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

2014 ◽  
Vol 610 ◽  
pp. 717-721 ◽  
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.

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

2018 ◽  
Vol 10 (12) ◽  
pp. 117 ◽  
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.

scholarly journals A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Guangxia Xu ◽  
Zhijing Tang ◽  
Chuang Ma ◽  
Yanbing Liu ◽  
Mahmoud Daneshmand
Keyword(s):  
Collaborative Filtering ◽  
Traditional Method ◽  
Recommendation System ◽  
Rapid Development ◽  
Poor Performance ◽  
Mobile Internet ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Similarity Calculation

Complex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation system which utilizes the user’s behaviour information to recommend interesting items emerged. Currently, collaborative filtering has been successfully utilized in personalized recommendation systems. However, under the condition of extremely sparse rating data, the traditional method of similarity between users is relatively simple. Moreover, it does not consider that the user’s interest will change over time, which results in poor performance. In this paper, a new similarity measure method which considers user confidence and time context is proposed to preferably improve the similarity calculation between users. Finally, the experimental results demonstrate that the proposed algorithm is suitable for the sparse data and effectively improves the prediction accuracy and enhances the recommendation quality at the same time.

Collaborative Filtering Recommendation Algorithm Integrated into Co-Rating Impact Factor

2014 ◽  
Vol 926-930 ◽  
pp. 3004-3007
Author(s):  
Xu Yang Wang ◽  
Heng Liu
Keyword(s):  
Impact Factor ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Recommendation Accuracy ◽  
The Impact ◽  
Rating Information ◽  
Original Rating

The sparsity rating data is one of the main challenges of recommendation system. For this problem, we presented a collaborative filtering recommendation algorithm integrated into co-ratings impact factor. The method reduced the sparsity of rating matrix by filling the original rating matrix. It made the full use of rating information and took the impact on similarity of co-ratings between users into consideration when looking for the nearest neighbor so that the similarities were accurately computed. Experimental results showed that the proposed algorithm, to some extent, improved the recommendation accuracy.

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: 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.

scholarly journals Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics

2021 ◽  
Vol 11 (20) ◽  
pp. 9554
Author(s):  
Jianjun Ni ◽  
Yu Cai ◽  
Guangyi Tang ◽  
Yingjuan Xie
Keyword(s):  
Collaborative Filtering ◽  
User Characteristics ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Rating Data ◽  
User Similarity ◽  
Public Data ◽  
Weighted Algorithm ◽  
Collaborative Filtering Algorithm ◽  
The Impact

The recommendation algorithm is a very important and challenging issue for a personal recommender system. The collaborative filtering recommendation algorithm is one of the most popular and effective recommendation algorithms. However, the traditional collaborative filtering recommendation algorithm does not fully consider the impact of popular items and user characteristics on the recommendation results. To solve these problems, an improved collaborative filtering algorithm is proposed, which is based on the Term Frequency-Inverse Document Frequency (TF-IDF) method and user characteristics. In the proposed algorithm, an improved TF-IDF method is used to calculate the user similarity on the basis of rating data first. Secondly, the multi-dimensional characteristics information of users is used to calculate the user similarity by a fuzzy membership method. Then, the above two user similarities are fused based on an adaptive weighted algorithm. Finally, some experiments are conducted on the movie public data set, and the experimental results show that the proposed method has better performance than that of the state of the art.

scholarly journals Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

Complexity ◽  
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.

A Collaborative Filtering Recommendation Algorithm Based on Cloud Model, Item Attribute and User Data

2014 ◽  
Vol 610 ◽  
pp. 747-751
Author(s):  
Jian Sun ◽  
Xiao Ying Chen
Keyword(s):  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Cloud Model ◽  
Prediction Algorithm ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
User Data ◽  
Item Attribute ◽  
Rating Prediction ◽  
User Ratings

Aiming at the problems of extremely sparse of user-item rating data and poor recommendation quality, we put forward a collaborative filtering recommendation algorithm based on cloud model, item attribute and user data which combined with the existing literatures. A rating prediction algorithm based on cloud model and item attribute is proposed, based on idea that the similar users rating for the same item are similar and the same user ratings for the similar items are similar and stable. Through compare and analysis this paper’s and other studies experimental results, we get the conclusion that the rating prediction accuracy is improved.

scholarly journals MINING TRUST VALUES FROM RECOMMENDATION ERRORS

2006 ◽  
Vol 15 (06) ◽  
pp. 945-962 ◽  
Author(s):  
JOHN O'DONOVAN ◽  
BARRY SMYTH
Keyword(s):  
Collaborative Filtering ◽  
User Satisfaction ◽  
Information Needs ◽  
Recommendation System ◽  
Predictive Accuracy ◽  
Target User ◽  
Fair Comparison ◽  
Collaborative Filtering Algorithm ◽  
History Of ◽  
User Trust

Increasing availability of information has furthered the need for recommender systems across a variety of domains. These systems are designed to tailor each user's information space to suit their particular information needs. Collaborative filtering is a successful and popular technique for producing recommendations based on similarities in users' tastes and opinions. Our work focusses on these similarities and the fact that current techniques for defining which users contribute to recommendation are in need of improvement. In this paper we propose the use of trustworthiness as an improvement to this situation. In particular, we define and empirically test a technique for eliciting trust values for each producer of a recommendation based on that user's history of contributions to recommendations. We compute a recommendation range to present to a target user. This is done by leveraging under/overestimate errors in users' past contributions in the recommendation process. We present three different models to compute this range. Our evaluation shows how this trust-based technique can be easily incorporated into a standard collaborative filtering algorithm and we define a fair comparison in which our technique outperforms a benchmark algorithm in predictive accuracy. We aim to show that the presentation of absolute rating predictions to users is more likely to reduce user trust in the recommendation system than presentation of a range of rating predictions. To evaluate the trust benefits resulting from the transparency of our recommendation range techniques, we carry out user-satisfaction trials on BoozerChoozer, a pub recommendation system. Our user-satisfaction results show that the recommendation range techniques perform up to twice as well as the benchmark.

scholarly journals Penerapan Content-Boosted Collaborative Filtering untuk Meningkatkan Kemampuan Sistem Rekomendasi Penyedia Jasa Acara Pernikahan

2020 ◽  
Vol 12 (1) ◽  
pp. 112
Author(s):  
Rahman Indra Kesuma ◽  
Amirul Iqbal
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Digital World ◽  
Business Opportunities ◽  
Efficiency And Effectiveness ◽  
Long Time ◽  
History Of ◽  
Smartphone Technology ◽  
World Development

AbstractThe changes in lifestyle of the global society in the era of digital world development have made the smartphone technology penetration to rise continually. This condition can increase business opportunities, especially e-commerce activities that utilize technology and the internet in terms of promotions and transactions. The efficiency and effectiveness is an interesting focus that is discussed in this issue. For example, in services or products searching for a wedding where many customers still feel difficult and need a long time to find the desired things. The existence of a recommendation system also has not been able to help, especially for users who are newly registered to the system. This is because most of them will provide recommendations based on a history of user activity. Therefore, this study applies the content-boosted collaborative filtering (CBCF) method to improve the ability of the recommendation system in providing recommendations for weddings, especially for a new user. The obtained results are then compared with two commonly used methods, content-based recommendations (CB) and collaborative filtering (CF). Based on the experimental results, it can be concluded that CBCF can maintain the quality of good recommendations for long registered users with an accuracy of 84% and also can provide recommendations for new users with an accuracy of 54% which is cannot be solved by CB or CF methods.Key Word: digital businesses, wedding vendors/organizers, recommendation system, content-boosted collaborative filtering  AbstrakPerubahan pola kehidupan masyarakat global pada era perkembangan dunia digital membuat penetrasi dari teknologi telepon pintar terus menaik. Kondisi ini dapat meningkatkan kesempatan bisnis khususnya kegiatan jual beli yang memanfaatkan teknologi dan internet dalam hal promosi dan transaksi. Efisiensi dan efektifitas proses menjadi fokus yang terus menarik dibahas dalam hal ini. Sebagai contoh, pada pencarian layanan atau produk untuk pernikahan yang mana banyak pelanggan masih merasakan kesulitan dan membutuhkan waktu yang lama untuk mencari sesuatu yang diinginkannya. Keberadaan sistem rekomendasi juga belum bisa membantu terlebih bagi pengguna yang baru terdaftar pada sistem. Hal ini dikarenakan kebanyakan sistem akan memberikan rekomendasi berdasarkan rekam jejak aktifitas pengguna. Maka itu, pada penelitian ini diusulkan penerapan metode content-boosted collaborative filtering (CBCF) untuk meningkatkan kemampuan sistem rekomendasi dalam pemberian rekomendasi untuk acara pernikahan, khususnya pada pengguna baru. Hasil yang diperoleh selanjutnya dibandingkan dengan dua metode yang umum digunakan yaitu content based recommendation (CB) dan collaborative filtering (CF). Berdasarkan hasil penelitian yang diperoleh, dapat disimpulkan bahwa CBCF dapat mempertahankan kualitas pemberian rekomendasi yang baik untuk pengguna lama dengan akurasi sebesar 84% serta mampu memberikan rekomendasi untuk pengguna baru dengan akurasi 54% yang mana kondisi ini tidak bisa diselesaikan oleh metode CB ataupun CF.Kata Kunci: bisnis digital, penyedia jasa acara pernikahan, sistem rekomendasi, content-boosted collaborative filtering 

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