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CRESCENT TCU Dept. of Computer Science
Smart Home Application
Intelligent TV ViewingVince Guerin
CRESCENT TCU Dept. of Computer Science
Glorified House Controller
• NSF funded research project on “Smart Home” technologies
• UTA / TCU Smart Home Project:- “Glorified House Controller (GHC), a remote control system, will be able to operate any electronic device in a home. It will also be able to change the status of different appliances, save settings of all devices for a quick change, and have the ability to learn television viewing habits.”
CRESCENT TCU Dept. of Computer Science
Smart TV Recommender Goal
• “Intelligent” program(s) will predict, according to a person’s likes and dislikes, whether it should record a television program or not.
• This will be similar to what “Amazon.com” does for books.
CRESCENT TCU Dept. of Computer Science
TV Recommender
Product must be:• Accurate• Easy to use• Able to build trust in the
recommendations delivered
CRESCENT TCU Dept. of Computer Science
Agenda
CRESCENT TCU Dept. of Computer Science
Data Needs
• Learning Algorithms
http://tvlistings2.zap2it.com
- Online TV Guide - Current online guides lack info needed for some learning methods (keywords, etc…)
CRESCENT TCU Dept. of Computer Science
TV Recommender – Two Ways to Learn
• Program reads various “keywords” inputted by the user (such as ‘comedy,’ ‘horses,’ ‘horror,’ etc..). Program then picks out television shows that contain those words in the description
• Program monitors how often the user watches certain types of shows; decides based on past viewings.
CRESCENT TCU Dept. of Computer Science
AI Project – Keyword Matching
CRESCENT TCU Dept. of Computer Science
Other types of Keywords
CRESCENT TCU Dept. of Computer Science
Scenario 1
1 – User watches at least 2 hours of TV per night.
2 – Program monitors viewing and gathers keywords and names of programs most watched.
3 – After 3 weeks of viewing, user takes vacation and turns on program to record shows most watched.
4 – User returns from vacation and views recorded shows.
CRESCENT TCU Dept. of Computer Science
Scenario 2
1 – User watches at least 2 hours of TV per night.
2 – Program monitors viewing and gathers keywords and names of programs most watched.
3 – After 3 weeks of viewing, user takes vacation and turns on program to record shows most watched, as well as programs he/she might enjoy.
4 – User returns from vacation and views recorded shows.
CRESCENT TCU Dept. of Computer Science
Scenario 3
• User manually inputs keywords, channels, and television programs to guide the system as to which programs to record.
• User lets system run all day.• According to specifications, system
records appropriate programs.• User returns and watches pre-selected
viewing material.
CRESCENT TCU Dept. of Computer Science
Java Expert System Shell (JESS)
• What is JESS?- Java rule based expert system from Sandia National Laboratories (http://herzberg.ca.sandia.gov/jess)- Stores rules and facts- Ability to reason given rules, and assert actions based on facts- Similar to a relational database
CRESCENT TCU Dept. of Computer Science
JESS Cont…
• Why is JESS important to Smart Home Technologies?– Continuously changing data – Unambiguous language to represent
rules – References and method invocations of
Java object – Seamless interaction between rule
evaluation and framework
CRESCENT TCU Dept. of Computer Science
JESS in Action – KM Project
• 2 Phases – Rank & Record• Rank
– rules with decreasing salience fire, with each rule looking for something different each time
– The highest salience rules fire first, and they assign the highest rankings based on the criteria for which they check
– When none of those rules can fire any more, then the phase change rule fires and changes the phase from ranking shows to recording shows
CRESCENT TCU Dept. of Computer Science
JESS in Action cont…
• Record Phase– Iterates through the rankings in the
same fashion (using decreasing salience)
– It will keep recording shows with decreasing rank, so long as there isn't a time conflict, and there is enough tape left.
CRESCENT TCU Dept. of Computer Science
Phillips USA solution
Kaushal Kurapati’s ideas for capturing preferences:
• Using “stereotypes” from which the user can choose (clusters of TV shows that are similar to one another)
• Create a user “Profile” according to the stereotypes
CRESCENT TCU Dept. of Computer Science
Phillips USA solution cont…
• Calculating the “distance” between networks/shows
Example:
Calculating the “distance” between FOX and NBC
CRESCENT TCU Dept. of Computer Science
Cont…
Computing Distances:
CRESCENT TCU Dept. of Computer Science
Cont…
Deriving Stereotypes from Clustering Algorithm:
CRESCENT TCU Dept. of Computer Science
Phillips Solution conclusion
• Tested in Manor, New York area on 10 users
• Users contributed TV viewing histories for periods ranging from 5 months to 2 years
• Average initial “error rate” was around 40% (best was 30%, worst was 62.6%)• Need to improve “out-of-box” error rates• Future work – deeper pool of user data
CRESCENT TCU Dept. of Computer Science
Summary
• 2 solutions presented somewhat solve the problem, but for the final product, we need more.
• Java, perhaps, as the language of choice• Implementation of keywords for online
TV guides• Overall, these ideas are a good start on
working toward a useful, functional product
CRESCENT TCU Dept. of Computer Science
References
• http://www.cs.umbc.edu/~skaush1/IASTED_2002.pdf
TV-Learning paper #1• http://www.csee.umbc.edu/~skaush1/TV02_Ea
se_of_Use_Trust_Accuracy.pdf TV-Learning paper #2• http://tvlistings2.zap2it.com/
television guide site
• http://www.captions.org/ closed captioning information site
CRESCENT TCU Dept. of Computer Science
References Cont…
• http://red.cs.tcu.edu/crescent.html#_Work_InformationCrescent Home