AVATAR: An Advanced Multi-Agent Recommender System of Personalized TV Contents by Semantic Reasoning WISE 2004- November 23rd, 2004 Yolanda Blanco Fernandez yolanda@det vigo.es Department of Telematic Engineering, University of Vigo(Spain)
Slide 1/14 AVATAR: An Advanced Multi-Agent Recommender System of Personalized TV Contents by Semantic Reasoning WISE 2004 - November 23rd, 2004 Yolanda Blanco Fernández yolanda@det.uvigo.es Department of Telematic Engineering, University of Vigo (Spain)
Motivation of TV Recommender Systems a Migration from analogue to digital tv. Recommender Systems ● Related Work Implications: e Main Design Decisions e The Architecture More channels in the same bandwidth ● The Contributions of AVATAR Software applications mixed with audiovisual contents ●TheL| KO language ● Conclusions ● Further Work
● Motivation of TV Recommender Systems ● Related Work ● Main Design Decisions ● The Architecture ● The Contributions of AVATAR ● The LIKO language ● Conclusions ● Further Work Slide 2/14 Motivation of TV Recommender Systems ■ Migration from analogue to digital TV. ■ Implications: ◆ More channels in the same bandwidth. ◆ Software applications mixed with audiovisual contents. ■ Disoriented users among large amount of irrelevant information. ◆ User cannot use this new type of TV efficiently. ◆ Necessary tools to find interesting TV programs
Motivation of TV Recommender Systems ● Motivation of Tv Migration from analogue to digital Tv Recommender Systems ● Related Work Implications e Main Design Decisions e The Architecture More channels in the same bandwidth ● The Contributions of AVATAR Software applications mixed with audiovisual contents ●TheL| KO language ● Conclusions a Disoriented users among large amount of irrelevant ● Further Work information User cannot use this new type of Tv efficiently. Necessary tools to find interesting TV programs
● Motivation of TV Recommender Systems ● Related Work ● Main Design Decisions ● The Architecture ● The Contributions of AVATAR ● The LIKO language ● Conclusions ● Further Work Slide 2/14 Motivation of TV Recommender Systems ■ Migration from analogue to digital TV. ■ Implications: ◆ More channels in the same bandwidth. ◆ Software applications mixed with audiovisual contents. ■ Disoriented users among large amount of irrelevant information. ◆ User cannot use this new type of TV efficiently. ◆ Necessary tools to find interesting TV programs
Related Work ● Motivation of Tv a Different approaches in the field of Tv personalization tools Recommender Systems Bayesian techniques e Main Design Decisions Decision trees e The Architecture Content-based methods ● The Contributions of AVATAR Collaborative filtering ●TheL| KO language ● Conclusions ● Further Work
● Motivation of TV Recommender Systems ● Related Work ● Main Design Decisions ● The Architecture ● The Contributions of AVATAR ● The LIKO language ● Conclusions ● Further Work Slide 3/14 Related Work ■ Different approaches in the field of TV personalization tools: ◆ Bayesian techniques ◆ Decision trees ◆ Content-based methods ◆ Collaborative filtering ◆ ... ■ A common base: limitation in reasoning capabilities. ◆ Mechanisms to represent the knowledge of TV domain are not used in previous proposals. ◆ Reasoning process allows to obtain enhaced recommendations
Related Work ● Motivation of Tv Different approaches in the field of TV personalization tools Recommender Systems ◆ Bayesian techniques e Main Design Decisions Decision trees e The Architecture o Content-based methods ● The Contributions of AVATAR Collaborative filtering ●TheL| KO language ● Conclusions ● Further Work A common base: limitation in reasoning capabilities Mechanisms to represent the knowledge of tv domain are not used in previous proposals Reasoning process allows to obtain enhaced recommendations
● Motivation of TV Recommender Systems ● Related Work ● Main Design Decisions ● The Architecture ● The Contributions of AVATAR ● The LIKO language ● Conclusions ● Further Work Slide 3/14 Related Work ■ Different approaches in the field of TV personalization tools: ◆ Bayesian techniques ◆ Decision trees ◆ Content-based methods ◆ Collaborative filtering ◆ ... ■ A common base: limitation in reasoning capabilities. ◆ Mechanisms to represent the knowledge of TV domain are not used in previous proposals. ◆ Reasoning process allows to obtain enhaced recommendations