Movie Recommendation Research Articles

Web-Based Movie Recommendation System Using Content-Based Filtering and Cosine Similarity

Movie are one of the most popular entertainment media among people and are often chosen as activities during weekend holidays. As time goes by, world cinema continues to develop with various interesting and entertaining genres, stories and visuals. Because film is one of the entertainment media that can relieve stress from work assignments or lectures and now film production is also growing so that more and more films are being produced until finally people are confused about choosing the film they will watch. To resolve the obstacles faced, movie information is needed that can help people find movies that suit user preferences, so users need a system that can recommend movies. In this research, the author used the content-based filtering method to find movie recommendations. The substance utilizedis the movie genre. The Check Vectorization calculation is utilized to discover the term/word weight values in each record and after that these values are utilized as factors within the Cosine closeness to discover similitudes between archives.As a result of this last project the system can generate a kind of recommendations for the 10 most similar movies. The test results from this final project are that the system is running well and is reliable with an alpha test result of 100%, and a reliability test result of 0.7.

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

Predicting movies’ eudaimonic and hedonic scores: A machine learning approach using metadata, audio and visual features

In the task of modeling user preferences for movie recommender systems, recent research has demonstrated the benefits of describing movies with their eudaimonic and hedonic scores (E and H scores), which reflect the depth of their message and the level of fun experience they provide, respectively. So far, the labeling of movies with their E and H scores has been done manually using a dedicated instrument (a questionnaire), which is time-consuming. To address this issue, we propose an automatic approach for predicting E and H scores. Specifically, we collected E and H scores of 709 movies from 370 users (with a total of 3699 records), augmented this dataset with metadata, audio, and low-level and high-level visual features, and trained machine learning models for predicting the E and H scores of movies. This study investigates the use of machine learning models in predicting the E and H scores of movies using various feature sets, including audio, low-level and high-level visual features, and metadata. We compared the performance of predictive models using different combinations of features with the majority classifier as the baseline approach. The results demonstrate that our proposed machine learning-based models significantly outperform the baseline in predicting E and H scores, particularly when leveraging metadata features. Specifically, the random forest classifier achieved a 20% increase in ROC AUC compared to the baseline when predicting both the E score and the H score. These improvements were found to be statistically significant. Overall, our findings suggest that automated tools for predicting E and H scores in movies are promising alternatives to traditional questionnaire-based approaches.

Movie Recommendation System Based on Machine Learning

In today’s era, with the growth of businesses on an individual basis, many applications are trying to provide more effective services to individual users, and eventually launched personalized services. Simply put, when a user enters certain keywords in a search engine, the recommendation system can push certain information or products to the user. Based on this principle, there are personalized recommendation videos on the homepage of short video platforms, and various music software have launched personalized radio stations. Even if you want to watch a movie on Netflix, it can recommend it to you. These applications are all looking for similar content in the content that users are interested in and recommending it. A simple movie recommendation system only matches similar movies through user history records, and the accuracy of matching is not high and easily affects efficiency. Therefore, a simple movie recommendation system can no longer meet user needs. The movie recommendation system that incorporates Spark and machine learning algorithms can better serve the public. This article will explore the impact of adding different algorithms to machine learning content on the matching degree of recommended content.

A highly scalable CF recommendation system using ontology and SVD-based incremental approach

In recent years, the need of recommender systems has increased to enhance user engagement, provide personalized services, and increase revenue, especially in the online shopping industry where vast amounts of customer data are generated. Collaborative filtering (CF) is the most widely used and effective approach for generating appropriate recommendations. However, the current CF approach has limitations in addressing common recommendation problems such as data inaccuracy recommendations, sparsity, scalability, and significant errors in prediction. To overcome these challenges, this study proposes a novel hybrid CF method for movie recommendations that combines the incremental singular value decomposition approach with an item-based ontological semantic filtering approach in two phases, online and offline. The ontology-based technique is leveraged to enhance the accuracy of predictions and recommendations. Evaluating our method on a real-world movie recommendation dataset using precision, F1 scores, and mean absolute error (MAE) demonstrates that our system generates accurate predictions while addressing sparsity and scalability issues in recommendation system. Additionally, our method has the advantage of reduced running time.

A grasshopper optimization algorithm-based movie recommender system

A grasshopper optimization algorithm-based movie recommender system

Enhancing User Engagement through Hybrid Algorithms in Movie Recommendation Systems

The Netflix Movie Recommendation System is a sophisticated algorithmic solution designed to enhance user experience by providing personalized movie suggestions. This system utilizes a combination of collaborative filtering, content-based filtering, and hybrid approaches to predict and recommend movies that users are likely to enjoy based on their viewing history and preferences. Collaborative filtering leverages the collective behavior of users, identifying patterns and similarities in viewing habits to suggest films. Content-based filtering, on the other hand, analyzes the attributes of movies, such as genre, director, and cast, to recommend titles with similar characteristics. The hybrid approach integrates both methods to overcome individual limitations and improve recommendation accuracy. Netflix employs advanced machine learning techniques, including deep learning and matrix factorization, to handle vast amounts of data and continuously refine the recommendation process. The system also incorporates user feedback, ratings, and implicit signals such as viewing time and interaction patterns to dynamically adapt to changing user preferences. This multi-faceted recommendation engine is critical in retaining user engagement, reducing churn, and enhancing the overall viewing experience on the platform. Through constant innovation and optimization, Netflix aims to deliver highly relevant and enjoyable content to its diverse global audience.

A Privacy Preserving System for Movie Recommendations Using Federated Learning

Recommender systems have become ubiquitous in the past years. They solve the tyranny of choice problem faced by many users, and are utilized by many online businesses to drive engagement and sales. Besides other criticisms, like creating filter bubbles within social networks, recommender systems are often reproved for collecting considerable amounts of personal data. However, to personalize recommendations, personal information is fundamentally required. A recent distributed learning scheme called federated learning has made it possible to learn from personal user data without its central collection. Consequently, we present a recommender system for movie recommendations, which provides privacy and thus trustworthiness on multiple levels: First and foremost, it is trained using federated learning and thus, by its very nature, privacy-preserving, while still enabling users to benefit from global insights. Furthermore, a novel federated learning scheme, called FedQ, is employed, which not only addresses the problem of non-i.i.d.-ness and small local datasets, but also prevents input data reconstruction attacks by aggregating client updates early. Finally, to reduce the communication overhead, compression is applied, which significantly compresses the exchanged neural network parametrizations to a fraction of their original size. We conjecture that this may also improve data privacy through its lossy quantization stage.

Movie Recommendation Algorithms Based on an Improved Pythagorean Hesitant Fuzzy Distance Measure and VIKOR Method

Movie Recommendation Algorithms Based on an Improved Pythagorean Hesitant Fuzzy Distance Measure and VIKOR Method

Movie recommendation system using taymon optimized deep learning network

The increased usage of the internet and social networks generates a large volume of information. Exploring through the large collection is time-consuming and hard to find the required one, so there is a serious need for a recommendation system. Based on this context several movie recommendation (MR) systems have been recently established. In addition, they have poor data analytics capability and cannot handle changing user preferences. As a result, there are many movies listed on the recommendation page, which provides for a poor user experience is the major issue. Therefore, in this work, a novel Taymon Optimized Deep Learning network (TODL net) for recommending top best movies based on their past choices, behaviour and movie contents. The deep neural network is a combination of Dilated CNN with Bi-directional LSTM. The DiCNN-BiLSTM model eliminates the functionality pooling operations and uses a dilated convolution layer to address the issue of information loss. The DiCNN is employed to learn the movie contents by mining user behavioral pattern attributes. The BiLSTM is applied to recommend the best movies on basis of the extracted features of the movie rating sequences of users in other social mediums. Moreover, for providing better results the DiCNN-BiLSTM is optimized with Taymon optimization algorithm to recommend best movies for the users. The proposed TODL net obtains the overall accuracy of 97.24% for best movies recommendation by using TMDB and MovieLens datasets.

Enhancing Recommendation System using Ontology-based Similarity and Incremental SVD Prediction

Background: With the explosion of data in recent years, recommender systems have become increasingly important for personalized services and enhancing user engagement in various industries, including e-commerce and entertainment. Collaborative filtering (CF) is a widely used approach for generating recommendations, but it has limitations in addressing issues such as sparsity, scalability, and prediction errors. Methods: To address these challenges, this study proposes a novel hybrid CF method for movie recommendations that combines an incremental singular value decomposition approach with an item-based ontological semantic filtering approach in both online and offline phases. The ontology-based technique improves the accuracy of predictions and recommendations. The proposed method is evaluated on a real-world movie recommendation dataset using several performance metrics, including precision, F1 scores, and MAE. Results: The results demonstrate that the proposed method outperforms existing methods in terms of accuracy while also addressing sparsity and scalability issues in recommender systems. Additionally, our approach has the advantage of reduced running time, making it a promising solution for practical applications. Conclusion: The proposed method offers a promising solution to the challenges faced by traditional CF methods in recommender systems. By combining incremental SVD and ontological semantic filtering, the proposed method not only improves the accuracy of predictions and recommendations but also addresses issues related to scalability and sparsity. Overall, the proposed method has the potential to contribute to the development of more accurate and efficient recommendation systems in various industries, including e-commerce and entertainment.

Machine Learning based Movie Recommendation System

Abstract: Machine Learning (ML) could be a fashionable engineering technique to form machines suppose or use their intelligence like humans by mimicking traits and by learning to require acceptable choices and to perform appointed tasks properly. a number of the businesses that have done outstanding add the sphere of ML (AI) ar Facebook, Google, Microsoft, IBM, etc. that ar investment millions and billions during this terribly field of ML development and analysis. presently there's a large market and want for building Intelligent Systems for Recommendation. To counter this, one amongst the simplest and most desirable System is Recommendation System (RS). Recommendation Systems had well-tried to play a crucial role within the field of E-Commerce websites, on-line searching, chemical analysis Apps, Social-Networking, Digital selling, on- line Advertisements, etc. by providing customized recommends and feedback to users in step with their preferences and selections. the subject of this report is AI primarily based picture show Recommendation System. because the topic of this paper suggests we have a tendency to ar planning to discuss concerning varied ways in which and approaches of ML (AI) to make a picture show Recommendation System (RS) application. There ar several approaches to make a recommendation system in step with one’s want like cooperative Filtering, Content primarily based Recommendation Systems, K-Means algorithmic rule. which can be in brief mentioned during this report, however we are going to primarily discuss and work on K suggests that algorithmic rule approach

A content-based collaborative filtering algorithm for movies and TVS recommendation

With the rapid development of multimedia technology and the constant upgrading of film and television libraries, users' demand for movies and television is increasing. How to accurately and timely find favorite movies from massive movie and television resources according to user's preferences and needs has become a great challenge. In recent years, the recommendation of movies and TVs has attracted a lot of research interest from academia and industry. The existing recommendation algorithms mainly include content based and collaborative filtering. The former recommends projects through collaborative learning of others' interests, while the content-based method examines the rich context of the project. In this paper, to further improve the performance of recommendations, a content based collaborative filtering method is proposed to provide recommendations for movies and television. Specifically, we extract and vectorize feature and category information from movies based on TF-IDF and apply truncated SVD to reduce the dimensions of the rating and TF-IDF matrix to retain the most representative information. We calculate the cosine similarity between the vectors from these two matrices. The final recommendation is to list 10 movies based on the average similarity of content and ratings. Extensive experiments on Amazon review data have proven the effectiveness of this method.

Deep learning methods used in movie recommendation systems

As the amount of internet movie data grows rapidly, traditional movie recommendation systems face increasing challenges. They typically rely on statistical algorithms such as item-based or user-based collaborative filtering. However, these algorithms struggle to handle large-scale data and often fail to capture the complexity and contextual information of user behavior. Therefore, deep learning techniques have been widely applied to movie recommendation systems. This paper reviews movie recommendation algorithms based on traditional statistical models and introduces three main deep learning techniques: Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). ANN can extract features at different levels of users and movies; CNN can capture features of movie posters and movie data to recommend similar movies; RNN can consider user historical behavior and contextual information to better understand user interests and demands. The application of these deep learning techniques can enhance the accuracy and user experience of movie recommendation systems. This paper also demonstrates the advantages and disadvantages of these models and their specific application methods in movie recommendation systems, and points out the direction for further development and improvement of deep learning models in this field.

CMF-SMR: Convolutional matrix factorization for sequential movie recommendations

Recommender system (RS) has become an essential component of e-commerce, social media, and other online platforms. Collaborative filtering (CF) is one of the most commonly used techniques in RS that relies on user-item interactions to generate recommendations. However, CF suffers from the cold-start problem, sparsity, and scalability issues. To address these challenges, this work propose a hybrid system called Convolutional Matrix Factorization for Sequential Movie Recommendations (CMF-SMR), which combines matrix factorization (MF) with convolutional neural networks (CNNs). CMF-SMR leverages the non-linear feature extraction capabilities of CNNs and the representation learning abilities of deep learning to enhance the accuracy and robustness of traditional MF-based RS. Specifically, CNNs and MF were used to respectively extract features from user-item interaction data and use them as input for learning user and item representations. The learned representations are then used to predict user-item ratings. This work evaluates the performance of our proposed method on two publicly available datasets, and the experimental results demonstrate that our method outperforms several state-of-the-art techniques in terms of accuracy, scalability, and robustness. Moreover, this work conduct evaluation metrics to demonstrate the accuracy of our proposed method. Overall, our proposed CMF-SMR provides a promising solution for addressing the limitations of traditional CF-based RS and can be applied in various domains, including e-commerce, social media, and personalized content recommendation systems.

Research and improvement of movie recommendation based on a collaborative filtering algorithm

Among various recommendation algorithms, collaborative filtering stands out as a popular and effective technique that analyzes user behavior to suggest personalized items. The goal of studying recommendation algorithms is to enhance the exactness of recommendation outcomes and provide users with tailored suggestions. In this paper, the algorithm of collaborative filtering is divided into User-CF and Item-CF, and their algorithm principles and implementation steps are introduced. The similarity calculation of both methods is improved, and a comparison of their advantages and disadvantages is conducted. Experiments are conducted on the MovieLens-latest-small movie dataset to compare the performance of User-CF and Item-CF before and after their similarity calculation is improved. The results show that the similarity improvement of User-CF can substantially enhance the algorithms inclusiveness, while the similarity improvement of Item-CF does not have a considerable impact on the algorithms performance indicators. Finally, the comparison shows that Item-CF has better coverage and popularity, while User-CF has better precision and recall.

The application of social recommendation algorithm integrating attention model in movie recommendation

To improve the accuracy of recommendations, alleviate sparse data problems, and mitigate the homogenization of traditional socialized recommendations, a gated recurrent neural network is studied to construct a relevant user preference model to mine user project preferences. Through the Preference Attention Model Based on Social Relations (PASR), this study extracts user social influence preferences, performs preference fusion, and obtains a Recommendation Algorithm Based on User Preference and Social Influence (UPSI). The study demonstrates that the UPSI algorithm outperforms other methods like the SocialMF algorithm, yielding improved recommendation results, higher HR values, and larger NDCG values. Notably, when the K value equals 25 in Top-K recommendation and using the CiaoDVDs dataset, the NDCG value of the UPSI algorithm is 0.267, which is 0.120 higher than the SocialMF algorithm's score. Considering the user's interaction with the project and their social relationships can enhance the effectiveness of recommendations. Unlike other variants, the UPSI algorithm achieves a maximum hit rate HR value of 0.3713 and NDCG value of 0.2108 in the Douban dataset. In the CiaoDVDs dataset, the maximum hit rate HR value of UPSI is 0.4856, 0.0333 higher than UPS-A, 0.0601 higher than UPS, and 0.0901 higher than UP. Research methods can effectively improve the homogenization problem of traditional socialized recommendations, increase algorithm hit rates and NDCG values. Compared to previous studies, research methods can more fully explore the preference correlation between users, making recommended movies more in line with user requirements.

Effective movie recommendation based on improved densenet model

In recent times, recommendation systems provide suggestions for users by means of songs, products, movies, books, etc. based on a database. Usually, the movie recommendation system predicts the movies liked by the user based on attributes present in the database. The movie recommendation system is one of the widespread, useful and efficient applications for individuals in watching movies with minimal decision time. Several attempts are made by the researchers in resolving these problems like purchasing books, watching movies, etc. through developing a recommendation system. The majority of recommendation systems fail in addressing data sparsity, cold start issues, and malicious attacks. To overcome the above-stated problems, a new movie recommendation system is developed in this manuscript. Initially, the input data is acquired from Movielens 1M, Movielens 100K, Yahoo Y-10-10, and Yahoo Y-20-20 databases. Next, the data are rescaled using a min-max normalization technique that helps in handling the outlier efficiently. At last, the denoised data are fed to the improved DenseNet model for a relevant movie recommendation, where the developed model includes a weighting factor and class-balanced loss function for better handling of overfitting risk. Then, the experimental result indicates that the improved DenseNet model almost reduced by 5 to 10% of error values, and improved by around 2% of f-measure, precision, and recall values related to the conventional models on the Movielens 1M, Movielens 100K, Yahoo Y-10-10, and Yahoo Y-20-20 databases.

Interactive Content Diversity and User Exploration in Online Movie Recommenders: A Field Experiment

Recommender systems often struggle to strike a balance between matching users’ tastes and providing unexpected recommendations. When recommendations are too narrow and fail to cover the full range of users’ preferences, the system is perceived as useless. Conversely, when the system suggests too many items that users don’t like, it is considered impersonal or ineffective. To better understand user sentiment about the breadth of recommendations given by a movie recommender, we conducted interviews and surveys and found out that many users considered narrow recommendations to be useful, while a smaller number explicitly wanted greater breadth. Additionally, we designed and ran an online field experiment with a larger user group, evaluating two new interfaces designed to provide users with greater access to broader recommendations. We looked at user preferences and behavior for two groups of users: those with higher initial movie diversity and those with lower diversity. Among our findings, we discovered that different levels of exploration control and users’ subjective preferences on interfaces are more predictive of their satisfaction with the recommender.

A Long Tail Item Recommendations for MovieLens

Amidst the framework of the Movie-lens dataset, this research paper digs into the essential topic of long-tail item suggestions. Long tail items, which are frequently neglected in traditional recommendation systems, constitute a substantial reservoir of specialty material. We evaluate and analyze existing methodology and breakthroughs in this subject, including collaborative filtering and matrix factorization, as well as hybrid and deep learning-based approaches. Our assessment highlights the ongoing issue of improving recommendation accuracy and diversity, especially for less popular films. In this paper, we investigate the crucial importance of user-item interaction patterns and auxiliary data sources in tackling the long tail problem. We present a complete analysis of the state-of-the-art in long tail item suggestions for movie lenses by exploring the strengths and limits of various strategies. This study gives insightful information for long-tail recommendations looking to create more inclusive, user-centric, and interesting movie recommendation platforms. It also sheds light on the changing landscape of recommendation systems.