Amazon Dataset Research Articles

An Image Recommendation Algorithm Based on Target Alternating Attention and User Affiliation Network

Currently, how to exploit the deep features of images in image recommender systems to achieve image enhancement still needs further research. In addition, little research has explored the implicit and increasing preferences of users by using the affiliation generated by indirect users and virtual users of the main users, which leads to the phenomenon of information cocoon. An Image Recommendation Algorithm Based on Target Alternating Attention and User Affiliation Network (TAUA) is proposed in this paper that addresses the problems of inadequate extraction of semantic features in an image and information cocoon in image recommender systems. First, to complete the multi-dimensional description of the image, we extract the category, color, and style features of the image through a multi-channel convolutional neural network (MCNN), and we then perform migration and integration on these features. Then, to enhance the pixel-level representation ability of the image and achieve image feature enhancement, we propose target alternating attention to capture the information of surrounding pixels alternately from inside to outside. Finally, a user affiliation network, including indirect users and virtual users, is established according to the user behavior and transaction record, and the users’ increasing preferences and affiliated users are mined through the implicit interaction relationship of users. Experimental results show that compared with baselines on the Amazon dataset, the results of F@10, NDCG@10, and AUC of the proposed algorithm are 4.02%, 5.00%, and 2.14% higher than those of ACF, and 5.76%, 0.86% and 1.16% higher than those of VPOI. On the Flickr dataset, our algorithm outperforms ACF by 5.74%, 5.12%, and 3.68% in F@10, NDCG@10, and AUC, respectively, and outperforms VPOI by 0.45%, 0.47%, and 0.49%. TAUA has better recommendation performance and can significantly improve the recommendation effect.

Detecting Group Shilling Attacks In Recommender Systems Based On User Multi-dimensional Features And Collusive Behaviour Analysis

Abstract Group shilling attacks are more threatening than individual shilling attacks due to the collusive behaviours among group members, which pose a great challenge to the credibility of recommender systems. Detection of group shilling attacks can reduce the risk caused by such attacks and ensure the credibility of recommendations. The existing methods for detecting group shilling attacks mainly extract features from the rating patterns of users at group level to measure the shilling behaviours of groups. However, they may become ineffective with the change of attack strategy, resulting in a decrease in detection performance. Aiming at this problem, a new solution based on user multi-dimensional features and collusive behaviour analysis is presented for detecting group shilling attacks. First, we employ the information entropy and latent semantic analysis to analyse the user behavioural patterns from dimensions of item, rating, time and interest, and propose a suite of indicators to measure the anomaly behaviours of users. Second, we propose a measure based on the multi-dimensional features of users to capture the collusion of group members from the perspective of their synchronized behaviours and abnormal behaviours, and treat the groups with high collusion as candidate groups. Finally, based on the multi-dimensional features of users, we construct the user behaviour similarity matrix using Gaussian radial basis function (Gaussian-RBF) and adopt the spectral clustering algorithm to spot group shilling attackers in the candidate groups. Experiments show that the detection performance (F1-measure) of the proposed method can achieve 0.965, 0.964, 0.991 and 0.868 on the Netflix, CiaoDVD, Epinions and Amazon datasets, respectively, which is better than that of state-of-the-art methods.

KC-GCN: A Semi-Supervised Detection Model against Various Group Shilling Attacks in Recommender Systems

Various detection methods have been proposed for defense against group shilling attacks in recommender systems; however, these methods cannot effectively detect attack groups generated based on adversarial attacks (e.g., GOAT) or mixed attack groups. In this study, we propose a two-stage method, called KC-GCN, which is based on k -cliques and graph convolutional networks. First, we construct a user relationship graph, generate suspicious candidate groups, and extract influential users by calculating the user nearest-neighbor similarity. We construct the user relationship graph by calculating the edge weight between any two users through analyzing their similarity over suspicious time intervals on each item. Second, we combine the extracted user initial embeddings and the structural features hidden in the user relationship graph to detect attackers. On the Netflix and sampled Amazon datasets, the detection results of KC-GCN surpass those of the state-of-the-art methods under different types of group shilling attacks. The F1-measure of KC-GCN can reach above 93% and 87% on these two datasets, respectively.

Interactive Multifactorial Evolutionary Optimization Algorithm with Multidimensional Preference Surrogate Models for Personalized Recommendation

Interactive evolutionary algorithms (IEAs) coupled with a data-driven user surrogate model (USM) have recently been proposed for enhancing personalized recommendation performance. Since the USM relies on only one model to describe the full range of user preferences, existing USM-based IEAs have not investigated how knowledge migrates between preference models to improve the diversity and novelty of recommendations. Motivated by this, an interactive multifactorial evolutionary optimization algorithm with multidimensional preference user surrogate models is proposed here to perform a multi-view optimization for personalized recommendation. Firstly, multidimensional preference user surrogate models (MPUSMs), partial-MPUSMs, and probability models of MPUSMs are constructed to approximate the different perceptions of preferences and serve for population evolution. Next, a modified multifactorial evolutionary algorithm is used for the first time in the IEAs domain to recommend diverse and novel items for multiple preferences. It includes initialization and diversification management of a population with skill factors, recommendation lists of preference grading and interactive model management of inheriting previous information. Comprehensive comparison studies in the Amazon dataset show that the proposed models and algorithm facilitate the mining of knowledge between preferences. Eventually, at the cost of losing only about 5% of the Hit Ratio and Average Precision, the Individual Diversity is improved by 54.02%, the Self-system Diversity by 3.7%, the Surprise Degree by 2.69%, and the Preference Mining Degree by 16.05%.

A Deceptive Reviews Detection Method Based on Multidimensional Feature Construction and Ensemble Feature Selection

Deceptive reviews on social media and e-commerce websites are inflammatory and will significantly affect the judgment and purchase behavior of other users. At present, many researchers build models based on single text features to detect deceptive reviews. However, deceptive reviewers will deliberately imitate the text style of true reviews when writing reviews. At this time, these methods based on text features are not necessarily effective. What’s more, detection performance is limited because the category distribution is likely to be unbalanced in practice. In this work, to address these shortcomings, a deceptive review detection method based on multidimensional feature construction and ensemble feature selection is proposed. Our proposal constructs 3-D features including text feature, reviewer behavior feature, and deceptive score feature. In addition, to alleviate the impact of unbalanced category distribution, a data resampling algorithm is applied which incorporates random under-sampling (RUS) and Borderline-SMOTE algorithm. Furthermore, we integrate the results of different feature selection based on the Chi-square test, Information gain, and XGBoost feature importance. Our method addresses the limitation of single dimension features and can provide useful detection. Experimental results area under the curve (AUC, Macro Average Precision, and weighted F1-score) show that the proposed method performs well in the task of deceptive review detection on two Amazon datasets. Compared with other advanced methods, our method achieves additional performance gains in the case of poor text quality and datasets with unbalanced category distribution.

What Is the Internet Water Army? A Practical Feature-Based Detection of Large-Scale Fake Reviews

Information is passed by word-of-mouth figures prominently when consumers evaluate products through reviews. However, severe logistical problems are caused by the internet’s Water Army (i.e., literally people who are hired by individuals or organizations to compose false reviews), that flood the internet e-commerce websites. An array of internet e-commerce sites is flooded with inauthentic information, and false reviews are used to maliciously induce consumers to purchase specific products, that often contain some defects. Notwithstanding the fact that the internet Water Army first manifested in China, it can also exist in other countries. The rationale lies in the high profitability possible, in the minds of numerous organized underground paid poster groups, and in writing fake reviews to misinform consumers. It has become an increasingly daunting task to precisely spot the Water Army members, who often alter their writing style and posted content. In this paper, the authors devise a comprehensive set of features to characterize all users and compare the paid posters against the normal users on different dimensions; furthermore, an ensemble detection model equipped with seven disparate algorithms is put into place. Our model reached a score of 0.730 in the AUC measure, 0.691 in the F1 measure on the JD dataset, 0.926 in the AUC measure, and 0.871 in the F1 measure on the Amazon dataset, which outshines the measures in the existing research. The significance and contribution of this work are in advancing constructive solutions and recommendations for this major concern of the entire e-commerce industry.

Text Sentiment Classification Based on BERT Embedding and Sliced Multi-Head Self-Attention Bi-GRU.

In the task of text sentiment analysis, the main problem that we face is that the traditional word vectors represent lack of polysemy, the Recurrent Neural Network cannot be trained in parallel, and the classification accuracy is not high. We propose a sentiment classification model based on the proposed Sliced Bidirectional Gated Recurrent Unit (Sliced Bi-GRU), Multi-head Self-Attention mechanism, and Bidirectional Encoder Representations from Transformers embedding. First, the word vector representation obtained by the BERT pre-trained language model is used as the embedding layer of the neural network. Then the input sequence is sliced into subsequences of equal length. And the Bi-sequence Gated Recurrent Unit is applied to extract the subsequent feature information. The relationship between words is learned sequentially via the Multi-head Self-attention mechanism. Finally, the emotional tendency of the text is output by the Softmax function. Experiments show that the classification accuracy of this model on the Yelp 2015 dataset and the Amazon dataset is 74.37% and 62.57%, respectively. And the training speed of the model is better than most existing models, which verifies the effectiveness of the model.

LoMar: A Local Defense Against Poisoning Attack on Federated Learning

Federated learning (FL) provides a high efficient decentralized machine learning framework, where the training data remains distributed at remote clients in a network. Though FL enables a privacy-preserving mobile edge computing framework using IoT devices, recent studies have shown that this approach is susceptible to poisoning attacks from the side of remote clients. To address the poisoning attacks on FL, we provide a <i>two-phase</i> defense algorithm called <inline-formula><tex-math notation="LaTeX">${\underline{Lo}cal\ \underline{Ma}licious\ Facto\underline{r}}$</tex-math></inline-formula> (LoMar). In phase I, LoMar scores model updates from each remote client by measuring the relative distribution over their neighbors using a kernel density estimation method. In phase II, an optimal threshold is approximated to distinguish malicious and clean updates from a statistical perspective. Comprehensive experiments on four real-world datasets have been conducted, and the experimental results show that our defense strategy can effectively protect the FL system. Specifically, the defense performance on Amazon dataset under a label-flipping attack indicates that, compared with FG+Krum, LoMar increases the target label testing accuracy from <inline-formula><tex-math notation="LaTeX">$96.0\%$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$98.8\%$</tex-math></inline-formula> , and the overall averaged testing accuracy from <inline-formula><tex-math notation="LaTeX">$90.1\%$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$97.0\%$</tex-math></inline-formula> .

Deep Learning Hybrid Approaches to Detect Fake Reviews and Ratings

Nowadays, online reviews and ratings are the most valuable source of word-of-mouth, voice-of-customer, and feedback, also customers can make purchasing decisions on what to buy, where to buy, and what to select. Genuine online reviews are becoming popular, but unfortunately, we have an issue that might only sometimes be unbiased or accurate. Because most of the reviews are fake reviews and ratings, these could mislead innocent customers and highly influence customers' purchasing decisions in the wrong manner. This paper's primary goal is to accurately detect fake reviews and what is the main difference between them. The secondary goal is to detect fake ratings and actual ratings-based reviews across the online platform, especially Amazon datasets. The Paper proposes two novel deep-learning Hybrid techniques: CNN-LSTM for detecting fake online reviews, and LSTM-RNN for detecting fake ratings in the e-commerce domain. Both Hybrid models can outperform and achieve better performance with the most advanced word embedding techniques, Glove, and One hot encoding techniques. As per the experimental results, the first technique efficiently detects fake online reviews with the highest prediction accuracy. The second hybrid model is better than the existing models that detect fake online ratings with the most excellent precision of 93.8%. The experimental research efficiently revealed that the CNN-LSTM and LSTM-RNN methods are more efficient and practicable and might be better suited for optimal results and maximizing the efficiency of fake online review detection .

Recommendation System Based on Deep Sentiment Analysis and Matrix Factorization

In order to solve the problem of data sparsity and credibility in collaborative filtering, a recommendation system based on sentiment analysis and matrix factorization (SAMF) is proposed in this paper, which uses topic model and deep learning technology to fully mine the implicit information in reviews to improve the rating matrix and assist recommendation. Firstly, user topic distribution and item topic distribution are generated from reviews(consisting user reviews and item reviews) through LDA(Latent Dirichlet Allocation). The user feature matrix and item feature matrix are created based on topic probability. Secondly, user feature matrix and item feature matrix are integrated to create user-item preference matrix. Thirdly, the user-item preference matrix and the original rating matrix are integrated to create the user-item rating matrix. Fourthly, BERT(Bidirectional Encoder Representation from Transformers) is used to quantify the sentiment information contained in the reviews and integrate the sentiment information with the user-item rating matrix, to modify and update the user-item rating matrix. Finally, the updated user-item rating matrix is used to achieve rating prediction and Top-N recommendation. Experiments on Amazon datasets demonstrates that the proposed SAMF has better recommendation performance than other classical algorithms.

Towards improving e-commerce customer review analysis for sentiment detection

According to a report published by Business Wire, the market value of e-commerce reached US$ 13 trillion and is expected to reach US$ 55.6 trillion by 2027. In this rapidly growing market, product and service reviews can influence our purchasing decisions. It is challenging to manually evaluate reviews to make decisions and examine business models. However, users can examine and automate this process with Natural Language Processing (NLP). NLP is a well-known technique for evaluating and extracting information from written or audible texts. NLP research investigates the social architecture of societies. This article analyses the Amazon dataset using various combinations of voice components and deep learning. The suggested module focuses on identifying sentences as ‘Positive‘, ‘Neutral‘, ‘Negative‘, or ‘Indifferent‘. It analyses the data and labels the ‘better’ and ‘worse’ assumptions as positive and negative, respectively. With the expansion of the internet and e-commerce websites over the past decade, consumers now have a vast selection of products within the same domain, and NLP plays a vital part in classifying products based on evaluations. It is possible to predict sponsored and unpaid reviews using NLP with Machine Learning. This article examined various Machine Learning algorithms for predicting the sentiment of e-commerce website reviews. The automation achieves a maximum validation accuracy of 79.83% when using Fast Text as word embedding and the Multi-channel Convolution Neural Network.

Improved DeepFM Recommendation Algorithm Incorporating Deep Feature Extraction

In recent years, deep learning has been applied to the field of recommendation, which can learn complex user interaction features and make better recommendations. However, deep learning only focuses on the interaction of high-order features and neglects the low-order features. The DeepFM model combines the linear FM (Factorization Machines) model and the deep DNN (Deep Neural Network) model to realize the interactive learning of low-order and high-order features, but it does not take into account that user interests will change dynamically with time. When the data sparsity is high, it cannot be effectively recommended. Based on this, an improved DeepFM recommendation algorithm that combines depth feature extraction was proposed, named fDeepFM. Firstly, the word features are transformed into low-dimensional dense vectors through the Embedding layer. Then Doc2Vec is combined to mine item features with context, and the two are stitched together as the input to the FM model and DNN model. Subsequently, user features are input to the GRU (Gated Cyclic Unit) model according to different cycles to mine user features. Finally, the results of the FM model, DNN model, and GRU model are combined by linear stitching as the overall output of the fDeepFM model. Experiments were carried out on Movielens-20M and Amazon data sets. The experimental results showed that MAE, RMSE, F1-score, and AUC on the Movielens-20M data set were optimized by 1.69%, 2.4%, 1.67%, and 2.28%, respectively; On the Amazon dataset, MAE, RMSE, F1-score, and AUC are optimized by 3.2%, 3.86%, 1.63%, and 2.2% respectively compared with DeepFM.

Computational Intelligence Based Recurrent Neural Network for Identification Deceptive Review in the E-Commerce Domain.

Most consumers depend on online reviews posted on e-commerce websites when determining whether or not to buy a service or a product. Moreover, due to the presence of fraudulent (deceptive) reviews, the fundamental problem in such reviews is not fully addressed. Thus, deceptive reviews present wrong and misguiding opinions that are harmful to consumers and e-commerce. People called fraudsters who intentionally write deceptive reviews to target and deceive potential consumers, as they target businesses that have a well-built reputation or fame for their personal promotion, create such reviews. Therefore, developing a deceptive review detection system is essential for identifying and classifying online product reviews as truthful or fake/deceptive reviews. The main objective of this research work is to analyze and identify online deceptive reviews in electronic product reviews in the Amazon and Yelp domains. For this purpose, two experiments were conducted individually. The first was executed on standard Yelp product reviews. The second was performed on Amazon product review datasets. For this dataset, we created and labeled it using a deceptiveness score calculated based on features extracted from the review text using the linguistic inquiry and word count (LIWC) tool. These features were authenticity, negative words, comparing words negation words, analytical thinking, and positive words as well as the given rating value by a user. The recurrent neural network, bidirectional long short-term memory (RNN-BLSTM) model, was used to both datasets in order to conduct the evaluation. The application of this model was contingent upon the learning of words embedding of the review text. Finally, we evaluated the RNN-BLSTM model's performance using the Yelp and Amazon datasets and compared the results. The results were 89.6% regarding testing accuracy for both datasets. From our experimental results, we observed that the LIWC feature with word embedding in the review text provided better accuracy performance compared with other existing methods.

Improving graph prototypical network using active learning

Due to the growth of using various devices and applications in modern life, the amount of data available is skyrocketing, but labeling all of this data is beyond the reach of data scientists. Thus, it is necessary to categorize data with a small amount of labeled data. In fact, it should be possible to prioritize data for labeling. To achieve this goal in this study, we have used few-shot learning with active learning and also used the power of graph convolutional networks in classifying data with a graphical structure. To implement the proposed model, we use two graph convolutional networks in parallel to calculate the embedding and the importance of each node. Using the output of both networks, we create prototypes of classes, and then, we classify them according to the distance of each node of these prototypes. We have also used active learning to select data more intelligently, which improves the overall model performance. As well as this, we have tested our proposed model in the field of electronic commerce for tagging goods in big online stores, which encounter a large number of diverse products, where high accuracy categorization in a short time without the interference of human factor and with the help of artificial intelligence is needed to reduce costs. The results of implementing the model on the Amazon dataset and its comparison with the state-of-the-art models in this field show the superiority of our method.

Multisource Heterogeneous User-Generated Contents-Driven Interactive Estimation of Distribution Algorithms for Personalized Search

Personalized search is essentially a complex qualitative optimization problem, and interactive evolutionary algorithms (EAs) have been extended from EAs to adapt to solving it. However, the multisource user-generated contents (UGCs) in the personalized services have not been concerned on in the adaptation. Accordingly, we here present an enhanced restricted Boltzmann machine (RBM)-driven interactive estimation of distribution algorithms (IEDAs) with multisource heterogeneous data from the viewpoint of effectively extracting users’ preferences and requirements from UGCs to strengthen the performance of IEDA for personalized search. The multisource heterogeneous UGCs, including users’ ratings and reviews, items’ category tags, social networks, and other available information, are sufficiently collected and represented to construct an RBM-based model to extract users’ comprehensive preferences. With this RBM, the probability model for conducting the reproduction operator of estimation of distribution algorithms (EDAs) and the surrogate for quantitatively evaluating an individual (item) fitness are further developed to enhance the EDA-based personalized search. The UGCs-driven IEDA is applied to various publicly released Amazon datasets, e.g., recommendation of Digital Music, Apps for Android, Movies, and TV, to experimentally demonstrate its performance in efficiently improving the IEDA in personalized search with less interactions and higher satisfaction.

Singular value decomposition model application for e-commerce recommendation system

A recommendation system is one of the most important things in today’s technology. It can suggest products that match the user’s preferences. Many fields utilize this system, including e-commerce, using various algorithms. This paper used the matrix factorization-based algorithm, singular value decomposition (SVD), to make a recommendation system based on users’ similarities. Afterward, we implement the model against the ModCloth Amazon dataset. The results imply that the SVD algorithm yields the best accuracy compared to other matrix factorization-based algorithms with root mean square error (RMSE) of 1.055586. Then, we optimized the SVD algorithm by changing the hyperparameters of the algorithm to generate better accuracy and yield a model with an RMSE value of 1.041784.

An Improved African Vulture Optimization Algorithm for Feature Selection Problems and Its Application of Sentiment Analysis on Movie Reviews

The African Vulture Optimization Algorithm (AVOA) is inspired by African vultures’ feeding and orienting behaviors. It comprises powerful operators while maintaining the balance of exploration and efficiency in solving optimization problems. To be used in discrete applications, this algorithm needs to be discretized. This paper introduces two versions based on the S-shaped and V-shaped transfer functions of AVOA and BAOVAH. Moreover, the increase in computational complexity is avoided. Disruption operator and Bitwise strategy have also been used to maximize this model’s performance. A multi-strategy version of the AVOA called BAVOA-v1 is presented. In the proposed approach, i.e., BAVOA-v1, different strategies such as IPRS, mutation neighborhood search strategy (MNSS) (balance between exploration and exploitation), multi-parent crossover (increasing exploitation), and Bitwise (increasing diversity and exploration) are used to provide solutions with greater variety and to assure the quality of solutions. The proposed methods are evaluated on 30 UCI datasets with different dimensions. The simulation results showed that the proposed BAOVAH algorithm performed better than other binary meta-heuristic algorithms. So that the proposed BAOVAH algorithm set is the most accurate in 67% of the data set, and 93% of the data set is the best value of the fitness functions. In terms of feature selection, it has shown high performance. Finally, the proposed method in a case study to determine the number of neurons and the activator function to improve deep learning results was used in the sentiment analysis of movie viewers. In this paper, the CNNEM model is designed. The results of experiments on three datasets of sentiment analysis—IMDB, Amazon, and Yelp—show that the BAOVAH algorithm increases the accuracy of the CNNEM network in the IMDB dataset by 6%, the Amazon dataset by 33%, and the Yelp dataset by 30%.

A deep matching model for detecting reviews mismatched with products in e-commerce

Reviews of products in e-commerce are great value to both firms and customers. However, these reviews are not always genuine. Fake reviews can stymie firms and customers. Many studies have focused on detecting fake reviews. This paper addresses another kind of fake review on e-commerce websites called mismatched reviews where the content of reviews seems to be genuine, but they actually do not match the reviewed products. These reviews can harm the user experience in online shopping and damage the reputation of e-commerce websites. This paper develops a deep matching network to detect mismatched reviews, called MIRD, to calculate matching scores between the content of reviews and the reviewed products. MIRD encodes both reviews and products to the representation vectors and subsequently calculates a score in a latent space to measure how well the two vectors match. Experiments conducted on Yelp and Amazon datasets show that MIRD can recognize mismatched reviews well. Compared with the baseline models, MIRD obtains an 8% performance improvement on the two datasets. The experimental results demonstrate that our efforts to detect mismatched reviews are feasible and effective.

Detecting shilling groups in online recommender systems based on graph convolutional network

Online recommender systems have been shown to be vulnerable to group shilling attacks in which attackers of a shilling group collaboratively inject fake profiles with the aim of increasing or decreasing the frequency that particular items are recommended. Existing detection methods mainly use the frequent itemset (dense subgraph) mining or clustering method to generate candidate groups and then utilize the hand-crafted features to identify shilling groups. However, such two-stage detection methods have two limitations. On the one hand, due to the sensitivity of support threshold or clustering parameters setting, it is difficult to guarantee the quality of candidate groups generated. On the other hand, they all rely on manual feature engineering to extract detection features, which is costly and time-consuming. To address these two limitations, we present a shilling group detection method based on graph convolutional network. First, we model the given dataset as a graph by treating users as nodes and co-rating relations between users as edges. By assigning edge weights and filtering normal user relations, we obtain the suspicious user relation graph. Second, we use principal component analysis to refine the rating features of users and obtain the user feature matrix. Third, we design a three-layer graph convolutional network model with a neighbor filtering mechanism and perform user classification by combining both structure and rating features of users. Finally, we detect shilling groups through identifying target items rated by the attackers according to the user classification results. Extensive experiments show that the classification accuracy and detection performance (F1-measure) of the proposed method can reach 98.92% and 99.92% on the Netflix dataset and 93.18% and 92.41% on the Amazon dataset.

DETECTION AND MONITORING MULTIPLE SHILLING ATTACK IN ONLINE MANAGEMENT SYSTEMS PREDICATED ON BISECTING K-MEANS CLUSTRING ALGORITHM

While existing methods for detecting shillings attacks in online recommendation system are efficient in detecting individuals’ offenders, they are not as effective at detecting group shilling operations. Using the bisecting K-means clustering technique, we offer a method for detecting coordinated shilling attacks. To begin, we take the ratings for each item and split them into groups based on a predetermined amount of time. Second, we suggest using the proportion of product concentration and usage data to determine the degree of suspicion around potential groupings. Research performed on the Netflix and Amazon data sets validate the superiority of the suggested strategy over the gold standard techniques. KEYWORDS: ORS, bisecting K-means clustering technique, shilling attacks, dataset