cse 6362.003 intelligent environments paper presentation darin brezeale april 16, 2003
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Surfing the Digital WaveGeneralizing Personalized TV Listings using Collaborative,
Case-Based Recommendation
Barry Smyth, Paul CotterDept. of Computer ScienceUniversity College Dublin
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Paper Source Published: In proceedings of the third International
Conference on Case-based Reasoning. Munich, Germany, 1999.
URL: http://www.cs.ucd.ie/staff/bsmyth/home/crc/iccbr99a.ps
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Introduction Cable and satellite services make
it possible to have hundreds or thousands of television channels available
TV Guide is over 400 pages Channel surfing 200 channels at
10 seconds each will take nearly 35 minutes
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Introduction cont. Problem: It is difficult for viewers
to locate television programs they may be interested in.
Solution: Create a system that will identify and recommend programs of interest to the viewers.
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PTV System Paper describes the PTV system
(Personalized Television Listings) Online system http://www.ptv.ie/
(listed in paper as http://ptv.ucd.ie) Registered users can view
personalized TV listings
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Profile Database and Profiler Program Case-Base Schedule Database Recommender Guide Compiler
PTV Architecture
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Profile Database and Profiler Stores profiles of each user, including:
TV programs liked and disliked Preferred viewing times Subject preferences
Preliminary profiles constructed at registration
Helps to initiate the personalization process Most profile information learned from user
grading of recommendations
PTV Architecture cont.
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Program Case-Base Database of TV program content
descriptions, including: Title Genre Cast
PTV Architecture cont.
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Schedule Database Contains TV listings for all supported
channels Constructed from online sources
Recommender The brain of the PTV system Takes user profile information and
selects new TV programs to recommend
PTV Architecture cont.
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Guide Compiler Personalized listings are constructed
dynamically by matching: List of recommended TV programs and
the user’s likes TV programs to be aired on the specified
date
PTV Architecture cont.
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Hybrid Information Filter PTV makes recommendations by
combining two differrent approaches Case-based Collaborative Filtering
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Case-based Approach Matches features in the user’s
profile to TV programs
),(p) ema(u),PrgSim(Sch 1. )( pi
uSchemaii ffsimw
Schema(u) = feature-based representation of u’s profilep = program casewi = weight of program feature ifi = program feature i
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Case-based Approach cont. Pros
Based strictly on the user’s profile Cons
Knowledge-engineering effort to develop case representations and similarity models
Recommendations will be very similar to previously viewed TV programs
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Collaborative Filtering Approach
Recommendations are based on what similar users like
k similar user profiles are selected using function PrfSim
r programs are selected for recommendation using function PrgRank
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Collaborative Filtering Approach cont.
r(piu) = rank of program pi in profile u
p(u) = ranked programs in user u’s profile
)'()(4
)()(
)u' PrfSim(u, 2. )'()(
'
upup
prprupup
ui
ui
)',(PrfSimu)PrgRank(p, 3.'
uuUu
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Collaborative Filtering Approach cont.
Pros No need for rich content representation Increased recommendation diversity
Cons Cost to gather enough profile
information to make accurate similarity measures
Latency of new shows spreading
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Experimental Studies Setup
About 200 users Mainly students and staff from University College
Dublin and Trinity College Dublin Case-base consisted of about 400 TV
programs 2000 individual program guides were
requested Each guide contained an average of 3
recommendations
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Experimental Studies cont. Method
Recommendations in each guide were either:
generated by the case-based approach generated by the collaborative filtering approach generated by picking programs at random
Users graded recommendations with values of {-2, -1, 0, 1, 2}
About 1000 individual gradings from 100 users
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Experimental Studies cont. Results
Performance measured by counting percentage of users receiving ‘n’ or more good recommendations per day
Results shown in figure
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