scholarly journals Exposing Private User Behaviors of Collaborative Filtering via Model Inversion Techniques

2020 ◽  
Vol 2020 (3) ◽  
pp. 264-283
Author(s):  
Seira Hidano ◽  
Takao Murakami ◽  
Shuichi Katsumata ◽  
Shinsaku Kiyomoto ◽  
Goichiro Hanaoka
Keyword(s):  
Collaborative Filtering ◽  
Moderate Amount ◽  
Model Inversion ◽  
User Behaviors ◽  
Private Behavior ◽  
Public Behavior ◽  
Inference Attack ◽  
Real World Datasets ◽  
Privacy Risks ◽  
Inversion Techniques

AbstractPrivacy risks of collaborative filtering (CF) have been widely studied. The current state-of-theart inference attack on user behaviors (e.g., ratings/purchases on sensitive items) for CF is by Calandrino et al. (S&P, 2011). They showed that if an adversary obtained a moderate amount of user’s public behavior before some time T, she can infer user’s private behavior after time T. However, the existence of an attack that infers user’s private behavior before T remains open. In this paper, we propose the first inference attack that reveals past private user behaviors. Our attack departs from previous techniques and is based on model inversion (MI). In particular, we propose the first MI attack on factorization-based CF systems by leveraging data poisoning by Li et al. (NIPS, 2016) in a novel way. We inject malicious users into the CF system so that adversarialy chosen “decoy” items are linked with user’s private behaviors. We also show how to weaken the assumption made by Li et al. on the information available to the adversary from the whole rating matrix to only the item profile and how to create malicious ratings effectively. We validate the effectiveness of our inference algorithm using two real-world datasets.

scholarly journals NeuSE: A Neural Snapshot Ensemble Method for Collaborative Filtering

2021 ◽  
Vol 15 (6) ◽  
pp. 1-20
Author(s):  
Dongsheng Li ◽  
Haodong Liu ◽  
Chao Chen ◽  
Yingying Zhao ◽  
Stephen M. Chu ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Optimization Problems ◽  
Empirical Studies ◽  
Large Datasets ◽  
Model Learning ◽  
Global Models ◽  
Convex Optimization Problems ◽  
Memory Network ◽  
Real World Datasets ◽  
Performance Tradeoff

In collaborative filtering (CF) algorithms, the optimal models are usually learned by globally minimizing the empirical risks averaged over all the observed data. However, the global models are often obtained via a performance tradeoff among users/items, i.e., not all users/items are perfectly fitted by the global models due to the hard non-convex optimization problems in CF algorithms. Ensemble learning can address this issue by learning multiple diverse models but usually suffer from efficiency issue on large datasets or complex algorithms. In this article, we keep the intermediate models obtained during global model learning as the snapshot models, and then adaptively combine the snapshot models for individual user-item pairs using a memory network-based method. Empirical studies on three real-world datasets show that the proposed method can extensively and significantly improve the accuracy (up to 15.9% relatively) when applied to a variety of existing collaborative filtering methods.

scholarly journals Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 19-26 ◽  
Author(s):  
Chong Chen ◽  
Min Zhang ◽  
Yongfeng Zhang ◽  
Weizhi Ma ◽  
Yiqun Liu ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Heterogeneous Data ◽  
Model Parameters ◽  
Online Systems ◽  
Practical Applications ◽  
Real World Datasets ◽  
Primary Type

Recent studies on recommendation have largely focused on exploring state-of-the-art neural networks to improve the expressiveness of models, while typically apply the Negative Sampling (NS) strategy for efficient learning. Despite effectiveness, two important issues have not been well-considered in existing methods: 1) NS suffers from dramatic fluctuation, making sampling-based methods difficult to achieve the optimal ranking performance in practical applications; 2) although heterogeneous feedback (e.g., view, click, and purchase) is widespread in many online systems, most existing methods leverage only one primary type of user feedback such as purchase. In this work, we propose a novel non-sampling transfer learning solution, named Efficient Heterogeneous Collaborative Filtering (EHCF) for Top-N recommendation. It can not only model fine-grained user-item relations, but also efficiently learn model parameters from the whole heterogeneous data (including all unlabeled data) with a rather low time complexity. Extensive experiments on three real-world datasets show that EHCF significantly outperforms state-of-the-art recommendation methods in both traditional (single-behavior) and heterogeneous scenarios. Moreover, EHCF shows significant improvements in training efficiency, making it more applicable to real-world large-scale systems. Our implementation has been released 1 to facilitate further developments on efficient whole-data based neural methods.

Genetic Algorithm Influenced Top-N Recommender System to Alleviate New User Cold Start Problem

2021 ◽  
pp. 1492-1512
Author(s):  
Sharon Moses J. ◽  
Dhinesh Babu L.D.
Keyword(s):  
Genetic Algorithm ◽  
Collaborative Filtering ◽  
Cold Start ◽  
Filtering Algorithm ◽  
Other Information ◽  
Collaborative Filtering Algorithm ◽  
Real World Datasets ◽  
Gender Based ◽  
Recommender Algorithm ◽  
Cold Start Problem

Most recommender systems are based on the familiar collaborative filtering algorithm to suggest items. Quite often, collaborative filtering algorithm fails in generating recommendations due to the lack of adequate user information resulting in new user cold start problem. The cold start problem is one among the prevailing issue in recommendation system where the system fails to render recommendations. To overcome the new user cold start issue, demographical information of the user is utilised as the user information source. Among the demographical information, the impact of the user gender is less explored when compared with other information like age, profession, region, etc. In this work, a genetic algorithm-influenced gender-based top-n recommender algorithm is proposed to address the new user cold start problem. The algorithm utilises the evolution concepts of the genetic algorithm to render top-n recommendations to a new user. The evaluation of the proposed algorithm using real world datasets proved that the algorithm has a better efficiency than the state of art approaches.

Expert Detection and Recommendation Model With User-Generated Tags in Collaborative Tagging Systems

2020 ◽  
Vol 31 (4) ◽  
pp. 24-45
Author(s):  
Mengmeng Shen ◽  
Jun Wang ◽  
Ou Liu ◽  
Haiying Wang
Keyword(s):  
Collaborative Filtering ◽  
Community Detection ◽  
Real World ◽  
Agglomerative Hierarchical Clustering ◽  
Collaborative Tagging ◽  
Excellent Performance ◽  
Recommendation Algorithm ◽  
Proposed Model ◽  
Target User ◽  
Real World Datasets

Tags generated in collaborative tagging systems (CTSs) may help users describe, categorize, search, discover, and navigate content, whereas the difficulty is how to go beyond the information explosion and obtain experts and the required information quickly and accurately. This paper proposes an expert detection and recommendation (EDAR) model based on semantics of tags; the framework consists of community detection and EDAR. Specifically, this paper firstly mines communities based on an improved agglomerative hierarchical clustering (I-AHC) to cluster tags and then presents a community expert detection (CED) algorithm for identifying community experts, and finally, an expert recommendation algorithm is proposed based the improved collaborative filtering (CF) algorithm to recommend relevant experts for the target user. Experiments are carried out on real world datasets, and the results from data experiments and user evaluations have shown that the proposed model can provide excellent performance compared to the benchmark method.

scholarly journals Quaternion Collaborative Filtering for Recommendation

2019 ◽  
Author(s):  
Shuai Zhang ◽  
Lina Yao ◽  
Lucas Vinh Tran ◽  
Aston Zhang ◽  
Yi Tay
Keyword(s):  
Collaborative Filtering ◽  
Quaternion Algebra ◽  
Wide Spectrum ◽  
Representation Learning ◽  
Real Space ◽  
Inner Product ◽  
Hypercomplex Numbers ◽  
Learning Capability ◽  
Inductive Bias ◽  
Real World Datasets

This paper proposes Quaternion Collaborative Filtering (QCF), a novel representation learning method for recommendation. Our proposed QCF relies on and exploits computation with Quaternion algebra, benefiting from the expressiveness and rich representation learning capability of Hamilton products. Quaternion representations, based on hypercomplex numbers, enable rich inter-latent dependencies between imaginary components. This encourages intricate relations to be captured when learning user-item interactions, serving as a strong inductive bias  as compared with the real-space inner product. All in all, we conduct extensive experiments on six real-world datasets, demonstrating the effectiveness of Quaternion algebra in recommender systems. The results exhibit that QCF outperforms a wide spectrum of strong neural baselines on all datasets. Ablative experiments confirm the effectiveness of Hamilton-based composition over multi-embedding composition in real space. 

scholarly journals Discrete Trust-aware Matrix Factorization for Fast Recommendation

2019 ◽  
Author(s):  
Guibing Guo ◽  
Enneng Yang ◽  
Li Shen ◽  
Xiaochun Yang ◽  
Xiaodong He
Keyword(s):  
Social Influence ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Social Relations ◽  
Real World ◽  
Matrix Factorization ◽  
State Of The Art ◽  
Proposed Model ◽  
Hamming Space ◽  
Real World Datasets

Trust-aware recommender systems have received much attention recently for their abilities to capture the influence among connected users. However, they suffer from the efficiency issue due to large amount of data and time-consuming real-valued operations. Although existing discrete collaborative filtering may alleviate this issue to some extent, it is unable to accommodate social influence. In this paper we propose a discrete trust-aware matrix factorization (DTMF) model to take dual advantages of both social relations and discrete technique for fast recommendation. Specifically, we map the latent representation of users and items into a joint hamming space by recovering the rating and trust interactions between users and items. We adopt a sophisticated discrete coordinate descent (DCD) approach to optimize our proposed model. In addition, experiments on two real-world datasets demonstrate the superiority of our approach against other state-of-the-art approaches in terms of ranking accuracy and efficiency.

scholarly journals A Deep Learning-Based Course Recommender System for Sustainable Development in Education

2021 ◽  
Vol 11 (19) ◽  
pp. 8993
Author(s):  
Qinglong Li ◽  
Jaekyeong Kim
Keyword(s):  
Deep Learning ◽  
Online Education ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Personalized Recommendation ◽  
Course Content ◽  
User Behaviors ◽  
Real World Datasets ◽  
High Level

Recently, the worldwide COVID-19 pandemic has led to an increasing demand for online education platforms. However, it is challenging to correctly choose course content from among many online education resources due to the differences in users’ knowledge structures. Therefore, a course recommender system has the essential role of improving the learning efficiency of users. At present, many online education platforms have built diverse recommender systems that utilize traditional data mining methods, such as Collaborative Filtering (CF). Despite the development and contributions of many recommender systems based on CF, diverse deep learning models for personalized recommendation are being studied because of problems such as sparsity and scalability. Therefore, to solve traditional recommendation problems, this study proposes a novel deep learning-based course recommender system (DECOR), which elaborately captures high-level user behaviors and course attribute features. The DECOR model can reduce information overload, solve high-dimensional data sparsity problems, and achieve high feature information extraction performance. We perform several experiments utilizing real-world datasets to evaluate the DECOR model’s performance compared with that of traditional recommendation approaches. The experimental results indicate that the DECOR model offers better and more robust recommendation performance than the traditional methods.

A dorsiventral leaf radiative transfer model: Development, validation and improved model inversion techniques

2009 ◽  
Vol 113 (12) ◽  
pp. 2560-2573 ◽  
Author(s):  
Jan Stuckens ◽  
Willem W. Verstraeten ◽  
Stephanie Delalieux ◽  
Rony Swennen ◽  
Pol Coppin
Keyword(s):  
Radiative Transfer ◽  
Model Development ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Model Inversion ◽  
Improved Model ◽  
Inversion Techniques
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