrends </li></ul></ul><ul><li>Mobile Content Recommenders </li></ul><ul><ul><li>JumpTap, Matchmine </li></ul></ul>
  16. 16. MORE PLACES FOR RECOM. SYSTEMS <ul><li>Channel Creation Platforms </li></ul><ul><li>Video Mesh-ups </li></ul>
  17. 17. TRENDS Future Present Past Process My Customized channel UGC / Pre defined channels Broadcast Pre defined channels Distribution Lean back Lean Forward Text search of clips Lean Back – open loop Consumption UGC + Legal Premium content+Meshups UGC content + Stolen Premium content Premium Content Content type
  18. 18. TRENDS <ul><li>Recommendation Systems closes the loop between content creator/distributers and the users </li></ul>
  19. 19. SUMMARY <ul><li>Recommendation engine will not stay only as a stand alone system like Google in general search </li></ul><ul><li>Evolve into serving platforms to provide lean-back / personal channel user experience like: Pandora, MeeMix, LastFM </li></ul><ul><li>Immersed into content aggregation and distribution platforms </li></ul><ul><li>Work as a portal (Google) or a download application (Last.FM, Veoh, MatchMine) </li></ul><ul><li>Use a lot of Flash, AIR, Apollo </li></ul>
  20. 20. DSP-IP SERVICES <ul><li>Outsourcing, consulting and development services </li></ul><ul><ul><li>Video encoding and streaming </li></ul></ul><ul><ul><li>Flash Video, VP6 </li></ul></ul><ul><ul><li>IPTV architecture and services </li></ul></ul><ul><ul><li>Recommendation System consulting </li></ul></ul><ul><ul><li>Video on DSP platforms (TI DSPs) </li></ul></ul><ul><ul><li>Video Advertisement in IPTV, Internet and mobile </li></ul></ul><ul><ul><li>Image processing </li></ul></ul>
  21. 21. DSP-IP CONTACT INFORMATION <ul><li>www.dsp-ip.com </li></ul><ul><li>Giborey Israel 20, POB 8323, Netanya, Israel </li></ul><ul><li>Office Phone: 09-8850956, Fax: 050- 8962910 </li></ul><ul><li>HR Services: </li></ul><ul><ul><li>Michal Porat </li></ul></ul><ul><ul><li>[email_address] </li></ul></ul><ul><ul><li>09-8651933, 054-2383689 </li></ul></ul><ul><li>Technology Management Services : </li></ul><ul><ul><li>Yossi Cohen </li></ul></ul><ul><ul><li>[email_address] </li></ul></ul><ul><ul><li>09-8850956, 054-5313092 </li></ul></ul>
  22. 22. RESOURCES <ul><li>Recommendation Systems http://en.wikipedia.org/wiki/Recommendation_system </li></ul><ul><li>Collaborative Filtering http://en.wikipedia.org/wiki/Collaborative_filtering </li></ul><ul><li>MeeMix www.meemix.com </li></ul><ul><li>MovieLens http://movielens.umn.edu/main </li></ul><ul><li>Flixster http://www.flixster.com/ </li></ul><ul><li>MyStrends www.mystrends.com </li></ul><ul><ul><li>http://www.moconews.net/entry/419-recommendation-engine-provider-mystrands-receives-25-million-in-funding/ </li></ul></ul><ul><li>Matchmine www.matchtime.com </li></ul><ul><li>http://www.killerstartups.com/Video-Music-Photo/matchmine--Media-Discovery-Platform/ </li></ul>

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