1. RESEARCH POSTER PRESENTATION DESIGN © 2012
www.PosterPresentations.com
Recommendation system (RS) plays an
important role in many areas of today's digital
world. A recommender system for ecommerce
websites not only helps sellers to increase their
sells but it also helps buyers to purchase most
accurate item. In last one decade; several attacks
came into existence that can affect the accuracy
of the system. We have used a statistical based
approach to detect the attacks. We compare the
accuracy of six models and measure the accuracy
of models. Then by ensembling the top three
performing models using voting technique we
build our ensemble model.
ABSTRACT
Our implementation consists three steps. In first step, we measure the effectiveness of the attack in
terms of the prediction shift in the average rating of a particular item. In second step, we find the
attributes to distinguish the authentic and attack profiles. Two types of attributes generic
(RDMA,WDMA, Degree Similarity, Length Variance) [4] and model specific(Mean Variance, FMTD)
are used. In last step, we compare the accuracy of statistical models in distinguishing the profiles. It is
performed in two different ways. In first approach, we compare six machine learning models i.e.
decision tree, random forest, ada boost, SVM, linear regression and neural networks. We use precision
and recall to measure their accuracy. We find top three performing models and ensemble them using the
voting technique to build our ensemble model. In second approach, we give a comparative study of
four clustering models i.e. EM, farthest first, hierarchical and simple K mean.
IMPLEMENTATION & RESULTS
We use MovieLens 100K dataset. This
dataset contains 943 users who has given
100,000 ratings to 1682 items. All the
ratings are in the range between 1 to 5.
To measure the robustness of the system
we use K-fold cross validation.
CONCLUSION & FUTURE SCOPE
In this work, we examine six machine learning
classification models and measure their
performance for the detection of attack profiles
in recommender system. Based on their
performance we find out top three performing
models are neural network, SVM and random
forest. We combine them to make our ensemble
model. We also give a comparative study of four
unsupervised models. The accuracy of proposed
ensemble model is more than 90% in most of the
cases. It is expected that optimizing of model
parameters may leads to better results. This
approach can be used in other areas also like
spam filtering, intrusion detections etc. Similar
approach can be used in the detection of DOS
attacks or in the identification of intrusions in the
system.
REFERENCES
1. Davoodi, Fatemeh Ghiyafeh, and Omid
Fatemi. "Tag based recommender system for
social bookmarking sites." In Proceedings of
the 2012 International Conference on
Advances in Social Networks Analysis and
Mining (ASONAM 2012), pp. 934-940. IEEE
Computer Society, 2012.
2. Bobadilla, Jesús, Fernando Ortega, Antonio
Hernando, and Abraham Gutiérrez.
"Recommender systems survey." Knowledge-
Based Systems 46 (2013): 109-132.
3. Burke, Robin, Bamshad Mobasher, Runa
Bhaumik, and Chad Williams. "Segment-
based injection attacks against collaborative
filtering recommender systems." In Data
Mining, Fifth IEEE International Conference
on, pp. 4-pp. IEEE, 2005.
4. Chirita, Paul-Alexandru, Wolfgang Nejdl, and
Cristian Zamfir. "Preventing shilling attacks
in online.
Ashish Kumar
Master of Engineering (Software Engineering), CSED, Thapar University, Patiala
Profile Injection Attack Detection in Recommender System
INTRODUCTION
Recommender system predicts the preference
that a user would give to an item. Content based
and collaborative filtering based are two widely
used types of recommender systems. Content
based filtering approach uses the properties of an
item to recommend additional items with similar
properties [1]. Collaborative filtering works by
building a database of preferences for various
items in the system by users [2]. It works on the
principle that two users who had similar tastes in
the past will also have similar taste in future
also. As collaborative filtering based system is
open to the users input, so they have high
chances of profile injection attacks. The aim of
the attacker is to interact with the recommender
system to either push or nuke a particular item
[3]. Attack profile structure:
Types of attacks:
1. Push Attacks: Average, Random,
Bandwagon and Segment Attack.
2. Nuke Attacks: Love-Hate and Reverse-
bandwagon Attack.
Filler Size 1% 10% 20% 30% 40% 50%
Models P R P R P R P R P R P R
Decision Tree .90 .892 .921 .93 .928 .919 .939 .912 .94 .94 .961 .968
Random Forest .929 .930 .939 .92 .948 .948 .952 .956 .962 .961 .973 .979
Ada Boost .9 .908 .914 .918 .93 .924 .935 .938 .948 .934 .950 .943
SVM .938 .927 .949 .94 .959 .959 .971 .975 .979 .981 .988 .989
Linear Regression .89 .862 .89 .907 .918 .91 .925 .918 .929 .902 .93 .931
Neural Network .949 .938 .95 .943 .951 .954 .959 .958 .968 .967 .979 .98
Ensemble .939 .932 .946 .934 .953 .953 .961 .963 .968 .969 .98 .982