INHA UNIVERSITY INCHON, KOREA http://eslab.inha.ac.kr PocketLens : Toward a Perso PocketLens : Toward a Perso nal Recommender System nal Recommender System B.N. Miller, J.A. Konstan, J. Riedl, B.N. Miller, J.A. Konstan, J. Riedl, ACM Transact ACM Transact ions on Information Systems, Vol. 22, No. 3, July ions on Information Systems, Vol. 22, No. 3, July 2004 2004 석석 석석 2 2 석 석 석 석 석 석 석 석
PocketLens : Toward a Personal Recommender System B.N. Miller, J.A. Konstan, J. Riedl, ACM Transactions on Information Systems, Vol. 22, No. 3, July 2004. 석사 2 차 지 애 띠. OUTLINE. INTRODUCTION RELATED WORKS POCKETLENS POCKETLENS ARCHTECTURES SIMULATION RESULTS DISCUSSIONS. INTRODUCTION. - PowerPoint PPT Presentation
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INHA UNIVERSITY
INCHON, KOREA
http://eslab.inha.ac.kr
PocketLens : Toward a Personal RecoPocketLens : Toward a Personal Recommender Systemmmender System
B.N. Miller, J.A. Konstan, J. Riedl, B.N. Miller, J.A. Konstan, J. Riedl, ACM Transactions on InfACM Transactions on Information Systems, Vol. 22, No. 3, July 2004ormation Systems, Vol. 22, No. 3, July 2004
석사 석사 22 차 차 지 애 띠지 애 띠
INHA UNIVERSITY
INCHON, KOREA
http://eslab.inha.ac.kr
OUTLINE OUTLINE
INTRODUCTION
RELATED WORKS
POCKETLENS
POCKETLENS ARCHTECTURES
SIMULATION RESULTS
DISCUSSIONS
INHA UNIVERSITY
INCHON, KOREA
http://eslab.inha.ac.kr
INTRODUCTIONINTRODUCTION
Two key problems in recommenders
• Portability – portable recommendations are available on small device such as palmtop computers
• Trust – reliability and security
INHA UNIVERSITY
INCHON, KOREA
http://eslab.inha.ac.kr
INTRODUCTION – Research INTRODUCTION – Research GoalsGoals Portability.
• Users should be able to receive recommendations wherever they are, on whatever client they are using, even when client is disconnected from Internet.
Palmtop. • Users should be able to run the recommender on
palmtop size machines.
User Control. • Users should control their profile and ratings
information.
Accuracy. • To provide recommendations that are as good as those
To introduce the PocketLens, a peer-to-peer collaborative filtering algorithm. • To run on client devices as small as palmtop computers and
to enable users • To choose to only share some of their ratings with other user
s.
To provide a comparison of five architectures for distributing ratings among recommender clients.
To evaluate the architectures.• To meet the accuracy and performance goals• The tradeoffs among security, implementation complexity and
performance
INHA UNIVERSITY
INCHON, KOREA
http://eslab.inha.ac.kr
RELATED WORKRELATED WORK
User-item collaborative filtering Model based collaborative filtering Peer-to-peer
• Large scale computation – SETI@home• One-to-one communication – AIM, ICQ and other messengers• File sharing – Gnutella, OceanStore and Freenet• Content distribution – Publius, Tangler, Free Heaven, Interme
mory and Mojonation• Collaborative filtering
Intelligent Agents - Yenta
INHA UNIVERSITY
INCHON, KOREA
http://eslab.inha.ac.kr
POCKETLENS - benefitsPOCKETLENS - benefits
Portability • creating a model while the user is online, that can be
used to make recommendations while the user is offline.
Palmtop compatibility • the computation of the model is distributed across
many processors, and the resulting model is small. User control
• allowing the user to choose which part of her profile she wants to share with other members of the peer-to-peer community.
• The PocketLens algorithm is portable enough to run on disconnected palmtop computers, and can protect the user’s privacy and provide trust recommendations.
• The quality of recommendations is as good as the best previously reported results [Sarwar et al. 2001].
• Among five architectures, no one is perfect but the architectures provides fast, portable recommendations with privacy protection, and good quality.
Future Works• To turn these architectures into working systems.• To investigate interface issues for helping users message and contr
ol their profile information.• To prevent shilling attack problems.