Personalized Movie Recommendation Research Articles

Personalized movie recommendations based on probabilistic linguistic sentiment and integrated DEMATEL-TODIM methods

With the development of society, online reviews are increasingly becoming a crucial factor in decision-making. Especially for entertainment products such as movies, they are preferred for their affordability and high entertainment factor. Therefore, this paper proposes a movie recommendation model that considers user personalization using a probabilistic linguistic approach based on online reviews. Firstly, the method constructs a quantitative sentiment framework that transforms comments into a multi-granular probabilistic sentiment language. Secondly, we build the decision-making trial and evaluation laboratory (DEMATEL) method for probabilistic linguistic environments to explore interrelationships between product attributes, and improve the distance measure and score function to better integrate probabilistic linguistic information into DEMATEL weight calculations. Furthermore, to account for risk preferences, the model employs the extended TODIM (an acronym in Portuguese for interactive and multicriteria decision making) methodology to determine the ranking of alternatives. Finally, we design Douban movie ranking experiments to demonstrate the validity of the model. Compared with other methods, this paper incorporates the emotional tendency of movie attributes and user preference into the decision-making process leading to more reasonable results.

Optimizing User Satisfaction in Movie Recommendations Using Variable Learning Rates and Dynamic Neighborhood Functions in SOMs

Customized movie recommendations are crucial in elevating user satisfaction and engagement in the era of vast online entertainment options. This study presents an innovative approach utilizing Enhanced Self-Organizing Maps (SOMs) for movie categorization. SOMs, as unsupervised neural networks, are highly effective in recommendation systems due to their ability to identify intricate data patterns accurately. The proposed method involves collecting user-movie interaction data, such as user ratings and movie attributes. Data standardization is performed to ensure consistency before training the refined SOM. By integrating variable learning rates and dynamic Neighborhood functions, the advanced SOM can uncover complex patterns within datasets, thus enhancing the accuracy of personalized movie recommendations by identifying meaningful connections between users and films. To further improve recommendation quality, hybrid filtering techniques are employed, combining content-based filtering, which considers movie characteristics like genre and description, with collaborative filtering algorithms that analyze user-item interactions to expand the range of recommended films. This integrated approach allows for the generation of user-movie matrices by employing SVD collaborative filtering to give precedence to movie recommendations. The hybrid technique demonstrates superior performance compared to earlier models, attaining an RMSE of 0.410, MAE of 0.211, precision of 92.09%, recall of 93.12%, and an F1-score of 92.15%, consequently offering very accurate movie recommendations. Subsequent studies could concentrate on improving personalised recommendations by integrating supplementary contextual data.

Movie Recommendation System using Machine Learning

There is already enough content available on the movie recommendation system. Showing the movie recommendations is essential so that the user need not waste a lot of time searching for the content which he/she might like. Thus, movie recommendation system plays a vital role to get user personalized movie recommendations. After searching a lot on the internet and referring to a lot of research papers, we got to know that the recommendations made using Content-based Filtering are using a single text to vector conversion technique and a single technique to find the similarity between the vectors. In this research work, we have used multiple text to vector conversion techniques and manipulated the results of the multiple algorithms to get the final recommendation list. You can think of it as a hybrid approach using the Content-based Filtering technique only.

Beyond the Hype : Building a Personalized Movie Experience with Content-Based Recommendation

This review paper provides a detailed analysis of how a movie recommendation system based on content was planned, executed and evaluated using Streamlit framework. The exponential expansion of digital content has led to the need for efficient recommendation systems, particularly in the realm of movies. In this respect, the proposed recommender system utilizes cosine similarity computations and content-based filtering methods to offer personalized film suggestions relying upon various features such as genres, keywords, castings as well as crews. The author further illustrates rigorous data preparation methods which encompass feature engineering, data collection and cleaning techniques. Furthermore, various other data sources such as TMDb API have been integrated to obtain detailed information about movies. The system, after a thorough study, shows that it has the ability of offering personalized movie recommendations that improve user engagement and experience. This paper examines broader implications of recommendation algorithms and argues their importance for improving happiness and content discovery in digital entertainment platforms. It also raises some common issues encountered during system development and advocates further research work aimed at enhancing recommendation algorithms and making them more flexible and scalable. In this way, it advances our understanding on how personalized content delivery can be applied to shape the landscape of digital entertainment through unmasking the complexities of recommendation systems.

RECOMMENDAITON SYSTEM ENGINE FOR MOVIES USING MACHINE LEARNING ALGORITHM (TF-IDF VECTORIZATION)

By offering tailored movie recommendations, Movie Recommendation Systems (MRS) are crucial for improving the user experience on streaming services. This research paper proposes and evaluates a Movie Recommendation System utilizing TF-IDF vectorization and cosine similarity. TF-IDF vectorization is used to analyze textual information related to movies, such as plot summaries, cast bios, and genres, in order to give users precise and pertinent suggestions. The similarity between the user's preferences and the movies in the dataset is then calculated using cosine similarity. The results of the study show that the suggested Movie Recommendation System, which makes use of cosine similarity and TF-IDF vectorization, greatly improves user happiness and recommendation accuracy. The developed system offers an effective solution for providing personalized movie recommendations, contributing to the advancement of recommendation systems in the entertainment industry. This study provides valuable insights for streaming platforms to improve their recommendation systems and enhance user engagement. Keywords: Movie Recommendation System, Machine Learning (ML), Natural Language Processing (NLP) TF-IDF Vectorization, Cosine Similarity, Content-Based Filtering, Personalized Recommendations, User Engagement.

Optimizing a Personalized Movie Recommendation System with Support Vector Machine and Content-Based Filtering

Optimizing a Personalized Movie Recommendation System with Support Vector Machine and Content-Based Filtering

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

Movie Recommendation System Using KNN, Cosine Similarity and Collaborative Filtering

The movie recommendation system is becoming increasingly popular in the digital era. With the continuous emergence of a vast amount of movie resources, users are facing more and more choices. Therefore, an intelligent movie recommendation system can assist users in quickly finding movies that match their personal preferences, thereby enhancing user satisfaction and movie-watching experience . Our movie recommendation system recommends high-rated and well-reviewed films and TV shows to the general audience based on Netflix viewers’ ratings for them. These are the movies and shows that are considered worth watching. We utilize the Netflix open dataset, which consists of a large number of user ratings and movie information [1]. By merging the movie and rating datasets and performing data cleansing and preprocessing, we obtain a dataset suitable for models. Subsequently, we represent the movie rating data using sparse matrices and train a k-nearest neighbours (k-NN) algorithm to achieve personalized movie recommendations [2].Our approach considers both user rating preferences and movie similarities, enabling accurate and personalized recommendations for users. Through evaluation experiments and user surveys, we validate the effectiveness of our recommendation system in terms of accuracy and user satisfaction [3]. This research is significant for improving the effectiveness of movie recommendation systems and enhancing user experiences.

Construction of Personalized Movie Recommendation Model Relying on Recurrent Neural Network

Recommendation system and classification machine learning techniques have emerged as a major area of investigation into the issue of information overload. Software tools called "recommender systems" aim to help users make informed choices about services. This paper developed a low-dimensional embedding in which a limited number of features can be used to represent the data object. For the purpose of acquiring the low rank latent factors, matrix factorization techniques have received a lot of attention. The proposed model is stated as the Collaborative Filtering Machine Learning (CFML) for the multi-label classification are focused. In most extreme edge lattice factorization plan of cooperative sifting, appraisals framework with different discrete qualities is treated by uncommonly stretching out pivot misfortune capability to suit numerous levels. The comparative analysis is performed for the two-class classifier into a single multi-class classifier. Alternately, multiple two-class classifiers can be arranged in a hierarchical fashion to create a multi-class classifier. To deal with the ordinal rating matrix's completion. The performance of the CFML model is effective for the recommendation system design. Through automatic feature extraction and pattern recognition, deep learning models excel at capturing intricate relationships and latent factors that underlie user preferences. The paper discusses the multifaceted considerations that these systems can incorporate, such as genre, cast, director, viewer ratings, and individual viewing history. Simulation analysis examine the impact of deep learning-based movie recommenders on streaming platforms and the entertainment industry, demonstrating their ability to not only suggest movies but to curate a personalized journey through the world of cinema.

SOM and Hybrid Filtering: Pioneering Next-Gen Movie Recommendations in the Entertainment Industry

In an age where digital connectivity is increasingly shaping entertainment content, personalized movie recommendations play a pivotal role in enhancing user satisfaction and engagement. This research introduces an innovative approach utilizing Enhanced Self-Organizing Maps (SOM) to streamline movie selection processes. Self-Organizing Maps (SOMs), a type of unsupervised neural network architecture, are particularly adept at discerning intricate data patterns, making them valuable assets in recommendation systems. The methodology outlined in this paper commences with gathering user-movie interaction data, including user feedback and movie characteristics, which is standardized to ensure consistency before model training. Leveraging its adaptable learning rate and neighborhood function, the Enhanced SOM effectively identifies subtle data nuances. Personalized movie suggestions are then generated by exploiting the Enhanced SOM's capacity to identify similar users and films. Integration of hybrid filtering techniques enriches recommendation quality, blending collaborative filtering algorithms, which leverage user-item interactions, with content-based filtering, which utilizes movie attributes such as genres and descriptions. This amalgamation results in suggestions that harmoniously combine diverse filtering methodologies. The proposed solution's efficacy is rigorously evaluated by comparing suggestion accuracy and user satisfaction against predefined benchmarks. Extensive real-world dataset testing corroborates the effectiveness of the Enhanced SOM-based movie recommendation approach. Furthermore, the system offers flexibility through options for parameter adjustment, grid size variations, and neighborhood function modifications to further refine recommendation accuracy. Collectively, these elements underscore the efficacy of the proposed method in furnishing tailored movie recommendations. When coupled with hybrid filtering techniques, the implementation of Enhanced SOMs emerges as a reliable model for content platforms seeking to enhance user experiences by delivering precise movie recommendations, coupled with scalability and adaptability.

Cinematic Curator: A Machine Learning Approach to Personalized Movie Recommendations

Cinematic Curator: A Machine Learning Approach to Personalized Movie Recommendations

Optimizing a Personalized Movie Recommendation System with Support Vector Machine and Content-Based Filtering

Optimizing a Personalized Movie Recommendation System with Support Vector Machine and Content-Based Filtering

Temporal-Guided Knowledge Graph-Enhanced Graph Convolutional Network for Personalized Movie Recommendation Systems

Traditional movie recommendation systems are increasingly falling short in the contemporary landscape of abundant information and evolving user behaviors. This study introduced the temporal knowledge graph recommender system (TKGRS), a ground-breaking algorithm that addresses the limitations of existing models. TKGRS uniquely integrates graph convolutional networks (GCNs), matrix factorization, and temporal decay factors to offer a robust and dynamic recommendation mechanism. The algorithm’s architecture comprises an initial embedding layer for identifying the user and item, followed by a GCN layer for a nuanced understanding of the relationships and fully connected layers for prediction. A temporal decay factor is also used to give weightage to recent user–item interactions. Empirical validation using the MovieLens 100K, 1M, and Douban datasets showed that TKGRS outperformed the state-of-the-art models according to the evaluation metrics, i.e., RMSE and MAE. This innovative approach sets a new standard in movie recommendation systems and opens avenues for future research in advanced graph algorithms and machine learning techniques.

Personalized movie recommendations based on deep representation learning.

Personalized recommendation is a technical means to help users quickly and efficiently obtain interesting content from massive information. However, the traditional recommendation algorithm is difficult to solve the problem of sparse data and cold-start and does not make reasonable use of the user-item rating matrix. In this article, a personalized recommendation method based on deep belief network (DBN) and softmax regression is proposed to address the issues with traditional recommendation algorithms. In this method, the DBN is used to learn the deep representation of users and items, and the user-item rating matrix is maximized. Then softmax regression is used to learn multiple categories in the feature space to predict the probability of interaction between users and items. Finally, the method is applied to the area of movie recommendation. The key to this method is the negative sampling mechanism, which greatly improves the effectiveness of the recommendations, as a result, creates an accurate list of recommendations. This method was verified and evaluated on Douban and several movielens datasets of different sizes. The experimental results demonstrate that the recommended performance of this model, which has high accuracy and generalization ability, is much better than typical baseline models such as singular value decomposition (SVD), and the mean absolute error (MAE) value is 98%, which is lower than the best baseline model.

INTELLIGENT MOVIE RECOMMENDER FRAMEWORK BASED ON CONTENT-BASED & COLLABORATIVE FILTERING ASSISTED WITH SENTIMENT ANALYSIS

Recommendation systems for rating movies and forming opinion have grown exponentially in popularity, they make it very convenient for consumers to select films that suit their tastes. However, traditional recommendation systems often rely solely on user ratings or reviews, which may not accurately reflect the user's true feelings about a movie. To address this issue, sentiment analysis has been proposed as a more reliable method for capturing emotional information about movies. In this research paper, we propose a novel movie recommendation system that combines sentiment analysis with collaborative filtering and content-based methods. Our system is designed to provide accurate and timely recommendations to mobile users based on their preferences, reviews, and emotions. We evaluate the performance of our system using real-world data and demonstrate its effectiveness in improving the accuracy and timeliness of movie recommendations. In this research paper, we propose a comparative study of three popular movie recommendation techniques: content-based filtering, collaborative filtering, and sentiment analysis. We aim to evaluate the effectiveness of each approach in providing accurate and personalized movie recommendations. Work done has utilized MovieLens dataset for this study, which is a popular benchmark dataset for assessing movie recommendation systems. The dataset includes almost 100,000 ratings, ranging from 1 to 5, from 943 individuals on 1,682 films. Each rating has a timestamp, a special user ID, and a movie ID. The collection also contains details about each movie's genre. To prepare the dataset for our experiments, we first performed some data cleaning and pre-processing. We removed any duplicate ratings, and any movies or users with a low number of ratings were also removed. We also performed some feature engineering to extract relevant features from the raw data, such as movie genres, user demographics, and movie release dates. Our tests on the Movielens dataset show that the system we've suggested works well. Precision, recall, and F1-score were some of the measures we used to assess the system's performance. We discovered that it performed better than conventional recommendation systems that just use content-based or collaborative filtering techniques. A better user experience was produced with the use of sentiment analysis, which improved the accuracy and promptness of suggestions. Algorithm designed achieves a precision of 0.875, Recall of 0.0435, F1 Score of 0.0828, Accuracy of 0.9889, RMSE of 0.407, and F measure of 0.1813.

Movie Recommendation Based System Using Time Series Data

Finding the right movie from a wide selection can be difficult, leading to frustration and wasted time. Recommendation systems offer a solution by providing personalized movie recommendations based on users' interests and preferences. These systems use data analytics, machine learning algorithms and temporal analysis techniques to understand user behavior and provide accurate recommendations. Collaborative filtering algorithms identify similarities between users or movies, while content-based filtering separates movie features based on user preferences. Time series analysis methods collect temporal patterns for dynamic recommendations. The results of the literature review support the effectiveness of movie recommendation systems based on time series data, showing their ability to provide accurate recommendations despite changing information and changing preferences. Real-time data collection improves system efficiency. Overall, the proposed solution aims to improve the movie selection process, save users time and effort, and at the same time improve the movie viewing experience.

Cinephiles Integration System using ML

Abstract: Our project aims to create a movie recommendation and community platform where users can discover and share their favorite movies with others. The platform will utilize a recommendation system to suggest personalized movie recommendations based on the user's preferences and viewing history. Users will also be able to rate and review movies, create watchlists, and follow other users with similar movie tastes. The community aspect of the platform will allow users to engage with others through forums, discussions, and private messaging. This will create a space for movie enthusiasts to connect and share their thoughts on the latest releases, hidden gems, and all-time favorites. To develop the recommendation system, we will use collaborative filtering and content-based filtering techniques. The platform will also utilize machine learning algorithms to analyze user behavior and provide more accurate recommendations over time. Overall, our movie recommendation and community platform will provide a comprehensive and interactive movie-watching experience for users while promoting a sense of community among movie enthusiasts.

Personalized Movie Recommendations Based on a Multi-Feature Attention Mechanism with Neural Networks

With the rapid growth of the Internet, a wealth of movie resources are readily available on the major search engines. Still, it is unlikely that users will be able to find precisely the movies they are more interested in any time soon. Traditional recommendation algorithms, such as collaborative filtering recommendation algorithms only use the user’s rating information of the movie, without using the attribute information of the user and the movie, which has the problem of inaccurate recommendations. In order to achieve personalized accurate movie recommendations, a movie recommendation algorithm based on a multi-feature attention mechanism with deep neural networks and convolutional neural networks is proposed. In order to make the predicted movie ratings more accurate, user attribute information and movie attribute information are added, user network and movie network are presented to learn user features and movie features, respectively, and a feature attention mechanism is proposed so that different parts contribute differently to movie ratings. Text features are also extracted using convolutional neural networks, in which an attention mechanism is added to make the extracted text features more accurate, and finally, personalized movie accurate recommendations are achieved. The experimental results verify the effectiveness of the algorithm. The user attribute features and movie attribute features have a good effect on the rating, the feature attention mechanism makes the features distinguish the degree of importance to the rating, and the convolutional neural network adding the attention mechanism makes the extracted text features more effective and achieves high accuracy in MSE, MAE, MAPE, R2, and RMSE indexes.

Hybrid Movie Recommendation System Using Machine Learning

This research suggests a hybrid movie recommendation system and optimization approach based on weighted classification and user collaborative filtering algorithm to address the issue that the single model of the standard recommendation system cannot adequately reflect user preferences. The top-N personalized movie recommendations are made by fusing the weighted classification model with the local recommendation model, which is trained based on user clustering, and the sparse linear model, which serves as the fundamental recommendation model. The scoring matrix is transformed into a low-dimensional, dense item category preference matrix based on item category preference, multiple cluster centers are obtained, the distance between each cluster center and the target user is calculated, and the target user is categorized into the closest cluster. Finally, a suggestion list is created using the collaborative filtering algorithm to forecast the scores for the target user's unrated items. The highdimensional rating matrix is transformed into a lowdimensional item category preference matrix, which further reduces the sparsity of the data. The items are then grouped based on item category preference. The recommendation algorithm suggested in this article addresses some of the limitations of a single algorithm model and enhances the suggestion effect, according to experiments using the MovieLens movie dataset.

Personalized Movie Recommendation System Based on DDPG: Application and Analysis of Reinforcement Learning in User Preferences

Personalized Movie Recommendation System Based on DDPG: Application and Analysis of Reinforcement Learning in User Preferences