AVATAR: An Improved Solution for Personalized TV based on Semantic Inference 2006 IEEE Transactions on Consumer Electronics Yolanda Blanco Fernández, José J. Pazos Arias, Martín López Nores, Alberto Gil Solla and Manuel Ramos Cabrer The University of Vigo in Spain Presented by Nam, KwangHyun, IDS Lab., Seoul National University

Copyright  2008 by CEBT Outline  Motivation: TV recommender systems  Semantic Web and Personalized TV  The AVATAR system: Ontology about TV domain User profiles Recommendation strategies  Conclusions and Future Work 2

Copyright  2008 by CEBT Motivation: TV recommender systems  Migration from analog to digital TV (DTV) More channels in the same bandwidth Interactive applications and audiovisual contents  Overwhelming amount of irrelevant programs for viewers: Tools to search personalized contents according to the user preferences are necessary: TV recommender systems. Functionality similar to the Internet search engines: – Simple syntactic comparison  Limited quality results – Solution: Semantic Web  Semantic search engines 3

Copyright  2008 by CEBT Semantic Web & Personalization in TV  Semantic Web: Description of Web resources by metadata Inference of semantic relationships between them  AVATAR: AdVAnced Telematic search of Audiovisual contents by semantic Reasoning: This system incorporates advanced semantic inference capabilities involving the knowledge about the TV domain. Reasoning on the user preferences and semantics of TV contents. Goal: To discover relationships between user preferences and suggested programs. 4

Copyright  2008 by CEBT AVATAR: A Knowledge-Based System  Three main blocks in the AVATAR structure: Knowledge about the TV domain: OWL ontology Knowledge about the user preferences: Personal profiles A mechanism for establishing correspondences between TV contents and viewers: Recommendation strategies 5

Copyright  2008 by CEBT Architecture of the AVATAR system  Recommender Agent  Ranking Agent  Information Agent  Feedback Agent Data extracted from the viewing behavior of user Success or failure in the recommendations previously elaborated by AVATAR 6

Copyright  2008 by CEBT TV Ontology  Conceptualization of a specific application domain by identifying typical concepts and relationships between them. Knowledge base (KB): Classes and properties organized hierarchically. Description base (DB): Specific instances of classes and properties defined in KB.  In AVATAR: Hierarchy of TV contents to identify general categories to which programs belong. Programs and their attributes (semantic characteristics) are instances related to each other by properties. 7

Copyright  2008 by CEBT The Hierarchy of TV Contents IS-A relations 8

Copyright  2008 by CEBT The User Profiles in AVATAR  Preferences consist of: Specific programs the user likes or dislikes. Their semantic characteristics (cast, genre, topic, etc.) Class hierarchies which these programs belong to Level of interest for each class and instance: Index DOI (Degree of Interest)  [-1,1] – The acceptance or rejection of the user to the suggestion – The percentage of the program watched by the user – The time elapsed until the user decides to watch the offered program 9

Copyright  2008 by CEBT Example of Ontology-Profile Dancing with the Stars (0.4) Dancing (0.2) Jennifer Lopez (0.7) Chayanne (0.3) Jerry Maguire (0.9) Vanilla Sky (0.85) Tom Cruise (0.85) Romance (0.7) Mystery (0.8) Mystery Movies (0.85) Romance Movies (0.88) Movies (0.87) Cinema Programs (0.6) TV Contents Entertainment Shows (0.2) hasGenre hasTopic hasArtist hasActor hasGenre Reality shows (0.2) Property InstanceOf subClassOf 10  User Profile: Vanilla Sky, Jerry Maguire and Dancing with the Stars Semantic characteristics Class hierarchy

Copyright  2008 by CEBT Personalization Strategy  Goal: To select users in AVATAR to whom a target content must be suggested.  Target content can be: proposed by a user who liked it or a new program in AVATAR  Hybrid strategy that fuses: Content-based methods: suggest contents similar to those the user found appealing in the past. Collaborative filtering: offer contents interesting for other like- minded users (neighbors). 11

Copyright  2008 by CEBT Content-based Strategy: Semantic matching  Target content is offered to a user if it is similar to: His/Her personal preferences, or to His/Her neighbors’ preferences  Semantic similarity between two programs: Hierarchical semantic similarity: – Defines explicitly to classify contents – Depends on the location of the classes of boths programs in the TV content hierarchy Inferential semantic similarity: – Based on discovering implicit relations between the matched programs. Compute semantic matching between the target content and the user’s preferences. 12

Copyright  2008 by CEBT Example of Semantic Similarity  User Profile: Welcome to Tokyo and Born on the 4th of July  Target content: Last Samurai r Welcome to Tokyo (0.9) Born on the 4th of July(0.8) Last Samurai Tokyo (0.9) Kyoto Japan Drama Movies Action Movies Cinema Programs TV Contents Tom Cruise (1) … … Property InstanceOf subClassOf hasActor hasPlace 13 Tourism Documentary Movie Hierarchical similarity Inferential similarity

Copyright  2008 by CEBT Collaborative Strategy: Semantic Prediction  Neighborhood formation of a given user: Rating vector for each user: – It contains DOI indexes defined in the user profile for all of the classes in the TV content hierarchy. Computes Pearson-r correlation between the rating vectors – It results in the highest values for those users interested in programs belonging to the same hierarchy classes. – ‘Pearson-r correlation’ shows similiarity between two different object Computes semantic prediction – It estimates the level of interest of the target user in relation to the considered content, according to his/her neighbors’ interest. 14

Copyright  2008 by CEBT Example of Collaborative Strategy  Target user: User U  Target content: Dancing with the stars  Suggest target content to user U if most of his/her neighbors had watched and enjoyed this show. But... Only one neighbor knows target content. His/Her level of interest is medium. 15 User UUser N1User N2User N3 Rolling Stones (0.8)Music! (0.9)Dancing with the Stars (0.6) Wheel of Fortune (0.7) Runaway bride (0.9)Dance with me (0.85) Videoclips (0.8)Friends (1) Frasier (0.85)Desperate housewives (0.65) Monster-in-law (0.85) About celebrities (-0.8)

Copyright  2008 by CEBT Final Recommendation  ‘Two-chance’ mechanism In order to fuse the content-based and collaborative strategies Algorithm 16 Recommend (target-content, user-profile){ if(semantic_matching (target-content, user-profile) ≥ β Match :1 st chance return TRUE : suggested elseif (semantic_prediction (target-content, user-profile) ≥ β Pred : 2 nd chance return TRUE : suggested else return FALSE : discarded ( 0 ≤ β Match, β Pred ≤ 1 ) }

Copyright  2008 by CEBT Conclusion and Future Work  Semantic inference is able to discover interesting relationships between contents, not considered in existing approaches. AVATAR represents a step forward in the future personalized TV.  Flexible approach: It can be used in other personalization domains: not exclusively joined to the TV domain. It is valid for both models of single-user and multiple target audience.  Future work: Experimental evaluation of the presented hybrid strategy Comparison to other existing proposals: – item-based collaborative filtering, – association rules, etc. 17