Cse 6362 003 intelligent environments paper presentation darin brezeale april 16 2003
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CSE 6362.003 Intelligent Environments Paper Presentation Darin Brezeale April 16, 2003. Surfing the Digital Wave. Generalizing Personalized TV Listings using Collaborative, Case-Based Recommendation Barry Smyth, Paul Cotter Dept. of Computer Science University College Dublin.

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CSE 6362.003 Intelligent Environments Paper Presentation Darin Brezeale April 16, 2003

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Cse 6362 003 intelligent environments paper presentation darin brezeale april 16 2003

CSE 6362.003

Intelligent Environments

Paper Presentation

Darin Brezeale

April 16, 2003


Surfing the digital wave

Surfing the Digital Wave

Generalizing Personalized TV Listings using Collaborative, Case-Based Recommendation

Barry Smyth, Paul Cotter

Dept. of Computer Science

University College Dublin


Paper source

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


Introduction

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


Introduction cont

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.


Ptv system

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


Ptv architecture

Profile Database and Profiler

Program Case-Base

Schedule Database

Recommender

Guide Compiler

PTV Architecture


Ptv architecture cont

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.


Ptv architecture cont1

Program Case-Base

Database of TV program content descriptions, including:

Title

Genre

Cast

PTV Architecture cont.


Ptv architecture cont2

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.


Ptv architecture cont3

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.


Ptv architecture cont4

PTV Architecture cont.


Hybrid information filter

Hybrid Information Filter

  • PTV makes recommendations by combining two differrent approaches

    • Case-based

    • Collaborative Filtering


Case based approach

Case-based Approach

  • Matches features in the user’s profile to TV programs

Schema(u) = feature-based representation of u’s profile

p = program case

wi = weight of program feature i

fi = program feature i


Case based approach cont

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


Collaborative filtering approach

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


Collaborative filtering approach cont

Collaborative Filtering Approach cont.

r(piu) = rank of program pi in profile u

p(u) = ranked programs in user u’s profile


Collaborative filtering approach cont1

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


Experimental studies

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


Experimental studies cont

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


Experimental studies cont1

Experimental Studies cont.

  • Results

    • Performance measured by counting percentage of users receiving ‘n’ or more good recommendations per day

    • Results shown in figure


Experimental studies cont2

Experimental Studies cont.


Conclusions

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


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