User-based Recommender System Research Articles

Shilling Attack Detection in User Based Recommendation System

The majority of the existing unsupervised methods for detecting shilling attacks are based on user rating patterns, ignoring the differences in rating behavior between legitimate users and attack users. These methods have low accuracy in detecting different shilling attacks without having any prior knowledge of the attack types. We provide a novel unsupervised shilling assault detection technique based on an examination of user rating behavior in order to overcome these constraints. By first examining the deviation of rating tendencies on each item, we are able to determine the target item(s) and the accompanying goals of the attack users. Based on the results of this study, a group of suspicious users is then created. Second, we examine the users' rating behaviors in terms of their rating and interest preferences. Finally, using measurements of user rating behavior, we determine the suspicious degree and identify attack users within the collection of suspicious users. The Movie Lens 1M dataset, the sampled Amazon review dataset, and the Netflix dataset all show how good the suggested detection model.

Recommender System Based on Tweets with Singular Value Decomposition and Support Vector Machine Classification

In modern times, the movie industry is growing rapidly. Netflix is one of the platforms that can be used to watch movies and provides many types of genres and movie titles. With so many genres and movie titles sometimes making it difficult for people to choose a movie to watch, one solution to the problem is a recommendation system that can recommend movies based on user ratings. One method in the recommendation system is collaborative filtering. One of the algorithms contained in collaborative filtering is singular value decomposition. Twitter is one of the places where people often write their opinions about the movies they have watched, from people's tweets on Twitter will be processed into rating value data. In this system, tweets become input that is processed into data that has a rating. This research implements a user-based recommendation system based on ratings from tweets using collaborative filtering combined with the Singular Value Decomposition (SVD) algorithm and Support Vector Machine (SVM) classification and implemented it on user-based and item-based. This research aims to implement a system that combines collaborative filtering techniques with the Singular Value Decomposition (SVD) algorithm and Support Vector Machine (SVM) classification. With the hope of producing a good movie recommendation model and providing accurate predictions for recommended and non-recommended movies. The test results in this study show that Collaborative Filtering gets the best RMSE value of 0.8162 on user-based and 0.5911 on item-based. The combination of Singular Value Decomposition (SVD) algorithm and Support Vector Machine (SVM) classification using hyperparameter tuning resulted in 81% precision and 81% recall for user-based while 80% precision and 80% recall for item-based.

E-Commerce Personalized Recommendation Based on Machine Learning Technology

As e-commerce offers more and more choices for users, its structure becomes more and more complicated. Inevitably, it brings about the problem of information overload. The solution to this problem is an e-commerce personalized recommendation system using machine learning technology. People often seem confused when facing extensive information and cannot grasp the key points. This paper studies the personalized recommendation technology of e-commerce: deeply analyzes the related technologies and algorithms of the e-commerce recommendation system and proposes the latest architecture of the e-commerce recommendation system according to the current development status of the e-commerce recommendation system. The system recommends accuracy and real-time requirements and divides the system into two parts: offline mining and online recommendation and analyzes and implements the functions and technologies of each part. User-based recommender systems, collaborative filtering recommender systems, and content-based recommender systems are analyzed, respectively. The personalized recommendation cannot only quickly help customers find the required commodity information in a wide range of complex information but also can compare more commodity information to help customers to judge. However, the existing recommendation system has some problems such as the lack of recommendation personality, the reduced relevance of recommendation, and the poor timeliness of recommendation. Finally, a recommendation system that combines three recommendation algorithms is designed, and experiments are carried out. The newly designed recommendation system is compared with three different recommendation systems, and a summary and outlook are made. Based on the introduction of the relevant theories, characteristics, and mainstream technologies of personalized recommendation based on machine learning, this document presents a constructive example of a model based on the factors that influence personalized e-commerce information recommendations in the retail sector. Through questionnaire surveys, we analyze and design the influencing factors for consumers to purchase personalized products after the survey and build a project using state-of-the-art field learning techniques. Through the model to test the eight hypotheses proposed in this paper, the results show that customer income level, customer online shopping experience, commodity prices, product quality, recommendation relevance, credit evaluation, and service quality will have a significant positive impact on shopping willingness and ultimately affect the customer’s shopping behavior. e-commerce platform can use this influencing factor to establish personalized information recommendation service mode.

Novel implicit-trust-network-based recommendation methodology

Recommender systems are known for helping e-commerce and entertainment sits to provide excellent services into their clients by finding those interests in a short time as possible. Recently, researchers have studied on trust relationships of people for improving user-based recommender systems. Hence, this study proposed an implicit approach for constructing a trust-network, which contains two parts. i. constructing an incipient trust-network by examining Similarity, Confidence and Identical Opinion of users; ii. Reconstructing the incipient trust-network as a telic trust-network by appraising its precision for predicting unknown-items rating. The study also presented methods for determining each of the similarity, the confidence, and the identical opinion. Furthermore, this study presented a recommendation strategy by merging two ways of ranking items based on their predicted rating and users' score to them, considering the trust-network. The study was evaluated on datasets of Ciao and FilmTrust for the trust-network correctness, the predicted-ratings error, and the quality of the recommendations. The results have shown, the scheme of this study has significantly improved the performance compared to some state-of-the-art techniques.

Personalized Book Recommendation System using Machine Learning Algorithm

As the amounts of online books are exponentially increasing due to COVID-19 pandemic, finding relevant books from a vast e-book space becomes a tremendous challenge for online users. Personal recommendation systems have been emerged to conduct effective search which mine related books based on user rating and interest. Most of these existing systems are user-based ratings where content-based and collaborative-based learning methods are used. These systems' irrationality is their rating technique, which counts the users who have already been unsubscribed from the services and no longer rate books. This paper proposed an effective system for recommending books for online users that rated a book using the clustering method and then found a similarity of that book to suggest a new book. The proposed system used the K-means Cosine Distance function to measure distance and Cosine Similarity function to find Similarity between the book clusters. Sensitivity, Specificity, and F Score were calculated for ten different datasets. The average Specificity was higher than sensitivity, which means that the classifier could re-move boring books from the reader's list. Besides, a receiver operating characteristic curve was plotted to find a graphical view of the classifiers' accuracy. Most of the datasets were close to the ideal diagonal classifier line and far from the worst classifier line. The result concludes that recommendations, based on a particular book, are more accurately effective than a user-based recommendation system.

A collaborative filtering recommendation algorithm based on information theory and bi-clustering

Collaborative filtering is the most popular and efficient recommendation algorithm to character the potential preference of the new users, by exploring the patterns of historical consuming records/ratings of the investigated users. There are two types of primary collaborative filtering algorithms: the user-based recommendation system, which recommends items to new users by ranking the similarity of the shared items between the history users and the new users, and the item-based collaborative filtering recommend items to new users by considering the rank of the similarity among all the history items of the training data. Although the collaborative filtering has been successfully applied to many commercial fields, several original drawbacks of collaborative filtering, especially the sparsity of the rating data raises a serious challenge to the accuracy and the universality of those algorithms. In particular, the most rating terms for each specific user are missing in many applications, and the performance of collaborative filtering will be degraded along with the increment of the number of items in training dataset. In this paper, we proposed a novel collaborative filtering method (CBE-CF) to extract the local dense rating modules to cope with the data sparsity and the computational efficiency of the traditional recommendation algorithms, by introducing the information entropy and bi-clustering into collaborative filtering. Here, both the rows and columns of the user-item-rating matrix are clustered together to identify the dense rating modules of the historical records (training) data, and then an information entropy metric is used to quantify the similarity between the new user and each dense modules, and the final prediction is optimized by the aggregative recommendations of the global generalization of item-based methods and the local similarity of the nearest modules. Experimental analysis presents the characters of the proposed CBE-CF, and the precision and the computational cost, etc., are better than state of the art on the benchmark dataset.

Group recommender system for store product placement

We propose a group recommender system considering the recommendation quantity and repeat purchasing by using the existing collaborative filtering algorithm in order to optimize the offline physical store inventories. This research is the first of its kind to consider recommendation quantity and repetitive recommendations when creating group recommender systems. In offline stores, physical limitations result in the ability to display only a limited number of items. Quantity and selection of the item is an important decision for offline stores. In this paper, we suggest applying the user-based recommender system, which is capable of determining the best suited recommendation items for each store. This model is evaluated by the MAE, precision, recall, and F1 measures, and shows higher performance than the baseline model. A new performance evaluation measure is also suggested in this research. New quantity precision, quantity recall, and quantity F1 measures consider a penalty for a shortage or excess of the recommendation quantity. Novelty is defined as the proportion of items that the consumer may not have experienced in the recommendation list. Through the use of this novelty measure, we assess the new profit creation effect of the suggested model. Finally, previous research focused on recommendations for online customers, however, we expanded the recommender system to incorporate offline stores. This research is not only an academic contribution to the marketing field, but also practical contribution to offline stores through the usability of a developed offline shopping algorithm.

Solving Cold-Start Problem by Combining Personality Traits and Demographic Attributes in a User Based Recommender System

Solving Cold-Start Problem by Combining Personality Traits and Demographic Attributes in a User Based Recommender System

User based Recommender Systems using Implicative Rating Measure

This paper proposes the implicative rating measure developed on the typicality measure. The paper also proposes a new recommendation model presenting the top N items to the active users. The proposed model is based on the user-based collaborative filtering approach using the implicative intensity measure to find the nearest neighbors of the active users, and the proposed measure to predict users’ ratings for items. The model is evaluated on two datasets MovieLens and CourseRegistration, and compared to some existing models such as: the item based collaborative filtering model using the Jaccard measure, the user based collaborative filtering model using Jaccard measure, the popular items based model, the latent factor based model, and the association rule based model using the confidence measure. The experimental results show that the performance of the proposed model is better when compared to other five models.

Survey on Item Based and User Based Recommendation System in Cloud

Survey on Item Based and User Based Recommendation System in Cloud