change icon

README.md

@@ -4,7 +4,7 @@
 [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)
 [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
 
-### Contents
+### 👉 Table of Contents 👈
 + [Attack](#1)
 	+ [2020](#1-1)
 	+ [2019](#1-2)
@@ -34,9 +34,9 @@
 + **Attacking Black-box Recommendations via Copying Cross-domain User Profiles**, *Arxiv*, [[📝Paper]](https://arxiv.org/abs/2005.08147)
 + **Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems**, *Arxiv*, [[📝Paper]](https://arxiv.org/abs/2006.07934)
 + **Adversarial Attacks on Linear Contextual Bandits**, *Arxiv*, [[📝Paper]](https://arxiv.org/pdf/2002.03839)
-+ **Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start**, *Arxiv*, [[📝Paper]](https://arxiv.org/abs/2006.01888), [[🔥Code]](https://github.com/liuzrcc/AIP)
++ **Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start**, *Arxiv*, [[📝Paper]](https://arxiv.org/abs/2006.01888), [[:octocat:Code]](https://github.com/liuzrcc/AIP)
 + **Influence Function based Data Poisoning Attacks to Top-N Recommender Systems**, *WWW*, [[📝Paper]](https://arxiv.org/abs/2002.08025)
-+ **TAaMR: Targeted Adversarial Attack against Multimedia Recommender Systems**, *Dependable and Secure Machine Learning (DSML)*, [[📝Paper]](http://sisinflab.poliba.it/publications/2020/DMM20/PID6442119.pdf), [[🔥Code]](https://github.com/sisinflab/TAaMR)
++ **TAaMR: Targeted Adversarial Attack against Multimedia Recommender Systems**, *Dependable and Secure Machine Learning (DSML)*, [[📝Paper]](http://sisinflab.poliba.it/publications/2020/DMM20/PID6442119.pdf), [[:octocat:Code]](https://github.com/sisinflab/TAaMR)
 
 
 
@@ -51,7 +51,7 @@
 <a class="toc" id ="1-3"></a>
 
 ## 2018
-+ **Poisoning attacks to graph-based recommender systems**, *Annual Computer Security Applications Conference (ACSAC)*, [[📝Paper]](https://arxiv.org/abs/1809.04127), [[🔥Code]](https://github.com/alanefl/graph-based-recommender-attacks)
++ **Poisoning attacks to graph-based recommender systems**, *Annual Computer Security Applications Conference (ACSAC)*, [[📝Paper]](https://arxiv.org/abs/1809.04127), [[:octocat:Code]](https://github.com/alanefl/graph-based-recommender-attacks)
 
 
 <a class="toc" id ="1-4"></a>
@@ -62,7 +62,7 @@
 <a class="toc" id ="1-5"></a>
 
 ## 2016
-+ **Data Poisoning Attacks on Factorization-Based Collaborative Filtering**, *NIPS*, [[📝Paper]](https://arxiv.org/abs/1608.08182), [[🔥Code]](https://github.com/fuying-wang/Data-poisoning-attacks-on-factorization-based-collaborative-filtering)
++ **Data Poisoning Attacks on Factorization-Based Collaborative Filtering**, *NIPS*, [[📝Paper]](https://arxiv.org/abs/1608.08182), [[:octocat:Code]](https://github.com/fuying-wang/Data-poisoning-attacks-on-factorization-based-collaborative-filtering)
 + **Segment-Focused Shilling Attacks against Recommendation Algorithms in Binary Ratings-based Recommender Systems**, *International Journal of Hybrid Information Technology*, [[📝Paper]](https://www.semanticscholar.org/paper/Segment-Focused-Shilling-Attacks-against-Algorithms-Zhang/5c7e96dcaf253f37904f91fdb6fdd6f486dba134)
 + **Shilling attack detection in collaborative filtering recommender system by PCA detection and perturbation**, *International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)*, [[📝Paper]](https://ieeexplore.ieee.org/document/7731644)
 + **Shilling attack models in recommender system**, *International Conference on Inventive Computation Technologies (ICICT)*, [[📝Paper]](https://ieeexplore.ieee.org/document/7824865)
@@ -88,10 +88,10 @@
 <a class="toc" id ="2-2"></a>
 
 ## 2019
-+ **Adversarial Training Towards Robust Multimedia Recommender System**, *TKDE*, [[📝Paper]](https://graphreason.github.io/papers/35.pdf), [[🔥Code]](https://github.com/duxy-me/AMR)
++ **Adversarial Training Towards Robust Multimedia Recommender System**, *TKDE*, [[📝Paper]](https://graphreason.github.io/papers/35.pdf), [[:octocat:Code]](https://github.com/duxy-me/AMR)
 + **Adversarial Collaborative Neural Network for Robust Recommendation**, *SIGIR*, [[📝Paper]](https://www.researchgate.net/publication/332861957_Adversarial_Collaborative_Neural_Network_for_Robust_Recommendation)
-+ **Adversarial Mahalanobis Distance-based Attentive Song Recommender for Automatic Playlist Continuation**, *SIGIR*, [[📝Paper]](http://web.cs.wpi.edu/~kmlee/pubs/tran19sigir.pdf), [[🔥Code]](https://github.com/thanhdtran/MASR)
-+ **Adversarial tensor factorization for context-aware recommendation**, *RecSys*, [[📝Paper]](https://dl.acm.org/doi/10.1145/3298689.3346987), [[🔥Code]]
++ **Adversarial Mahalanobis Distance-based Attentive Song Recommender for Automatic Playlist Continuation**, *SIGIR*, [[📝Paper]](http://web.cs.wpi.edu/~kmlee/pubs/tran19sigir.pdf), [[:octocat:Code]](https://github.com/thanhdtran/MASR)
++ **Adversarial tensor factorization for context-aware recommendation**, *RecSys*, [[📝Paper]](https://dl.acm.org/doi/10.1145/3298689.3346987), [[:octocat:Code]]
 + **Adversarial Training-Based Mean Bayesian Personalized Ranking for Recommender System**, *IEEE Access*, [[📝Paper]](https://ieeexplore.ieee.org/document/8946325)
 
 
@@ -99,7 +99,7 @@
 <a class="toc" id ="2-3"></a>
 
 ## 2018
-+ **Adversarial Personalized Ranking for Recommendation**, *SIGIR*, [[📝Paper]](https://dl.acm.org/citation.cfm?id=3209981),  [[🔥Code]](https://github.com/hexiangnan/adversarial_personalized_ranking)
++ **Adversarial Personalized Ranking for Recommendation**, *SIGIR*, [[📝Paper]](https://dl.acm.org/citation.cfm?id=3209981),  [[:octocat:Code]](https://github.com/hexiangnan/adversarial_personalized_ranking)
 + **A shilling attack detector based on convolutional neural network for collaborative recommender system in social aware network**, *The Computer Journal*, [[📝Paper]](https://academic.oup.com/comjnl/article-abstract/61/7/949/4835634)
 + **Adversarial Sampling and Training for Semi-Supervised Information Retrieval**, *WWW*, [[📝Paper]](https://arxiv.org/abs/1506.05752)
 + **Enhancing the Robustness of Neural Collaborative Filtering Systems Under Malicious Attacks**, *IEEE Transactions on Multimedia*, [[📝Paper]](https://ieeexplore.ieee.org/document/8576563)