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

outline

Introduction Related Work The AVATAR Recommend System Example Conclusion

outline

Introduction Related Work The AVATAR Recommend System Example Conclusion

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

outline

Introduction Related Work The AVATAR Recommend System Example Conclusion

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

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

Drawbacks Limited diversity while recommending

Maybe always suggest from few programs

Suggestions based on immature profiles to new users

Content-based methods

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

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

More…

Hybrid approaches PTV & PTVPlus

Semantic inference AVATAR

Improve recommending quality due to semantic inference

outline

Introduction Related Work The AVATAR Recommend

System Example Conclusion

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

AVATAR System

OWL language TV-Anytime

Normalize a common data format to describe TV contents

AVATAR System – an excerpt

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

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

AVATAR System - flow

user

program

Content-basedstrategy

Collaborativestratesy

FinalRecommendation

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

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

Final Recommendation

“two-chance ” mechanism

Targetcontent

User Profile

SemanticValue > βMatch

y

n

suggest

Semanticprediction > βMatch

y

suggest

n

discard

AVATAR System - Architecture

Feedback Agent:Modify DOI indexes in user’s profile according to user’s response while watching

outline

Introduction Related Work The AVATAR Recommend System Example Conclusion

Example Target content: Dancing with the Stars Target user: U Neighbors:N1 => N3

OWL ontology (subset)

AVATAR System – an excerpt

AVATAR System - Recommend

outline

Introduction Related Work The AVATAR Recommend System Example Conclusion

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