Identifying Attack Models for Securing Cluster-Based Recommendation System

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

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Application of Improved Recommendation System Based on Spark Platform in Big Data Analysis

Cybernetics and Information Technologies ◽

10.1515/cait-2016-0092 ◽

2016 ◽

Vol 16 (6) ◽

pp. 245-255 ◽

Cited By ~ 1

Author(s):

Li Xie ◽

Wenbo Zhou ◽

Yaosen Li

Keyword(s):

Big Data ◽

Recommender System ◽

Recommendation System ◽

Parallel Implementation ◽

Implementation Process ◽

Recommendation Algorithm ◽

Filtration Problem ◽

Information Filtration ◽

Similarity Information ◽

Better Than

Abstract In the era of big data, people have to face information filtration problem. For those cases when users do not or cannot express their demands clearly, recommender system can analyse user’s information more proactive and intelligent to filter out something users want. This property makes recommender system play a very important role in the field of e-commerce, social network and so on. The collaborative filtering recommendation algorithm based on Alternating Least Squares (ALS) is one of common algorithms using matrix factorization technique of recommendation system. In this paper, we design the parallel implementation process of the recommendation algorithm based on Spark platform and the related technology research of recommendation systems. Because of the shortcomings of the recommendation algorithm based on ALS model, a new loss function is designed. Before the model is trained, the similarity information of users and items is fused. The experimental results show that the performance of the proposed algorithm is better than that of algorithm based on ALS.

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Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

Complexity ◽

10.1155/2020/6643888 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Hui Ning ◽

Qian Li

Keyword(s):

Collaborative Filtering ◽

Recommendation System ◽

Contextual Information ◽

Rapid Development ◽

Theoretical Research ◽

Context Information ◽

Music Recommendation ◽

Recommendation Algorithm ◽

Points Of Interest ◽

Popularity Prediction

Collaborative filtering technology is currently the most successful and widely used technology in the recommendation system. It has achieved rapid development in theoretical research and practice. It selects information and similarity relationships based on the user’s history and collects others that are the same as the user’s hobbies. User’s evaluation information is to generate recommendations. The main research is the inadequate combination of context information and the mining of new points of interest in the context-aware recommendation process. On the basis of traditional recommendation technology, in view of the characteristics of the context information in music recommendation, a personalized and personalized music based on popularity prediction is proposed. Recommended algorithm is MRAPP (Media Recommendation Algorithm based on Popularity Prediction). The algorithm first analyzes the user’s contextual information under music recommendation and classifies and models the contextual information. The traditional content-based recommendation technology CB calculates the recommendation results and then, for the problem that content-based recommendation technology cannot recommend new points of interest for users, introduces the concept of popularity. First, we use the memory and forget function to reduce the score and then consider user attributes and product attributes to calculate similarity; secondly, we use logistic regression to train feature weights; finally, appropriate weights are used to combine user-based and item-based collaborative filtering recommendation results. Based on the above improvements, the improved collaborative filtering recommendation algorithm in this paper has greatly improved the prediction accuracy. Through theoretical proof and simulation experiments, the effectiveness of the MRAPP algorithm is demonstrated.

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Robust Analysis on a Privacy Preserving Recommendation Algorithm under the KNN Attack

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.610.717 ◽

2014 ◽

Vol 610 ◽

pp. 717-721 ◽

Cited By ~ 1

Author(s):

Yan Gao ◽

Jing Bo Xia ◽

Jing Jing Ji ◽

Ling Ma

Keyword(s):

Collaborative Filtering ◽

Recommendation System ◽

Privacy Preserving ◽

Perturbation Techniques ◽

Recommendation Algorithm ◽

Rating Data ◽

Robust Analysis ◽

History Of

— Among algorithms in recommendation system, Collaborative Filtering (CF) is a popular one. However, the CF methods can’t guarantee the safety of the user rating data which cause private preserving issue. In general, there are four kinds of methods to solve private preserving: Perturbation, randomization, swapping and encryption. In this paper, we mimic algorithms which attack the privacy-preserving methods with randomized perturbation techniques. After leaking part of rating history of a customer, we can infer this customer’s other rating history. At the end, we propose an algorithm to enhance the system so as to avoid being attacked.

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Pre-Filling Collaborative Filtering Algorithm Based on Matrix Factorization

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.411-414.2223 ◽

2013 ◽

Vol 411-414 ◽

pp. 2223-2228

Author(s):

Dong Liang Su ◽

Zhi Ming Cui ◽

Jian Wu ◽

Peng Peng Zhao

Keyword(s):

Collaborative Filtering ◽

Recommender System ◽

Matrix Factorization ◽

Nearest Neighbor ◽

User Profile ◽

Personalized Recommendation ◽

System Quality ◽

Recommendation Algorithm ◽

Collaborative Filtering Algorithm ◽

Personalized Recommender System

Nowadays personalized recommendation algorithm of e-commerce can hardly meet the needs of users as an ever-increasing number of users and items in personalized recommender system has brought about sparsity of user-item rating matrix and the emergence of more and more new users has threatened recommender system quality. This paper puts forward a pre-filled collaborative filtering recommendation algorithm based on matrix factorization, pre-filling user-item matrixes by matrix factorization and building nearest-neighbor models according to new user profile information, thus mitigating the influence of matrix sparsity and new users and improving the accuracy of recommender system. The experimental results suggest that this algorithm is more precise and effective than the traditional one under the condition of extremely sparse user-item rating matrix.

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CAN DISSIMILAR USERS CONTRIBUTE TO ACCURACY AND DIVERSITY OF PERSONALIZED RECOMMENDATION?

International Journal of Modern Physics C ◽

10.1142/s0129183110015786 ◽

2010 ◽

Vol 21 (10) ◽

pp. 1217-1227 ◽

Cited By ~ 24

Author(s):

WEI ZENG ◽

MING-SHENG SHANG ◽

QIAN-MING ZHANG ◽

LINYUAN LÜ ◽

TAO ZHOU

Keyword(s):

Collaborative Filtering ◽

Recommender System ◽

High Accuracy ◽

Personalized Recommendation ◽

Recommendation Algorithm ◽

Filtering Algorithm ◽

Recommendation Algorithms ◽

Collaborative Filtering Algorithm ◽

High Diversity ◽

Better Than

Recommender systems are becoming a popular and important set of personalization techniques that assist individual users with navigating through the rapidly growing amount of information. A good recommender system should be able to not only find out the objects preferred by users, but also help users in discovering their personalized tastes. The former corresponds to high accuracy of the recommendation, while the latter to high diversity. A big challenge is to design an algorithm that provides both highly accurate and diverse recommendation. Traditional recommendation algorithms only take into account the contributions of similar users, thus, they tend to recommend popular items for users ignoring the diversity of recommendations. In this paper, we propose a recommendation algorithm by considering both the effects of similar and dissimilar users under the framework of collaborative filtering. Extensive analyses on three datasets, namely MovieLens, Netflix and Amazon, show that our method performs much better than the standard collaborative filtering algorithm for both accuracy and diversity.

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An Automated Music Recommendation System Based on Listener Preferences

10.3233/apc210182 ◽

2021 ◽

Author(s):

Mukkamala. S.N.V. Jitendra ◽

Y. Radhika

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Recommender System ◽

Recommendation System ◽

Software Tool ◽

Vital Role ◽

Music Recommendation ◽

The World ◽

Different Types ◽

Music Recommendation System

Recommender systems play a vital role in e-commerce. It is a big source of a market that brings people from all over the world to a single place. It has become easy to access and reach the market while sitting anywhere. Recommender systems do a major role in the commerce mobility go smoothly easily as it is a software tool that helps in showing or recommending items based on user’s preferences by analyzing their taste. In this paper, we make a recommender system that would be specifically for music applications. Different people listen to different types of music, so we make note of their taste in music and suggest to them the next song based on their previous choice. This is achieved by using a popularity algorithm, classification, and collaborative filtering. Finally, we make a comparison of the built system for its effectiveness with different evaluation metrics.

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Book Recommendation System using Matrix Factorization

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.36025 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 4578-4582

Author(s):

K. Venkata Ruchitha

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Recommender System ◽

Matrix Factorization ◽

Recommendation System ◽

Previous History ◽

Speed Up ◽

Area Unit ◽

Content Based Filtering

In recent years, recommender systems became more and more common and area unit applied to a various vary of applications, thanks to development of things and its numerous varieties accessible, that leaves the users to settle on from bumper provided choices. Recommendations generally speed up searches and create it easier for users to access content that they're curious about, and conjointly surprise them with offers they'd haven't sought for. By victimisation filtering strategies for pre-processing the information, recommendations area unit provided either through collaborative filtering or through content-based Filtering. This recommender system recommends books supported the description and features. It identifies the similarity between the books supported its description. It conjointly considers the user previous history so as to advocate the identical book.

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A Personalized Recommendation Algorithm Based on the User’s Implicit Feedback in E-Commerce

Future Internet ◽

10.3390/fi10120117 ◽

2018 ◽

Vol 10 (12) ◽

pp. 117 ◽

Cited By ~ 1

Author(s):

Bo Wang ◽

Feiyue Ye ◽

Jialu Xu

Keyword(s):

Collaborative Filtering ◽

Recommendation System ◽

Word Embedding ◽

Purchase Behavior ◽

Personalized Recommendation ◽

Implicit Feedback ◽

Recommendation Algorithm ◽

Rating Data ◽

Feedback Information ◽

Recommendation Accuracy

A recommendation system can recommend items of interest to users. However, due to the scarcity of user rating data and the similarity of single ratings, the accuracy of traditional collaborative filtering algorithms (CF) is limited. Compared with user rating data, the user’s behavior log is easier to obtain and contains a large amount of implicit feedback information, such as the purchase behavior, comparison behavior, and sequences of items (item-sequences). In this paper, we proposed a personalized recommendation algorithm based on a user’s implicit feedback (BUIF). BUIF considers not only the user’s purchase behavior but also the user’s comparison behavior and item-sequences. We extracted the purchase behavior, comparison behavior, and item-sequences from the user’s behavior log; calculated the user’s similarity by purchase behavior and comparison behavior; and extended word-embedding to item-embedding to obtain the item’s similarity. Based on the above method, we built a secondary reordering model to generate the recommendation results for users. The results of the experiment on the JData dataset show that our algorithm shows better improvement in regard to recommendation accuracy over other CF algorithms.

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Application of Using Simulated Annealing to Combine Clustering with Collaborative Filtering for Item Recommendation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.347-350.2747 ◽

2013 ◽

Vol 347-350 ◽

pp. 2747-2751 ◽

Cited By ~ 1

Author(s):

Zhi Ming Feng ◽

Yi Dan Su

Keyword(s):

Simulated Annealing ◽

Collaborative Filtering ◽

Recommender System ◽

Recommendation System ◽

Experimental Results ◽

Good Effect ◽

Large Item ◽

Performance Reduction ◽

An Electronic Commerce Recommender System Based on Product Character

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.267.909 ◽

2011 ◽

Vol 267 ◽

pp. 909-912 ◽

Cited By ~ 1

Author(s):

Shen Bao Chen

Keyword(s):

Electronic Commerce ◽

Collaborative Filtering ◽

Recommender System ◽

Recommendation System ◽

Competitive Environment ◽

Referral Process ◽

Personalized Service ◽

Filtering Algorithm ◽

Customer Churn ◽

Collaborative Filtering Algorithm

In the increasingly competitive environment, in order to effectively preserve the user, preventing customer churn, increase sales of e-commerce systems, e-commerce recommendation system in the importance of the products has been revealed. Recommendation system in e-commerce system can provide commodity information and advice to help customers decide what products to buy, analog sales staff to complete the purchase of goods to the customer referral process so that customers feel completely personalized service. To improve the item-based collaborative filtering algorithm, an electronic commerce recommendation system based on product character is presented. This approach revises the original similarity using product character, takes into account the influence of product character and customer rating, and combines the customer rating similarity and the product character similarity.

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