• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.26 no.4
• /
• pp.953-960
• /
• 2016

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.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.28 no.1
• /
• pp.241-248
• /
• 2018

A Recommender System (RS) is a system that provides optimized information to users in an over-supply situation. The key to RS is to accurately predict the behavior of the user. The Matrix Factorization (MF) method was used for this prediction in the early stage, and according to the recent SNS development, social information is additionally utilized to improve prediction accuracy. In this paper, we use RS internal trust cluster, which was overlooked in previous studies, to further improve performance and analyze the characteristics of trust clusters.

• KIISE Transactions on Computing Practices
• /
• v.21 no.10
• /
• pp.670-679
• /
• 2015

With a rapid development of internet, many users these days refer to various recommender sites when buying items, movies, music and more. However, there are malicious users (Sybil) who raise or lower item ratings intentionally in these recommender sites. And as a result, a recommender system (RS) may recommend incomplete or inaccurate results to normal users. We suggest a recommender algorithm to separate ratings generated by users into normal ratings and outlier ratings, and to minimize the effects of malicious users. Specifically, our algorithm first ensures a stable RS against three kinds of attack models (Random attack, Average attack, and Bandwagon attack) which are the main recent security issues in RS. To prove the performance of the method of suggestion, we conducted performance analysis on real world data that we crawled. The performance analysis demonstrated that the suggested method performs well regardless of Sybil size and type when compared to existing algorithms.

• Journal of Internet Computing and Services
• /
• v.10 no.6
• /
• pp.81-98
• /
• 2009

Collaborative filtering, one of the most successful technologies among recommender systems, is a system assisting users in easily finding the useful information and supporting the decision making. However, despite of its success and popularity, one notable issue is incredibility of recommendations by unreliable users called shilling attacks. To deal with this problem, in this paper, we analyze the type of shilling attacks and propose a unique method of building a model for protecting the recommender system against manipulated ratings. In addition, we present a method of applying the model to collaborative filtering which is highly robust and stable to shilling attacks.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.5
• /
• pp.624-629
• /
• 2011

In this paper, a new kernelized approach for use in a recommender system (RS) is proposed. Using a machine learning technique, the proposed method predicts the user's preferences for unknown items and recommends items which are likely to be preferred by the user. Since the ratings of the users are generally inconsistent and noisy, a robust binary classifier called a dual margin Lagrangian support vector machine (DMLSVM) is employed to suppress the noise. The proposed method is applied to MovieLens databases, and its effectiveness is demonstrated via simulations.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.27 no.6
• /
• pp.1499-1506
• /
• 2017

The use of smartphones and the launch of various apps have increased exponentially, and malicious apps have also increased. Existing app recommendation systems have been limited to operate based on static information analysis such as ratings, comments, and popularity categories of other users who are online. In this paper, we first propose a robust app recommendation system that realistically uses dynamic information of apps actually used in smartphone and considers static information and dynamic information at the same time. In other words, this paper proposes a robust Android app recommendation system by partially reflecting the time of the app, the frequency of use of the app, the interaction between the app and the app, and the number of contact with the Android kernel. As a result of the performance evaluation, the proposed method proved to be a robust and efficient app recommendation system.

• Journal of Intelligence and Information Systems
• /
• v.28 no.3
• /
• pp.119-137
• /
• 2022

Recent recommendation system studies apply various deep learning models to represent user and item interactions better. One of the noteworthy studies is ONCF(Outer product-based Neural Collaborative Filtering) which builds a two-dimensional interaction map via outer product and employs CNN (Convolutional Neural Networks) to learn high-order correlations from the map. However, ONCF has limitations in recommendation performance due to the problems with CNN and the absence of side information. ONCF using CNN has an inductive bias problem that causes poor performances for data with a distribution that does not appear in the training data. This paper proposes to employ a Vision Transformer (ViT) instead of the vanilla CNN used in ONCF. The reason is that ViT showed better results than state-of-the-art CNN in many image classification cases. In addition, we propose a new architecture to reflect side information that ONCF did not consider. Unlike previous studies that reflect side information in a neural network using simple input combination methods, this study uses an independent auxiliary classifier to reflect side information more effectively in the recommender system. ONCF used a single latent vector for user and item, but in this study, a channel is constructed using multiple vectors to enable the model to learn more diverse expressions and to obtain an ensemble effect. The experiments showed our deep learning model improved performance in recommendation compared to ONCF.

• Journal of Internet Computing and Services
• /
• v.14 no.5
• /
• pp.59-67
• /
• 2013

In this paper, we propose the LAR_CF, latent attribute rating-based collaborative filtering, that is robust to data sparsity problem which is one of traditional problems caused of decreasing rating prediction accuracy. As compared with that existing collaborative filtering method uses a preference rating rated by users as feature vector to calculate similarity between objects, the proposed method improves data sparsity problem using unique attributes of two target objects with existing explicit preference. We consider MovieLens 100k dataset and its item attributes to evaluate the LAR_CF. As a result of artificial data sparsity and full-rating experiments, we confirmed that rating prediction accuracy can be improved rating prediction accuracy in data sparsity condition by the LAR_CF.