Movie Lens Research Articles

Multi-stakeholder recommendation system through deep learning-based preference evaluation and aggregation model with multi-view information embedding

Learning the preferences of consumers, providers, and system stakeholders is a challenging problem in the Multi-Stakeholder Recommendation System (MSRS). Existing MSRS methods lack the ability to generate equitable recommendations and investigate implicit relationships between stakeholders and items. Hence, this study addresses this issue by proposing a multi-stakeholder preference learning-based recommendation model that exploits information from multiple views to evaluate stakeholders' preferences. The proposed model learns consumer preferences using users' ratings and reviews of an item, provider preferences with provider utility, and provider-item interaction. Furthermore, the proposed model learns the system-level preference of promoting long-tail items through the probabilistic evaluation of stakeholders' interest in popular and unpopular items. Finally, this study develops a multi-stakeholder, multi-view deep neural network model to aggregate stakeholders' preferences and deliver equitable recommendations. This work utilizes benchmark Movie Lens (ML) 25M, ML-100K, ML-1M, and TripAdvisor datasets to validate and compare the proposed model's performance with other baseline methods using standard evaluation metrics for each stakeholder. Examining the precision metrics, the proposed model attains the minimum enhancement of 7.91%, 18.24%, 10.72%, and 20.12% across the ML-25M, ML-100K, ML-1M, and TripAdvisor datasets. Further concerning the exposure, hit, and reach metrics, the model exhibits a substantial minimum improvement of 19.12%, 14.73%, 5.37%, and 28.46% over the ML-25M, ML-100K, ML-1M, and TripAdvisor datasets. Finally, the proposed model excels in promoting long-tail items and enhancing the cumulative utility gain of the stakeholders, surpassing the baseline methods.

Design and Development of an Efficient Demographic-based Movie Recommender System using Hybrid Machine Learning Techniques

Movie Recommender systems are frequently used in academics and industry to give users with relevant, engaging material based on their rating history. However, most traditional methods suffer from the cold-start problem, which is the initial lack of item ratings and data sparsity. The Hybrid Machine Learning (ML) technique is proposed for a movie recommendation system. Demographic data is collected from the Movie Lens dataset, and attributes are evaluated using the Attribute Analysis module. The Aquila Optimization Algorithm is used to select the best attributes, while Random Forest classifier is used for classification. Data is clustered using Fuzzy Probabilistic Cmeans Clustering Algorithm (FPCCA), and the Correspondence Index Assessment Phase (CIAP) uses Bhattacharyya Coefficient in Collaborative Filtering (BCCF) for similarity index calculation. The Outcomes gives the proposed method obtained low error, such as MAE has 0.44, RMSE has 0.63 compared with the baseline methods.

Mitigating filter bubbles: Diverse and explainable recommender systems

In recent years, the surge in online content has necessitated the development of intelligent recommender systems capable of offering personalized suggestions to users. However, these systems often encapsulate users within a “filter bubble”, limiting their exposure to a narrow range of content. This study introduces a novel approach to address this issue by integrating a novel diversity module into a knowledge graph-based explainable recommender system. Utilizing the Movie Lens 1M dataset, this research pioneers in fostering a more nuanced and transparent user experience, thereby enhancing user trust and broadening the spectrum of recommendations. Looking ahead, we aim to further refine this system by incorporating an explicit feedback loop and leveraging Natural Language Processing (NLP) techniques to provide users with insightful explanations of recommendations, including a comprehensive analysis of filter bubbles. This initiative marks a significant stride towards creating a more inclusive and informed recommendation landscape, promising users not only a wider array of content but also a deeper understanding of the recommendation mechanisms at play.

Movie Recommendation System Using Optimized RNN Approach.

This paper proposes a movie recommendation system that utilizes an optimized Recurrent Neural Network (RNN) approach. The proposed system is designed to provide users with personalized movie recommendations based on their previous movie preferences and the sentiments. The system works by taking user input, analyzing their movie preferences using content-based filtering techniques, and generating a list of recommended movies. The RNN architecture used in this system is optimized using a combination of techniques such as dropout regularization, early stopping, and parameter tuning. The proposed optimization techniques aim to reduce overfitting, improve convergence speed, and increase the model's overall accuracy. To evaluate the effectiveness of the proposed approach, we conducted experiments on the Movie Lens dataset. The results indicate that the optimized RNN-based movie recommendation system outperforms other existing recommendation systems such as collaborative filtering, content-based filtering, and standard RNN models. Furthermore, the proposed system achieved a significant improvement in accuracy and provided highly personalized recommendations to users. Overall, the proposed movie recommendation system using optimized RNN approach is a promising solution for providing personalized movie recommendations to users. It can be implemented in various platforms such as movie streaming websites, social media, and other movie-related platforms to improve the user experience and increase engagement

Increasing the Effectiveness of Prediction in Recommendation Engines Based on Collaborative Filtering

In the era of information abundance, the demand for personalized content recommendations has become paramount. Recommendation engines, particularly those employing collaborative filtering, play a pivotal role in delivering tailored suggestions based on user preferences. As technology evolves, the need to enhance the effectiveness of prediction algorithms within these engines becomes increasingly crucial. This research endeavors to contribute to this evolving landscape by delving into collaborative filtering methodologies, identifying challenges, and proposing novel strategies to elevate the accuracy and relevance of predictions in recommendation systems. Through this exploration, we aim to not only refine existing models but also pave the way for more sophisticated and reliable personalized content recommendations.
 This research aims to enhance prediction accuracy in recommendation engines utilizing collaborative filtering. Through an in-depth exploration of collaborative filtering techniques, we propose innovative approaches to improve the effectiveness of predictions. Our study addresses key challenges in collaborative filtering models, offering insights into refined algorithms and methodologies. By fine-tuning the collaborative filtering process, we anticipate a substantial boost in the overall performance of recommendation engines, ultimately advancing the field of personalized content suggestion. The simulation is performed using Java language and using two datasets Movie Lens 1M and Movie Lens 100K.The proposed model was evaluated using the Mean Absolute Error, Precision, and Recall.
 The proposed model achieved a mean absolute error value ranging between 0.78 and 0.84 using the Movie Lens 100K dataset, and a mean absolute error value ranging between 0.72 and 0.74 using the Movie Lens 1M dataset for different values of the number of user groups. As for precision and recall, the precision of the proposed model ranged between 0.97 and 0.985 using the Movie Lens 100K data set, and a precision value ranging between 0.944 and 0.954 using the Movie Lens 1M data set, also for different values of the number of user groups.
 As for the recall results, the proposed model achieved a recall value ranging between 0.755 and 0.85 using the Movie Lens 100K dataset, and a recall value ranging between 0.72 and 0.75 using the Movie Lens 100K dataset, also for different values of the number of user groups. These results were compared with the PMF, HPF, and NMF algorithms, where the proposed model proved its clear superiority over these algorithms. Using this analysis of the matrix allows us to obtain a good prediction accuracy of users' preferences and to find common groups of people with similar preferences.

Hybrid CNN-based Recommendation System

Recommendation systems are now being used to address the problem of excess information in several sectors such as entertainment, social networking, and e-commerce. Although conventional methods to recommendation systems have achieved significant success in providing item suggestions, they still face many challenges, including the cold start problem and data sparsity. Numerous recommendation models have been created in order to address these difficulties. Nevertheless, including user or item-specific information has the potential to enhance the performance of recommendations. The ConvFM model is a novel convolutional neural network architecture that combines the capabilities of deep learning for feature extraction with the effectiveness of factorization machines for recommendation tasks. The present work introduces a novel hybrid deep factorization machine (FM) model, referred to as ConvFM. The ConvFM model use a combination of feature extraction and convolutional neural networks (CNNs) to extract features from both individuals and things, namely movies. Following this, the proposed model employs a methodology known as factorization machines, which use the FM algorithm. The focus of the CNN is on the extraction of features, which has resulted in a notable improvement in performance. In order to enhance the accuracy of predictions and address the challenges posed by sparsity, the proposed model incorporates both the extracted attributes and explicit interactions between items and users. This paper presents the experimental procedures and outcomes conducted on the Movie Lens dataset. In this discussion, we engage in an analysis of our research outcomes followed by provide recommendations for further action.

MOVIE RECOMMENDATION SYSTEM USING HYBRID FILTERING WITH WORD2VEC AND RESTRICTED BOLTZMANN MACHINES

Recommender systems are designed to provide interesting information to users and assist them in making choices. With the help of a recommender system, users can feel more comfortable using an application. In this final project, we will implement a hybrid filtering method using two techniques: Word2Vec as the algorithm for content-based filtering and Restricted Boltzmann Machine for collaborative filtering. The Word2Vec algorithm will utilize a pre-trained model provided by Google, while the Restricted Boltzmann Machine algorithm will utilize the TensorFlow library. The dataset used for this project will be Movie Lens. The goal of this final project is to evaluate the accuracy and performance of the recommender system using various metrics such as Precision and Normalized Discounted Cumulative Gain.

Movie Recommendation System: Real-time Evaluation

In today's e-commerce time, it has become very troublesome task to find any content of online movies, songs, books , restaurants etc. A practical recommendation scheme now very helpful and important. Nowadays, movie recommendation system are also available( Netflix, Movie lens, Amazon movie recommendation system etc.) But using a method of combination of content based and collaborative filtering, a recommender system worked and finding a customized output to user. Key Words: Content-based, Collaborative Filtering, Recommender System, Machine Learning.

Movies Rating Prediction using Supervised Machine Learning Techniques

Social media are the most enormous and fast data technology on the internet. A massive amount of data is generated from the internet day by day. The efficient processing of such massive records is hard, so we require a system that learns from these facts and makes a useful prediction like machine learning. Machine learning strategies make the systems learn it. Applying K-nearest neighbors (KNN), Support Vector Machine (SVM), as well as Random Forest Model (RF), three supervised machine learning approaches, we attempted to develop a model for a movie recommendation system. This research not only compares the three models but also assesses how they compare favorably against one another in the areas of accuracy, accuracy, recalls, and F1 score. First of all, we discuss a summary of machine learning techniques used for recommendations of items in the past. We subsequently detail the most effective machine learning model for making predictions and recommendations about movie ratings. Predictions of movie reviews are made using K-nearest neighbor (KNN) and Support Vector Machine (SVM) models as well as the Random Forest Model (RF). The efficacy of all models was tested with Movie Lens 100k and 1M dataset. The random forest Model performs better as compared to other models. Furthermore, we summarize the current challenges based on state of art approaches. Finally, we present open issues and future directions for further research.

Improved time-aware debiasing models for recommender systems

Recommender systems are affected by user selection bias during user interactions. Previously, Huang, Jin et al. proposed the TMF-DANCER model to deal with the fact that selection bias is dynamic, and the popularity of an item and user preferences may change drastically over time. However, the previous time-aware methods did not consider the continuity of time, and performing collaborative filtering with matrix factorization may not be able to capture the complex structure of user interaction data. Our methods enhance the effectiveness of time-aware debiasing by utilizing the time-varying sequential method to alleviate the discontinuity of time-based on the frequency-based method, Replacing the matrix factorization with neural collaborative filtering (NCF). We used movie lens 100k as the data for training. The experimental results indicate that methods incorporating the time-varying sequential method and NCF have better performance in rating prediction than debiasing methods without.

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.

Movie Recommendation System with Sentiment Analysis

A recommendation system is a system that, depending on certain data, makes suggestions to users for specific resources like books, movies, songs, etc. The characteristics of previously loved movies are typically used by movie recommendation systems to anticipate what movies a user would like. Such recommendation systems are advantageous for businesses that gather data from a lot of clients and want to successfully offer the finest recommendations. When creating a movie recommendation system, several variables may be taken into account, including the movie's genre, cast, and even director. The algorithms are capable of recommending movies based on a single attribute or a combination of two or more. The recommendation algorithm in this study is based on the kinds of genres that the user would want to watch. The method used to do this is content-based filtering with genre relevance. Movie Lens set of data is the one processed by the system. R is the data analysis programmed utilized.

Clustering Techniques for Recommendation of Movies

A recommendation system employs a variety of algorithms to provide users with recommendations of any kind. The most well-known technique, collaborative filtering, involves users with similar preferences although it is not always as effective when dealing with large amounts of data. Improvements to this approach are required as the dataset size increases. Here, in our suggested method, we combine a hierarchical clustering methodology with a collaborative filtering algorithm for making recommendations. Additionally, the Principle Component Analysis (PCA) method is used to condense the dimensions of the data to improve the accuracy of the outcomes. The dataset will receive additional benefits from the clustering technique when using hierarchical clustering, and the PCA will help redefine the dataset by reducing its dimensionality as needed. The primary elements utilized for recommendations can be enhanced by applying the key elements of these two strategies to the conventional collaborative filtering recommendation algorithm. The suggested method will unquestionably improve the precision of the findings received from the conventional CFRA and significantly increase the effectiveness of the recommendation system. The total findings will be applied to the combined dataset of TMDB and Movie Lens, which is utilized to suggest movies to the user in accordance with the rating patterns that each individual user has generated.

Remembering past and predicting future: a hybrid recurrent neural network based recommender system.

Traditional recommender systems (RS) assume users’ taste to be static (taste remains same over time) and reactive (a change in taste cannot be predicted and is observed only after it occurs). Further, traditional RS restricts the recommendation process to candidate items generation. This work aims to explore two phases of RS, i.e., Candidate Generation as well as Candidate Ranking. We propose a RS from a multi-objective (short-term prediction, long-term prediction, diversity, and popularity bias) perspective which was previously overlooked. The sequential and non-sequential behavior of users is exploited to predict future behavioral trajectories with the consideration of short-term and long-term prediction using recurrent neural networks and nearest neighbors approach. Further, a novel candidate ranking method is introduced to prevent users from being entangled in recommended items. On multiple datasets, largest being MovieLens (ML) 1M, our model shows excellent results achieving a hit rate and short-term prediction success of 58% and 71% respectively on ML 1M. Further, it implicitly handles two important parameters, i.e., diversity and item popularity with a success rate of 59.22% and 34.28% respectively.

Graph Convolution Network Recommender Rystem with Feature Embedding

Recommender system has become one of the most important parts of information services on the Internet today the model based on graph neural network has been proved to be a very effective method in collaborative filtering recommender system [1,15]. However, the recommender system based on graph network usually only uses the interactive relationship between users and items, and their feature information is often not used effectively. In this work, we combine the use of feature information with graph convolutional network to design a new recommendation algorithm, Graph Convolution Network recommender system with Feature Embedding, GCNFE. Tests on the Movie lens and Last-FM datasets show that the new model performs better than previous models in most cases.

Improvement and Research of Collaborative Filtering Algorithm Based on Penalty Factor

The rapid development of the Internet has pushed society into the era of information explosion, and people are faced with more and more information screening and choices. A recommendation system is an effective way to process massive amounts of information, and it is also a tool that can make recommendations based on user behavior. Traditional collaborative filtering algorithms generally use the cosine similarity formula to calculate the similarity between users or items to make recommendations. Due to the popularity of the Internet, more and more popular items have appeared. The appearance of popular items not only affects the recommendation results, but also fails to reflect the real needs of users. This paper proposes an improved formula for cosine similarity with a penalty factor, which can restrain the influence of popular items on the recommendation result. Finally, the Movie Lens data set is used to verify that the recommended performance indicators have been improved to a certain extent.

Comparison Study on Improved Movie Recommender Systems

Recommendation system is a crucial part of offering items especially in services that offer streaming. For streaming movie services on OTT, RS are a helping hand for users in finding new movies for leisure. In this paper, we propose a machine learning an approach based on auto encoders to produce a CF system which outputs movie rating for a user based on a huge DB of ratings from other users. Utilising Movie Lens dataset, we explore the use of deep learning neural network based Stacked Auto encoders to predict user s ratings on new movies, thereby enabling movie recommendations. We consequently implement Singular Value Decomposition (SVD) to recommend movies to users. The experimental result showcase that our R S out performs a user-based neighbourhood baseline in terms of MSE on predicted ratings and in a survey in which user judge between recommendation s from both systems.

A Comparing Collaborative Filtering and Hybrid Recommender System for E-Commerce

Abstract: Here we are building an collaborative filtering matrix factorization based hybrid recommender system to recommend movies to users based on the sentiment generated from twitter tweets and other vectors generated by the user in their previous activities. To calculate sentiment data has been collected from twitter using developer APIs and scrapping techniques later these are cleaned, stemming, lemetized and generated sentiment values. These values are merged with the movie data taken and create the main data frame.The traditional approaches like collaborative filtering and content-based filtering have limitations like it requires previous user activities for performing recommendations. To reduce this dependency hybrid is used which combines both collaborative and content based filtering techniques with the sentiment generated above. Keywords: machine learning, natural language processing, movie lens data, root mean square equation, matrix factorization, recommenders system, sentiment analysis

An Effective Approach for Modular Community Detection in Bipartite Network Based on Integrating Rider with Harris Hawks Optimization Algorithms

The strenuous mining and arduous discovery of the concealed community structure in complex networks has received tremendous attention by the research community and is a trending domain in the multifaceted network as it not only reveals details about the hierarchical structure of multifaceted network but also assists in better understanding of the core functions of the network and subsequently information recommendation. The bipartite networks belong to the multifaceted network whose nodes can be divided into a dissimilar node-set so that no edges assist between the vertices. Even though the discovery of communities in one-mode network is briefly studied, community detection in bipartite networks is not studied. In this paper, we propose a novel Rider-Harris Hawks Optimization (RHHO) algorithm for community detection in a bipartite network through node similarity. The proposed RHHO is developed by the integration of the Rider Optimization (RO) algorithm with the Harris Hawks Optimization (HHO) algorithm. Moreover, a new evaluation metric, i.e., h-Tversky Index (h-TI), is also proposed for computing node similarity and fitness is newly devised considering modularity. The goal of modularity is to quantify the goodness of a specific division of network to evaluate the accuracy of the proposed community detection. The quantitative assessment of the proposed approach, as well as thorough comparative evaluation, was meticulously conducted in terms of fitness and modularity over the citation networks datasets (cit-HepPh and cit-HepTh) and bipartite network datasets (Movie Lens 100 K and American Revolution datasets). The performance was analyzed for 250 iterations of the simulation experiments. Experimental results have shown that the proposed method demonstrated a maximal fitness of 0.74353 and maximal modularity of 0.77433, outperforming the state-of-the-art approaches, including h-index-based link prediction, such as Multiagent Genetic Algorithm (MAGA), Genetic Algorithm (GA), Memetic Algorithm for Community Detection in Bipartite Networks (MATMCD-BN), and HHO.

Collaborative Filtering Movie Recommendation System

Recommendation System plays an important role in today’s era of e-commerce. From OTT platforms to the shopping application and music application everywhere we see that after watching a movie or buying an item, listening a song we are recommended with some other movie or item or song. Most of the time we select our next movie, item or song from the recommended one. In this paper I will give you a brief description of collaborative and user-based filtering. The data used in this research is taken from Movie Lens. The result obtained contains some movie recommendations.