avatar an improved solution for personalized tv based on semantic inference yolanda blanco fern á...
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![Page 1: AVATAR An Improved Solution for Personalized TV based on Semantic Inference Yolanda Blanco Fern á ndez, Jos é J. Pazos Arias, Mart í n L ó pez Nores, Alberto](https://reader036.vdocuments.site/reader036/viewer/2022062421/56649d365503460f94a0e77f/html5/thumbnails/1.jpg)
AVATAR An Improved Solution for Personalized TV based on Semantic Inference
Yolanda Blanco Fernández,José J. Pazos Arias,Martín López Nores,
Alberto Gil Solla,Manuel Ramos Cabrer
bearhsu 20060425
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outline
Introduction Related Work The AVATAR Recommend System Example Conclusion
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outline
Introduction Related Work The AVATAR Recommend System Example Conclusion
![Page 4: AVATAR An Improved Solution for Personalized TV based on Semantic Inference Yolanda Blanco Fern á ndez, Jos é J. Pazos Arias, Mart í n L ó pez Nores, Alberto](https://reader036.vdocuments.site/reader036/viewer/2022062421/56649d365503460f94a0e77f/html5/thumbnails/4.jpg)
Introduction
DTV generation Huge number of channels & contents
will cause users to be disoriented A personal assistant is required
To know what’s available and how to find them
To furnish a highly-personalized viewing experience
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outline
Introduction Related Work The AVATAR Recommend System Example Conclusion
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Related Work
Recommender => suggestions according to users’ preferences & needs Hot in the last 2 decades in both TV
domain and outside of it Recommender Systems
Content-based Collaborative filtering
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Content-based methods
Quantify the similarity between users’ profiles & programs’ candidates
To define appropriate descriptions of the considered contents Usually a time consuming task
user
program
quantify
Suggestion/
Similarity
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Drawbacks Limited diversity while recommending
Maybe always suggest from few programs
Suggestions based on immature profiles to new users
Content-based methods
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Collaborative approaches
More diverse recommendations Based on users with similar preferences
Search correlations among the ratings from users Resource-demanding content descriptio
ns aren’t necessary Movielens, Moviefinder
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Collaborative approaches
Drawbacks A significant latency observed
Requires that users have watched and rated a specific content for it
A meaningful number of users is required Sparsity problem
#programs increasing, 2 users hardly watch the same program
Hampers the discovery of like-minded users
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More…
Hybrid approaches PTV & PTVPlus
Semantic inference AVATAR
Improve recommending quality due to semantic inference
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outline
Introduction Related Work The AVATAR Recommend
System Example Conclusion
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AVATAR System
Advanced Telematic search of Audiovisual contents by semantic Reasoning
AVATAR designing byelaws: Broadcast through a TV service Adopt normalized formats & tech’s
MHP, TV-Anytime Allows adding new personalization tech
’s & adopting future standards
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AVATAR System
OWL language TV-Anytime
Normalize a common data format to describe TV contents
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AVATAR System – an excerpt
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AVATAR System – user profile
User’s profile => hierarchical structure programs the user likes along with
their attributes identified by instances, classes and
properties formalized in the OWL ontology
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AVATAR System - DOI
Assign an index to each class/instance DOI (Degree of Interest)
DOI is computed depends on: Accepted or rejected by user Percentage of the program watched How long to decide to watch this
program
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AVATAR System - flow
user
program
Content-basedstrategy
Collaborativestratesy
FinalRecommendation
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Content-based Strategy
Hierarchical Semantic Similarity Fine the common ancestor If the nearest ancestor is the root, their
similarity is null Inferential Semantic Similarity
Discovering implicit relations between 2 The greater the number of common
instances, the higher the inferential similarity value
- Semantic similarity
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Collaborative Strategy
Goal – find “neighbors” having same preferences
Define rating vectors DOI indexes for classes of TV contents Alleviates sparsity problem
Compute Pearson-r between users neighborhood constructed
AVATAR checks if the target content is appealing for the neighbors
Predicted value is greater when: Target is appealing for the neighbors The neighbors’ preferences are strongly correl
ated
- Semantic prediction
線性相關係數
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Final Recommendation
“two-chance ” mechanism
Targetcontent
User Profile
SemanticValue > βMatch
y
n
suggest
Semanticprediction > βMatch
y
suggest
n
discard
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AVATAR System - Architecture
Feedback Agent:Modify DOI indexes in user’s profile according to user’s response while watching
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outline
Introduction Related Work The AVATAR Recommend System Example Conclusion
![Page 24: AVATAR An Improved Solution for Personalized TV based on Semantic Inference Yolanda Blanco Fern á ndez, Jos é J. Pazos Arias, Mart í n L ó pez Nores, Alberto](https://reader036.vdocuments.site/reader036/viewer/2022062421/56649d365503460f94a0e77f/html5/thumbnails/24.jpg)
Example Target content: Dancing with the Stars Target user: U Neighbors:N1 => N3
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OWL ontology (subset)
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AVATAR System – an excerpt
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AVATAR System - Recommend
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outline
Introduction Related Work The AVATAR Recommend System Example Conclusion
![Page 29: AVATAR An Improved Solution for Personalized TV based on Semantic Inference Yolanda Blanco Fern á ndez, Jos é J. Pazos Arias, Mart í n L ó pez Nores, Alberto](https://reader036.vdocuments.site/reader036/viewer/2022062421/56649d365503460f94a0e77f/html5/thumbnails/29.jpg)
Conclusion Presented a hybrid recommendation strate
gy for a TV intelligent assistant Reduces the sparsity problem of the collab
orative filtering approaches alleviates the lack of diversity associated to
content-based methods Semantic similarity
Future work Continue the experimental evaluation Compare with more traditional approaches