ClusCite:Effective Citation Recommendation by Information Network-Based Clustering Date: 2014/10/16 Author: Xiang Ren, Jialu Liu,Xiao Yu, Urvashi Khandelwal, Quanquan Gu, Lidan Wang, Jiawei Han Source: KDD’14 Advisor: Jia-ling, Koh Speaker: Sheng-Chih, Chu

Introduction Model Overview Model Learning Experiment Conclusion Outline 2

Introduction 3 Based on content + KDD? +citation behavior? +Social network? +Dr.Koh? A small set of paper Google Scholar

Introduction 4 What’s heterogeneous network? What’s Meta-path?

Introduction Citation Recommendation 5 Heterogenous network Learning model

Introduction Model Overview Model Learning Experiment Conclusion Outline 6

Model overview 7 Define Score function s(q,p) : q = query manuscript, p = target paper : how likely query is to belong to the k group. : the relatedness between q, p according to k-th group. : the relative importance of p within the k-th group.

Feature Weight : weight change from importance : different meta path-based feature 8

Paper relevance Ex : k=1~4, 9 q p Interest Group 1 Interest Group 2 Interest Group 3 Interest Group

Relative Authority Affect Relative Authority Score: 1.Published in highly reputed venues. 2.Written by Authority authors. 3.Related to high quality paper. G() function is propagation function. 10

Paper-Speif c 11 q A V T Interest Group 1 Interest Group 2 Interest Group 3 Interest Group query’s group menbership

Introduction Model Overview Model Learning Experiment Conclusion Outline 12

Loss function Actual value – prediction = loss 13

Graph regularization Normalization on authority vector 14 degree of P i in R A i-th column vector of F p j-th column vector of F A degree of A j in R A R K*n R K*|A| R n*n R |A|*|A| R |V|*|V| R K*|V|

Joint optimization problem Joint optimization problem : 15 Tikhonov rehularizes

ClusCite Algorithm 16

Introduction Model Overview Model Learning Experiment Conclusion Outline 17

Experiment Data Set :DBLP dataset2 and PubMed dataset3 18

Process work 19

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Conclusion Propose novel cluster-based framework to satisfy a user’s diverse citation intents. Develop efficient algorithm and better performance this paper is good. 23