Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Providing Justifications in Recommender Systems Presenter : Keng-Yu Lin Author : Panagiotis Symeonidis, Alexandros Nanopoulos, and Yannis Manolopoulos IEEE. 2008

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outlines  Motivation  Objectives  Methodology  Experiments  Conclusions  Comments

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation  Existing recommended systems miss the interaction between the user and his favorite features that can be used for justifying a recommendation.  Existing recommended systems cannot detect partial matching of the user’s preferences.  Existing recommended systems lack metrics to evaluate the quality of justifications. 3

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objectives  This paper propose a novel approach to solve aforementioned problem.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  This paper propose to capture the interaction between users and their favorite features by constructing a feature profile. 5

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Creating groups of users 6 xMotif algorithm

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Weighted user-feature matrix 7

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Neighborhood formation 8 iPhoneAndroid Mango U={u1,u2}, I={HTC,Transformer}

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Generating the recommendation and justification lists 9 fr(f1)=2 fr(f3)=1 W(I1)=1 W(I7)=2+1=3 sum Item I7 is recommended, because it contains feature f1, which is included in item I1 you have rated.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Coverage 10 Coverage=(( ) / ( ))*100%=60%

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Precision and explain coverage of FWNB versus a for (a) MovieLens and (b) Reuters data sets. 11

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Comparison between SF, CFCB, and FWNB in terms of explain coverage versus N for (a) MovieLens and (b) Reuters data sets. 12

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Comparison between IB, CFCB, and FWNB in terms of precision versus recall for (a) MovieLens and (b) Reuters data sets. 13

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Result of user survey 14

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusions  This paper propose an approach to attain both accurate and justifiable recommendations. 15

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comments  Advantage  Applications  Recommended systems 16