Learning-based Attack and Defense on Recommender Systems

dc.contributor.advisorZou, Xukai
dc.contributor.authorPalanisamy Sundar, Agnideven
dc.contributor.otherLi, Feng
dc.contributor.otherHu, Qin
dc.date.accessioned2021-08-10T13:29:30Z
dc.date.available2021-08-10T13:29:30Z
dc.date.issued2021-08
dc.degree.date2021en_US
dc.degree.grantorPurdue Universityen_US
dc.degree.levelM.S.en_US
dc.descriptionIndiana University-Purdue University Indianapolis (IUPUI)en_US
dc.description.abstractThe internet is the home for massive volumes of valuable data constantly being created, making it difficult for users to find information relevant to them. In recent times, online users have been relying on the recommendations made by websites to narrow down the options. Online reviews have also become an increasingly important factor in the final choice of a customer. Unfortunately, attackers have found ways to manipulate both reviews and recommendations to mislead users. A Recommendation System is a special type of information filtering system adapted by online vendors to provide suggestions to their customers based on their requirements. Collaborative filtering is one of the most widely used recommendation systems; unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. On the other hand, many spammers write deceptive reviews to change the credibility of a product/service. This work aims to address these issues by treating the review manipulation and shilling attack scenarios independently. For the shilling attacks, we build an efficient Reinforcement Learning-based shilling attack method. This method reduces the uncertainty associated with the item selection process and finds the most optimal items to enhance attack reach while treating the recommender system as a black box. Such practical online attacks open new avenues for research in building more robust recommender systems. When it comes to review manipulations, we introduce a method to use a deep structure embedding approach that preserves highly nonlinear structural information and the dynamic aspects of user reviews to identify and cluster the spam users. It is worth mentioning that, in the experiment with real datasets, our method captures about 92\% of all spam reviewers using an unsupervised learning approach.en_US
dc.identifier.urihttps://hdl.handle.net/1805/26439
dc.identifier.urihttp://dx.doi.org/10.7912/C2/65
dc.language.isoenen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectRecommender Systemsen_US
dc.subjectMachine Learningen_US
dc.subjectDeep Learningen_US
dc.subjectReinforcement Learningen_US
dc.subjectFraud Detectionen_US
dc.subjectFake Reviewsen_US
dc.subjectShilling Attacksen_US
dc.subjectGraph Embeddingen_US
dc.subjectMulti-Armed Banditsen_US
dc.titleLearning-based Attack and Defense on Recommender Systemsen_US
dc.typeThesisen
thesis.degree.disciplineComputer & Information Scienceen
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Learning-based Attack and Defense on Recommender Systems [Agnideven MS Thesis].pdf
Size:
3.11 MB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.99 KB
Format:
Item-specific license agreed upon to submission
Description: