[논문]이분법 선호도를 고려한 강건한 추천 시스템

이재훈; 오하영; 김종권

doi:10.13089/jkiisc.2016.26.4.953

[국내논문] 이분법 선호도를 고려한 강건한 추천 시스템
Bipartite Preference aware Robust Recommendation System 원문보기

情報保護學會論文誌 = Journal of the Korea Institute of Information Security and Cryptology, v.26 no.4, 2016년, pp.953 - 960

초록
AI-Helper

온라인 시스템이 활성화 되고 접근 가능한 정보의 양이 늘어나면서 추천 시스템의 영향력 또한 커지고 있다. 하지만 일부 악의적인 유저들의 공격으로 인해 시스템에 대한 신뢰도를 저하시키고 조작하려는 시도가 늘고 있다. 본 연구팀은 해당 리뷰에 대한 공감, 비공감 비율을 분석하고 이를 추천 시스템에 적용함으로써 추천 시스템의 성능을 향상시키고 강건한 시스템을 유지할 수 있는 방법을 제안한다. 실제 영화 데이터를 수집하여 적용해 본 결과 기존의 추천 시스템보다 향상된 성능을 보였다.

Abstract ▼ AI-Helper

Due to the prevalent use of online systems and the increasing amount of accessible information, the influence of recommender systems is growing bigger than ever. However, there are several attempts by malicious users who try to compromise or manipulate the reliability of recommender systems with cyber-attacks. By analyzing the ratio of 'sympathy' against 'apathy' responses about a concerned review and reflecting the results in a recommendation system, we could present a way to improve the performance of a recommender system and maintain a robust system. After collecting and applying actual movie review data, we found that our proposed recommender system showed an improved performance compared to the existing recommendation systems.

주제어

AI 본문요약
AI-Helper

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문제 정의

This study could establish a robust recommender system through analysis of sympathy and apathy responses. By the utilizing a review evaluation method, the proposed recommender system is highly likely to be used as a whole new approach compared to the existing rating-based system.

가설 설정

3. We develop a more accurate recommender system by enabling the recommendation system to thwart the influence of Sybils.

제안 방법

Through this, we could confirm that the responses were extremely divided. Based on the results, we tried to enhance the performance of item recommendation for normal users by using the mean values of the concerned users.
In addition, they focus on another important part of tolerating label noise concept. Even though the labeling is not correct, the authors designed resilient SybilBelief with the probabilistic approach.
Ever since questions were first raised about Sybil attacks[2], which forged a countless number of IDs with a malicious intention to disrupt a recommender system, many related studies have been carried out. In [3], the study provided the definitions of various attacks including push attack and nuke attack and conducted an experiment with the actual data based on this. In [4], the study conducted the detection of five types of attacks by malicious users by using the K-NN, K-Means Clustering, and PLSA (Probabilistic Latent Semantic Analysis) techniques.
The proposed scheme is resilient to noise in our prior knowledge about known legitimate IDs and Sybils. Moreover, the proposed scheme performs orders of magnitudes better than existing Sybil classification mechanisms and significantly better than existing Sybil ranking mechanisms.
We show that the proposed scheme is able to accurately identify Sybil with low false positive rates and low false negative rates. The proposed scheme is resilient to noise in our prior knowledge about known legitimate IDs and Sybils. Moreover, the proposed scheme performs orders of magnitudes better than existing Sybil classification mechanisms and significantly better than existing Sybil ranking mechanisms.
The website can defend itself from Sybil attacks by evaluating the reliability of a concerned movie review with the ratio of sympathy responses against apathy responses. No matter how hard Sybils try to manipulate the average rating about a movie review, it will be impossible unless it earns the sympathy of other users.
We classified 90% of the collected data as the training group and the remaining 10% as the test group, and conducted each experiment for five times to produce the mean values. We experiment various sybil classification thresholds T between –0.

이론/모형

In [3], the study provided the definitions of various attacks including push attack and nuke attack and conducted an experiment with the actual data based on this. In [4], the study conducted the detection of five types of attacks by malicious users by using the K-NN, K-Means Clustering, and PLSA (Probabilistic Latent Semantic Analysis) techniques. In [5], the detection of abnormal users was carried out using the supervised classification technique which utilized the distribution and similarity.
In this paper, we used the gradient descent method to solve the optimization problem. Also, in order to prevent the overfitting problem, Equation (2) can be expressed as Equation (3) by including the normalization part.
To compare the performances between our proposed scheme and the existing method, we used the Mean Absolute Error (MAE), which is widely used as the standard indicator of accuracy for recommendation systems.

성능/효과

2. The reliability of the recommender system about a movie review was secured by analyzing the ratio of sympathy responses against apathy responses.

참고문헌 (10)

J.R Douceur, "The Sybil attack," In International Workshop on Peer-to- Peer Systems, pp. 251-260, Mar. 2002.
M.P. O'Mahony, N.J. Hurley and G.C.M. Silverstre, "Promoting recommendations: An attack on collaborative filtering," In Proceedings of the International Conference on Database and Expert Systems Applications. pp. 494 -503, Sep. 2002.
M.P. O'Mahony, N.J. Hurley, G.C.M. Silverstre, "Recommender systems: Attack types and strategies," In Proceedings of the 20st National Conference on Artificial Intelligence, pp. 334-339, Jul. 2005.
B. Mobasher, R. Burke, and JJ. Sandvig, "Model-based collaborative filtering as a defense against profile injection attacks," In Proceedings of the 21st National Conference on Artificial Intelligence, pp. 1388-1393, Jul. 2006.
R. Burke, B. Mobasher, C.A. Williams and R. Bhaumik. "Classification features for attack detection in collaborative recommender systems," In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 542-547, Aug. 2006.
N. Hurley, Z. Cheng, and M. Zhang. "Statistical attack detection," In Proceedings of the third ACM conference on Recommender systems. pp. 149-156, Oct. 2009.
C.A. Williams, B. Mobasher and R. Burke. "Defending Recommender Systems: Detection of Profile Injection Attacks," Service Oriented Computing and Applications, vol. 1, no. 3, pp. 157-170. Nov. 2007.

상세보기
T. Noh H. Oh, G. Noh and C. Kim. "STA : Sybil Type-aware Robust Recommender System," KIISE Transactions on Computing Practices, vol. 21, no. 10, pp. 670-679, Oct. 2015.

상세보기
Z. Zhang and S.R Kulkarni, "Detection of shilling attacks in recommender systems via spectral clustering", In Proceedings of the International Conference on Information Fusion, pp. 1-8, Jul. 2014.
N.Z. Gong, M. Frank, and P. Mittal, "SybilBelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection," IEEE Transactions on Information Forensics and Security, vol. 9, no. 6, pp. 976-987, Jun. 2014.

상세보기

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