CSE 6362.003Intelligent Environments

Paper Presentation

Darin BrezealeApril 16, 2003

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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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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Experimental Studies cont.

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Conclusions Case-based and collaboritive filtering

approaches offset each other’s weaknesses

Collaborative filtering approach outperformed case-based approach

Both collaborative filtering and case-based approaches outperformed random recommendations