Online evaluation of recommender system with MovieLens dataset

The purpose of this paper is to explore the advantages of recommender systems based on the matrix factorization in respect to classical first neighbor recommender systems to real users through A/B test, as these studies are more significant. The results presented in this paper confirms the hypothesis that the recommender systems based on the models of matrix factorization are superior in relation to classical nearest-neighbor recommender systems.

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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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A Hybrid Recommender System Based on User-Recommender Interaction

Mathematical Problems in Engineering ◽

10.1155/2015/145636 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 6

Author(s):

Heng-Ru Zhang ◽

Fan Min ◽

Xu He ◽

Yuan-Yuan Xu

Keyword(s):

Recommender Systems ◽

Recommender System ◽

Hybrid Algorithm ◽

Nearest Neighbor ◽

User Behavior ◽

User Choice ◽

User Request ◽

Hybrid Recommender System ◽

Hybrid Recommender ◽

Diversity Metrics

Recommender systems are used to make recommendations about products, information, or services for users. Most existing recommender systems implicitly assume one particular type of user behavior. However, they seldom consider user-recommender interactive scenarios in real-world environments. In this paper, we propose a hybrid recommender system based on user-recommender interaction and evaluate its performance with recall and diversity metrics. First, we define the user-recommender interaction. The recommender system accepts user request, recommendsNitems to the user, and records user choice. If some of these items favor the user, she will select one to browse and continue to use recommender system, until none of the recommended items favors her. Second, we propose a hybrid recommender system combining random andk-nearest neighbor algorithms. Third, we redefine the recall and diversity metrics based on the new scenario to evaluate the recommender system. Experiments results on the well-known MovieLens dataset show that the hybrid algorithm is more effective than nonhybrid ones.

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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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Collaborative filtering-based recommendation of online social voting

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.11630 ◽

2018 ◽

Vol 7 (3) ◽

pp. 1504 ◽

Cited By ~ 5

Author(s):

Dr Mohammed Ismail ◽

Dr K. Bhanu Prakash ◽

Dr M. Nagabhushana Rao

Keyword(s):

Recommender Systems ◽

Matrix Factorization ◽

Recommendation System ◽

Nearest Neighbor ◽

Social Trust ◽

Social Recommendation ◽

Peer Reviewers ◽

Hybrid Recommender System ◽

Hybrid Recommender ◽

Cold Start Problem

Social voting is becoming the new reason behind social recommendation these days. It helps in providing accurate recommendations with the help of factors like social trust etc. Here we propose Matrix factorization (MF) and nearest neighbor-based recommender systems accommodating the factors of user activities and also compared them with the peer reviewers, to provide a accurate recommendation. Through experiments we realized that the affiliation factors are very much needed for improving the accuracy of the recommender systems. This information helps us to overcome the cold start problem of the recommendation system and also y the analysis this information was much useful to cold users than to heavy users. In our experiments simple neighborhood model outperform the computerized matrix factorization models in the hot voting and non hot voting recommendation. We also proposed a hybrid recommender system producing a top-k recommendation inculcating different single approaches.

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Word Sequential Using Deep LSTM and Matrix Factorization to Handle Rating Sparse Data for E-Commerce Recommender System

Computational Intelligence and Neuroscience ◽

10.1155/2021/8751173 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Hanafi ◽

Burhanuddin Mohd Aboobaider

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Recommender System ◽

Matrix Factorization ◽

Factor Model ◽

Sparse Data ◽

Product Review ◽

Latent Factor ◽

Latent Factor Model ◽

Future Work

Recommender systems are essential engines to deliver product recommendations for e-commerce businesses. Successful adoption of recommender systems could significantly influence the growth of marketing targets. Collaborative filtering is a type of recommender system model that uses customers’ activities in the past, such as ratings. Unfortunately, the number of ratings collected from customers is sparse, amounting to less than 4%. The latent factor model is a kind of collaborative filtering that involves matrix factorization to generate rating predictions. However, using only matrix factorization would result in an inaccurate recommendation. Several models include product review documents to increase the effectiveness of their rating prediction. Most of them use methods such as TF-IDF and LDA to interpret product review documents. However, traditional models such as LDA and TF-IDF face some shortcomings, in that they show a less contextual understanding of the document. This research integrated matrix factorization and novel models to interpret and understand product review documents using LSTM and word embedding. According to the experiment report, this model significantly outperformed the traditional latent factor model by more than 16% on an average and achieved 1% on an average based on RMSE evaluation metrics, compared to the previous best performance. Contextual insight of the product review document is an important aspect to improve performance in a sparse rating matrix. In the future work, generating contextual insight using bidirectional word sequential is required to increase the performance of e-commerce recommender systems with sparse data issues.

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Detect User’s Rating Characteristics by Separate Scores for Matrix Factorization Technique

Symmetry ◽

10.3390/sym10110616 ◽

2018 ◽

Vol 10 (11) ◽

pp. 616 ◽

Cited By ~ 2

Author(s):

Jia Zhao ◽

Gang Sun

Keyword(s):

Recommender Systems ◽

Matrix Factorization ◽

Information Overload ◽

Bias Score ◽

Factorization Technique ◽

The Matrix ◽

Novel Method ◽

Open Datasets ◽

Accuracy Of Prediction ◽

Matrix Factorization Technique

A recommender system can effectively solve the problem of information overload in the era of big data. Recent research on recommender systems, specifically Collaborative Filtering, has focused on Matrix Factorization methods, which have been shown to have excellent performance. However, these methods do not pay attention to the influence of a user’s rating characteristics, which are especially important for the accuracy of prediction or recommendation. Therefore, in order to get better performance, we propose a novel method based on matrix factorization. We consider that the user’s rating score is composed of two parts: the real score, which is decided by the user’s preferences; and the bias score, which is decided by the user’s rating characteristics. We then analyze the user’s historical behavior to find his rating characteristics by using the matrix factorization technique and use them to adjust the final prediction results. Finally, by comparing with the latest algorithms on the open datasets, we verified that the proposed method can significantly improve the accuracy of recommender systems and achieve the best performance in terms of prediction accuracy criterion over other state-of-the-art methods.

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Analisis Perbandingan Model Matrix Factorization dan K-Nearest Neighbor dalam Mesin Rekomendasi Collaborative Berbasis Prediksi Rating

Jurnal Informatika Universitas Pamulang ◽

10.32493/informatika.v5i4.7379 ◽

2021 ◽

Vol 5 (4) ◽

pp. 506

Author(s):

Janny Eka Prayogo ◽

Aries Suharso ◽

Adhi Rizal

Keyword(s):

Collaborative Filtering ◽

Matrix Factorization ◽

Nearest Neighbor ◽

Prediction Models ◽

Mean Squared Error ◽

Absolute Error ◽

K Nearest Neighbor ◽

Filtering Method ◽

Factorization Model ◽

The Matrix

Rating is a form of assessment of the likes or dislikes of a user or customer for an item. Where the higher the rating number given, the item is preferred by customers or users. In the recommendation engine, a set of ratings can be predicted and used as an object to generate a recommendation by the Collaborative Filtering method. In the Collaborative Filtering method, there is a rating prediction model, namely the Matrix Factorization and K-Nearest Neighbor models. This study analyzes the comparison of the two prediction models based on the value of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and the prediction results generated using the movielens film rating dataset. From the analysis and testing results, it was found that MAE = 0.6371 and RMSE = 0.8305 for the Matrix Factorization model, while MAE = 0.6742 and RMSE = 0.8863 for the K-Nearest Neighbor model. The best model is Matrix Factorization because the MAE and RMSE values are lower than the K-Nearest Neighbor model and have the closest predicted rating results from the original rating value.

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Movie Recommendation through Multiple Bias Analysis

Applied Sciences ◽

10.3390/app11062817 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2817

Author(s):

Tae-Gyu Hwang ◽

Sung Kwon Kim

Keyword(s):

Decision Making ◽

Recommender System ◽

Real World ◽

Matrix Factorization ◽

User Preferences ◽

Improve Performance ◽

Bias Analysis ◽

Useful Knowledge ◽

Existing Problems ◽

Movie Recommendation

A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.

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A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

Sensors ◽

10.3390/s21155248 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5248

Author(s):

Aleksandra Pawlicka ◽

Marek Pawlicki ◽

Rafał Kozik ◽

Ryszard S. Choraś

Keyword(s):

Systematic Review ◽

Recommender Systems ◽

Recommender System ◽

State Of The Art ◽

The State ◽

Advantages And Disadvantages ◽

Comprehensive Survey ◽

Security Concerns ◽

Valuable Role

This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of the art concerning the use of recommender systems in cybersecurity; both the existing solutions and future ideas are presented. The contribution of this paper is two-fold: to date, to the best of our knowledge, there has been no work collecting the applications of recommenders for cybersecurity. Moreover, this paper attempts to complete a comprehensive survey of recommender types, after noticing that other works usually mention two–three types at once and neglect the others.

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