83 research outputs found
Web3Recommend: Decentralised recommendations with trust and relevance
Web3Recommend is a decentralized Social Recommender System implementation
that enables Web3 Platforms on Android to generate recommendations that balance
trust and relevance. Generating recommendations in decentralized networks is a
non-trivial problem because these networks lack a global perspective due to the
absence of a central authority. Further, decentralized networks are prone to
Sybil Attacks in which a single malicious user can generate multiple fake or
Sybil identities. Web3Recommend relies on a novel graph-based content
recommendation design inspired by GraphJet, a recommendation system used in
Twitter enhanced with MeritRank, a decentralized reputation scheme that
provides Sybil-resistance to the system. By adding MeritRank's decay parameters
to the vanilla Social Recommender Systems' personalized SALSA graph algorithm,
we can provide theoretical guarantees against Sybil Attacks in the generated
recommendations. Similar to GraphJet, we focus on generating real-time
recommendations by only acting on recent interactions in the social network,
allowing us to cater temporally contextual recommendations while keeping a
tight bound on the memory usage in resource-constrained devices, allowing for a
seamless user experience. As a proof-of-concept, we integrate our system with
MusicDAO, an open-source Web3 music-sharing platform, to generate personalized,
real-time recommendations. Thus, we provide the first Sybil-resistant Social
Recommender System, allowing real-time recommendations beyond classic
user-based collaborative filtering. The system is also rigorously tested with
extensive unit and integration tests. Further, our experiments demonstrate the
trust-relevance balance of recommendations against multiple adversarial
strategies in a test network generated using data from real music platforms
Chiron: A Robust Recommendation System with Graph Regularizer
Recommendation systems have been widely used by commercial service providers
for giving suggestions to users. Collaborative filtering (CF) systems, one of
the most popular recommendation systems, utilize the history of behaviors of
the aggregate user-base to provide individual recommendations and are effective
when almost all users faithfully express their opinions. However, they are
vulnerable to malicious users biasing their inputs in order to change the
overall ratings of a specific group of items. CF systems largely fall into two
categories - neighborhood-based and (matrix) factorization-based - and the
presence of adversarial input can influence recommendations in both categories,
leading to instabilities in estimation and prediction. Although the robustness
of different collaborative filtering algorithms has been extensively studied,
designing an efficient system that is immune to manipulation remains a
significant challenge. In this work we propose a novel "hybrid" recommendation
system with an adaptive graph-based user/item similarity-regularization -
"Chiron". Chiron ties the performance benefits of dimensionality reduction
(through factorization) with the advantage of neighborhood clustering (through
regularization). We demonstrate, using extensive comparative experiments, that
Chiron is resistant to manipulation by large and lethal attacks
์ ๋ณด ์์ค์ ์ด์ฉํ ๊ฐ๊ฑดํ ์๋น๊ณต๊ฒฉ ๋ฐฉ์ด ์๊ณ ๋ฆฌ์ฆ ์ค๊ณ ๋ฐ ๋ถ์
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ, 2014. 8. ๊น์ข
๊ถ.์ถ์ฒ ์์คํ
(Recommender System, RS)์ ๊ถ๊ทน์ ์ธ ์๋น์ (์ฆ, ์ถ์ฒ ์์คํ
์ฌ์ฉ์)์๊ฒ ์์
์ ์ธ ์์ดํ
๋ค์ ์ถ์ฒํด ์ฃผ๋ ๊ฒ์ด ์ฃผ์ ๊ธฐ๋ฅ์ด๋ค. ์ถ์ฒ ์์คํ
์์ ์ ํํ ์ ๋ณด๋ฅผ ์ ๊ณตํ๋ ๊ฒ์ ์ถ์ฒ ์๋น์ค ๊ณต๊ธ์์ ์์คํ
์ฌ์ฉ์ ๋ชจ๋์๊ฒ ์ค์ํ๋ค. ์จ๋ผ์ธ ์์
๋คํธ์ํฌ์ ํ์ฐ์ผ๋ก ์ถ์ฒ ์์คํ
์ ์ํฅ๋ ฅ์ ๊ธ๊ฒฉํ ์ฆ๊ฐํ๊ณ ์๋ค. ๋ฐ๋ฉด์ ์ถ์ฒ ์์คํ
์ ์๋์๋ ๋ฐ๋๋ก ์ ๋ณด๋ฅผ ์กฐ์ํ๋ ๊ฑฐ์ง ์์ด๋ดํฐํฐ๋ค์ ์ฌ์ฉํ ์
์์ ์ธ ์ฌ์ฉ์๋ค์ ์ถ์ฒ ์์คํ
์ ๋ํ ๊ณต๊ฒฉ์ด ์ฆ๊ฐํ๊ณ ์๋ค. ์ด๋ฌํ ๊ฑฐ์ง ์์ด๋ดํฐํฐ๋ค์ ํ์ฉํ ๊ณต๊ฒฉ์ ์๋น(Sybil) ๊ณต๊ฒฉ์ด๋ผ ๋ถ๋ฅธ๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ๋ค๋ฅธ ์ฐ๊ตฌ์์ ์๊ฐ๋ ์ ์ด ์๋ ์ด๋๋ฏธ์
ํต์ ๊ฐ๋
์ ํ์ฉํ RobuRec์ด๋ผ ๋ถ๋ฆฌ๋ ์๋ก์ด ๊ฐ๊ฑดํ ์ถ์ฒ ์์คํ
์ ์ ์ํ๋ค. ์ด๋๋ฏธ์
ํต์ ๋ผ๋ ๊ฐ๋ ฅํ ๊ฐ๋
์ ํ์ฉํ์ฌ ์ ์งํ ์ฌ์ฉ์๊ฐ ์์ฑํ ํ๊ฐ์ธ์ง ํน์ ์๋น ์์ด๋ดํฐํฐ๋ค์ ํ์ฉํ ์
์์ ์ธ ํ๊ฐ์ธ์ง์ ๊ด๊ณ์์ด ๊ณ ์ ๋ขฐ ์์ค์ ์ถ์ฒ์ ์์ธกํ ์ ์๋ค. RobuRec ์์คํ
์ ์ฑ๋ฅ์ ๋ณด์ด๊ธฐ ์ํด, ๋ณธ ๋
ผ๋ฌธ์์๋ ์ฌ๋ฌ๊ฐ์ง ๊ฐ๋ฅํ ์๋น ๊ณต๊ฒฉ ์๋๋ฆฌ์ค๋ ๋ฌผ๋ก ๋ค์ํ ๋ฐ์ดํฐ์
์ ํ์ฉํ์ฌ ๊ด๋ฒ์ํ ์คํ์ ์ํํ์๋ค. RobuRec์ ์คํ ๋ฐ ๋ถ์์ ํตํด RobuRec๊ณผ ๋น๊ต ๊ฐ๋ฅํ PCA (Principal Component Analysis) ๋ฐฉ์ ๋ฐ LTSMF (Least Trimmed Squared Matrix Factorization) ๋ฐฉ์๋ณด๋ค ํ๋ฆฌ๋์
์ฌํํธ (Prediction Shift, PS) ๋ฐ ์ ์ค ๋น์จ(Hit Ratio, HR)์์ ์๋ฑํ ์ฑ๋ฅ์ ๋ณด์ฌ ์ฃผ์๋ค.As the major function of Recommender Systems (RSs) is recommending commercial items to potential consumers (i.e., system users), providing correct information
of RS is crucial to both RS providers and system users. The influence of RS over Online Social Networks (OSNs) is expanding rapidly, whereas malicious users continuously
try to attack the RSs with fake identities (i.e., Sybils) by manipulating the information in the RS adversely. In this thesis, we propose a novel robust recommendation
algorithm called RobuRec which exploits a distinctive feature, admission control. RobuRec provides highly Trusted recommendation results since RobuRec predicts appropriate recommendations regardless of whether the ratings are given by honest users or by Sybils thanks to the power of admission control. To demonstrate the performance of RobuRec, we have conducted extensive exper
iments with various datasets as well as diverse attack scenarios. The evaluation results confirm that RobuRec outperforms the comparable schemes such as Principal
Component Analysis (PCA) and Least Trimmed Squared Matrix Factorization (LTSMF) significantly in terms of Prediction Shift (PS) and Hit Ratio (HR).Chapter 1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . 1
1.2 Goal and Contribution . . . . . . . . . . . . . . 3
1.3 Thesis Organization . . . . . . . . . . . . . . . 6
Chapter 2 Related Work 7
2.1 RS approaches . . . . . . . . . . . . . . . . . . 7
2.2 Sybil Attack Defense . . . . . . . . . . . . . . 9
2.3 Robust RS Approaches . . . . . . . . . . . . . . 10
Chapter 3 System Model 13
3.1 Target Applications . . . . . . . . . . . . . . 17
3.2 Strong Attacker . . . . . . . . . . . . . . . . 17
3.3 Attack Model . . . . . . . . . . . . . . . . . . 18
3.4 Model Assumptions . . . . . . . . . . . . . . . 21
Chapter 4 RobuRec Design 23
4.1 Algorithm Intuition . . . . . . . . . . . . . . 23
4.2 Initialization Phase . . . . . . . . . . . . . . 25
4.3 Admission Control Phase . . . . . . . . . . . . 26
4.4 Rating Prediction Phase . . . . . . . . . . . . 30
4.5 Dynamic Parameter Control . . . . . . . . . . . 35
4.5.1 Simplifying Control Parameters . . . . . . . . 36
4.5.2 Dynamic Cmax Control . . . . . . . . . . . . . 37
4.5.3 Dynamic Global and Local Control . . . . . . 42
Chapter 5 Evaluation and Analysis 45
5.1 Evaluation Metrics . . . . . . . . . . . . . . . 45
5.2 Parameter (alpha) Study . . . . . . . . . . . . 47
5.3 Datasets and Setup . . . . . . . . . . . . . . . 48
5.4 Results and Analysis . . . . . . . . . . . . . . 52
5.4.1 Performance on PS . . . . . . . . . . . . . . 52
5.4.2 Impact of Filler Size . . . . . . . . . . . . 55
5.4.3 Impact of Target Selection Strategy . . . . . 58
5.4.4 Dynamic Parameter Control . . . . . . . . . . 59
5.4.5 Performance on HR . . . . . . . . . . . . . . 62
5.4.6 Analysis on Escaping Probability . . . . . . . 63
Chapter 6 Conclusion 67Docto
Incentive-Centered Design for User-Contributed Content
We review incentive-centered design for user-contributed content (UCC) on the Internet. UCC systems, produced (in part) through voluntary contributions made by non-employees, face fundamental incentives problems. In particular, to succeed, users need to be motivated to contribute in the first place ("getting stuff in"). Further, given heterogeneity in content quality and variety, the degree of success will depend on incentives to contribute a desirable mix of quality and variety ("getting \emph{good} stuff in"). Third, because UCC systems generally function as open-access publishing platforms, there is a need to prevent or reduce the amount of negative value (polluting or manipulating) content.
The work to date on incentives problems facing UCC is limited and uneven in coverage. Much of the empirical research concerns specific settings and does not provide readily generalizable results. And, although there are well-developed theoretical literatures on, for example, the private provision of public goods (the "getting stuff in" problem), this literature is only applicable to UCC in a limited way because it focuses on contributions of (homogeneous) money, and thus does not address the many problems associated with heterogeneous information content contributions (the "getting \emph{good} stuff in" problem). We believe that our review of the literature has identified more open questions for research than it has pointed to known results.http://deepblue.lib.umich.edu/bitstream/2027.42/100229/1/icd4ucc.pdf7
Addressing the Issues of Coalitions and Collusion in Multiagent Systems
In the field of multiagent systems, trust and reputation systems are intended to assist agents in finding trustworthy partners with whom to interact. Earlier work of ours identified in theory a number of security vulnerabilities in trust and reputation systems, weaknesses that might be exploited by malicious agents to bypass the protections offered by such systems. In this work, we begin by developing the TREET testbed, a simulation platform that allows for extensive evaluation and flexible experimentation with trust and reputation technologies. We use this testbed to experimentally validate the practicality and gravity of attacks against vulnerabilities. Of particular interest are attacks that are collusive in nature: groups of agents (coalitions) working together to improve their expected rewards. But the issue of coalitions is not unique to trust and reputation; rather, it cuts across a range of fields in multiagent systems and beyond. In some scenarios, coalitions may be unwanted or forbidden; in others they may be benign or even desirable. In this document, we propose a method for detecting coalitions and identifying coalition members, a capability that is likely to be valuable in many of the diverse fields where coalitions may be of interest. Our method makes use of clustering in benefit space (a high-dimensional space reflecting how agents benefit others in the system) in order to identify groups of agents who benefit similar sets of agents. A statistical technique is then used to identify which clusters contain coalitions. Experimentation using the TREET platform verifies the effectiveness of this approach. A series of enhancements to our method are also introduced, which improve the accuracy and robustness of the algorithm. To demonstrate how this broadly-applicable tool can be used to address domain-specific problems, we focus again on trust and reputation systems. We show how, by incorporating our work into one such system (the existing Beta Reputation System), we can provide resistance to collusion. We conclude with a detailed discussion of the value of our work for a wide range of environments, including a variety of multiagent systems and real-world settings
Evaluating collaborative filtering over time
Recommender systems have become essential tools for users to navigate the plethora of content in the
online world. Collaborative filteringโa broad term referring to the use of a variety, or combination,
of machine learning algorithms operating on user ratingsโlies at the heart of recommender systemsโ
success. These algorithms have been traditionally studied from the point of view of how well they can
predict usersโ ratings and how precisely they rank content; state of the art approaches are continuously
improved in these respects. However, a rift has grown between how filtering algorithms are investigated
and how they will operate when deployed in real systems. Deployed systems will continuously be
queried for personalised recommendations; in practice, this implies that system administrators will iteratively
retrain their algorithms in order to include the latest ratings. Collaborative filtering research does
not take this into account: algorithms are improved and compared to each other from a static viewpoint,
while they will be ultimately deployed in a dynamic setting. Given this scenario, two new problems
emerge: current filtering algorithms are neither (a) designed nor (b) evaluated as algorithms that must
account for time. This thesis addresses the divergence between research and practice by examining how
collaborative filtering algorithms behave over time. Our contributions include:
1. A fine grained analysis of temporal changes in rating data and user/item similarity graphs that
clearly demonstrates how recommender system data is dynamic and constantly changing.
2. A novel methodology and time-based metrics for evaluating collaborative filtering over time,
both in terms of accuracy and the diversity of top-N recommendations.
3. A set of hybrid algorithms that improve collaborative filtering in a range of different scenarios.
These include temporal-switching algorithms that aim to promote either accuracy or diversity;
parameter update methods to improve temporal accuracy; and re-ranking a subset of usersโ recommendations
in order to increase diversity.
4. A set of temporal monitors that secure collaborative filtering from a wide range of different
temporal attacks by flagging anomalous rating patterns.
We have implemented and extensively evaluated the above using large-scale sets of user ratings; we
further discuss how this novel methodology provides insight into dimensions of recommender systems
that were previously unexplored. We conclude that investigating collaborative filtering from a temporal
perspective is not only more suitable to the context in which recommender systems are deployed, but
also opens a number of future research opportunities
Non-Hierarchical Networks for Censorship-Resistant Personal Communication.
The Internet promises widespread access to the worldโs collective information and fast communication among people, but common government censorship and spying undermines this potential. This censorship is facilitated by the Internetโs hierarchical structure. Most traffic flows through routers owned by a small number of ISPs, who can be secretly coerced into aiding such efforts. Traditional crypographic defenses are confusing to common users. This thesis advocates direct removal of the underlying heirarchical infrastructure instead, replacing it with non-hierarchical networks. These networks lack such chokepoints, instead requiring would-be censors to control a substantial fraction of the participating devicesโan expensive proposition. We take four steps towards the development of practical non-hierarchical networks. (1) We first describe Whisper, a non-hierarchical mobile ad hoc network (MANET) architecture for personal communication among friends and family
that resists censorship and surveillance. At its core are two novel techniques, an efficient routing scheme based on the predictability of human locations anda variant of onion-routing suitable for decentralized MANETs. (2) We describe the design and implementation of Shout, a MANET architecture for censorship-resistant, Twitter-like public microblogging. (3) We describe the Mason test, amethod used to detect Sybil attacks in ad hoc networks in which trusted authorities are not available. (4) We characterize and model the aggregate behavior of Twitter users to enable simulation-based study of systems like Shout. We use our characterization of the retweet graph to analyze a novel spammer detection technique for Shout.PhDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107314/1/drbild_1.pd
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