Movie Recommendation System Research Articles

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.

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.

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.

Enhanced Collaborative Filtering Algorithm for Movies Recommendation using Big Five Personality Traits

Recommender System suffers from data sparsity and cold start problems which arises when there is no sufficient rating history for user who has recently log into the system and no proper recommendations can be made. This paper develops an Enhanced collaborative filtering algorithm for Movie recommender system Using genre and Big five Personality traits (EMUBP) as system’s input. The experimental result shows that the EMUBP system improved recommendation quality and accuracy by 8.33% compared with the existing state-of-the-art using precision, recall and MAE.

SISTEM REKOMENDASI FILM MENGGUNAKAN METODE CONTENT-BASED FILTERING STUDI KASUS MATERI DATA MINING DI SMK IDN BOARDING SCHOOL

Movie recommendation systems have become an interesting research topic, as they can assist users in finding movies that match their preferences. The Content-Based Filtering method is one of the methods commonly used in film recommendation systems, which focuses on film content to make recommendations. This study aims to develop a film recommendation system using the Content-Based Filtering method. This method is used to make film recommendations based on the similarity of the features of the films that have been watched by the user. This research uses case studies at SMK IDN Boarding School, where this film recommendation system was built as a means of learning data mining materials for students. Movie data is taken from the film API and processed using an algorithm to extract features from film content. Then, the system recommends films based on the similarity of features with the films that are liked by the user. The test results show that the developed film recommendation system has good performance in recommending films based on user preferences. Users can provide feedback to improve the accuracy of recommendations and the system can continue to be improved using more sophisticated methods.

Multi-knowledge resources-based semantic similarity models with application for movie recommender system

Multi-knowledge resources-based semantic similarity models with application for movie recommender system

Movie Recommender System Using Decision Tree Method

In this modern era, many things that can be done online, one of which is watching movies. When the number of movies increases, people often find it difficult to decide which movie to watch next. To solve this problem, a useful recommendation system was developed to find movies that one might like based on movies that have been watched before. This research develops a movie recommendation system using Collaborative Filtering (CF) with the Decision Tree algorithm. In this study, the data used were movie data and ratings obtained from the grouplens.org website. Then the movielens dataset is filtered and only saves movies with a rating of more than 50 that are used in the recommendation system. In this study, Mean Absolute Error (MAE) is used as a method to assess the accuracy of the movie recommendation system. Based on the research that has been done, Decision Tree gets better results with an MAE value of 0,942 compared to Collaborative Filtering with an MAE value of 1,242.

A Deep Learning Approach towards Cold Start Problem in Movie Recommendation System

Recommendation systems play an important role for e-commerce websites to make profits. It has a variety of applications in different domains. There are three types of categories in which recommendation systems are classified i.e. content based, collaborative and hybrid systems. These systems suffer when a redundant amount of information is not available to provide recommendations. This problem is known as the cold start problem. In this digital era, it is possible to collect meta information about a user and provide rich recommendations. Various approaches such as social media analysis, graph networks have been proposed to solve this problem. But they lack personalization and generate irrelevant recommendations affecting the system performance. The objective of this work is to resolve new user cold start problem in movie recommendation systems using a deep learning approach that utilizes demographic attributes to cluster similar users. This embedding is given to the deep neural network to generate the recommendations. From the analysis done, we verify the effectiveness of our approach..

Collaborative Movie Recommendation System using Enhanced Fuzzy C-Means Clustering with Dove Swarm Optimization Algorithm

Recommender Systems (RSs) aid in filtering information seeking to envisage user and item ratings, primarily from huge data to recommend the likes. Movie RSs offer a scheme to help users categorize them based on comparable interests. This enables RSs to be a dominant part of websites and e-commerce applications. This paper proposes an optimized RS for movies, primarily aiming to suggest an RS by clustering data and Computational Intelligence (CI). Unsupervised clustering, a model-based Collaborative Filtering (CF) category, is preferred as it offers simple and practical recommendations. Nevertheless, it involves an increased error rate and consumes more iterations for converging. Enhanced Fuzzy C-Means (EFCM) clustering is proposed to handle these issues. Dove Swarm Optimisation Algorithm (DSOA)-based RS is proposed for optimising Data Points (DPs) in every cluster, providing effcient recommendations. The performance of the proposed EFCM-DSOA-based RS is analysed by performing an experimental study on benchmarked MovieLens Dataset. To ensure the effciency of the proposed EFCM-DSOA-based RS, the outcomes are compared with EFCM-Particle Swarm Optimization (EFCM-PSO) and EFCM-Cuckoo Search (EFCM-CS) based on standard optimization functions. The proposed EFCM-DSOA-based RS offers improved F-measure, Accuracy, and Fitness convergence.

Emotion based Movie Recommendation System using Deep Learning

Emotion based Movie Recommendation System using Deep Learning

Individualized Movie Recommendation Algorithm Based on Machine Learning

With the continuous acceleration of economic growth and the remarkable improvement of people's living standards, people's individualized needs have increased. Moreover, due to the growth of computer technology and network technology, many forms of entertainment and services have been integrated into the network. The recommendation engine actively pushes the information that meets the user's taste and preference to the user by analyzing the user's behavior data on the website when the user's needs are not clear. This article will study how to apply the deep learning algorithm to the field of movie recommendation system to solve the problems existing in the traditional recommendation algorithm based on machine learning (ML), and combine the existing mainstream big data (BD) to improve the execution efficiency of the model. Individualized movie recommendation system saves users the time of searching, viewing and screening, and discovers users' potential movie preferences at the same time. Most users think that the algorithm can help them find the type they want from a large quantity of film and television resources, so as to meet the needs of users and tap their potential interests.

Moviebox: A Movie Recommendation System

Abstract: In the vast world of cinema, numerous critically acclaimed movies often go unnoticed, despite their significant artistic and cultural value. Recognizing this disparity, our research endeavors to bridge the gap by developing a comprehensive movie recommendation system that highlights these "Out of the box" films. In the initial phase, we meticulously collected a bespoke dataset by scraping data from IMDb, encompassing a wide range of movies from various genres and regions. In the subsequent phase, we constructed an advanced algorithm utilizing content-based filtering techniques. This algorithm analyzes both user behavior and movie features to provide personalized recommendations that align with users' unique preferences. By embracing this approach, our research aims to enhance the discoverability and appreciation of lesser-known yet meaningful movies, empowering users to explore a diverse array of cinematic experiences.

Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System

Movies are an important part of our daily entertainment, with the global movie industry experiencing significant growth and capturing the attention of people of all ages. However, only a few movies achieve success, leading to pressure on movie production stakeholders. Therefore, researchers and moviemakers require expert systems that can accurately predict the probability of a movie's success prior to its production. Most research on predicting movie popularity has focused on post-production stages, but it's essential to predict a movie's success at an early stage to enable necessary changes to be made. To this end, a content-based movie recommendation system (RS) has been proposed that uses preliminary movie features such as genre, cast, director, keywords, and movie description. This RS output and movie rating and voting information are then used to create a new feature set, which is input into a CNN deep learning (DL) model to build a multiclass movie popularity prediction system. The study also proposes a system to predict the popularity of the upcoming movie among different age groups, dividing the audience into four categories: junior, teenage, mid-age, and senior. The study uses publicly available data from the Internet Movie Database (IMDb) and The Movie Database (TMDb). The multiclass classification model implemented in this study achieved 96.8% accuracy, outperforming all benchmark models. Overall, this study highlights the potential of predictive and prescriptive data analytics in information systems to support industry decision- making.

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.

MOVIE RECOMMENDER SYSTEM USING MACHINE LEARNING

The recommendation system of today has transformed the way we search for items of our interest through the use of an information filtering approach that predicts user preferences. A movie recommendation system named RECOM has been proposed in this paper. Based on the content-based filtering approach, it utilizes the information in the dataset, undergoes analysis, and recommends the most suitable movies for the user. The recommended movie list is ordered according to the IMDB ratings calculated by a standard formula. The system also enables users to search for movies based on their favourite actors/actresses. Developed in Python and Machine Learning, the recommender system generates recommendations through the utilization of various forms of knowledge and data about movies, such as vote count, vote average, mean, quantile, etc. Overall, the effectiveness and efficacy of movie searches have significantly increased thanks to the introduction of content-based filtering and machine learning techniques in movie recommendation systems, making it simpler for users to locate films they are likely to enjoy.

Implementing a Movie Recommendation System in Machine Learning Using Hybrid Approach

Abstract: In this paper, we have proposed a movie recommendation system using hybrid recommendation system. Today, there are a lot of recommendation systems available which are practically implemented in various websites and mobile apps. Variety exists in types of recommendation systems, user interfaces but most importantly, the accuracy of the recommendation systems. Determining a user’s possible future preference of movie or TV shows to watch is a complex task which requires a lot of relevant user data such as watch history of user, genres liked by the user, favorite actor or director, etc. Hence, the aim of this proposed system is to refine the search engine and make it more enhanced and accurate in terms of prediction. The system recommends the movies graphically based on both, user preference and similarity of individual user with other users. It also shows top rated movies worldwide and updates the recommendation after every choice of movie or show by the user

Adopting Machine Learning in Demographic Filtering for Movie Recommendation System

In this era of big data explosion, humans widely use the movie recommendation system as an information tool. There are two common issues found in the machine learning movie recommendation system that is still undeniable: first, cold start, and second, data sparsity. To minimize the problems, a research study is conducted to find a decision-making algorithm to solve the complex start problem in a movie recommendation system with precise parameters. It involves the implementation of the proposed demographics filtering technique with the k-means clustering method. The research findings present the effects of demographic filtering for movie recommendations. Demographic filtering can group users into clusters based on gender, age group, and occupation. The clusters distribution representative group based on the top 100 results of the experiment. The user with the least distance to the cluster center is chosen as the usual group in that cluster. Three clusters were experimented: Cluster 0, Cluster 1, and Cluster 2. Cluster 0 has a representative group of male, college, or graduate students aged 25 to 34. Cluster 1 has a representative group of females, executive or managerial, aged 25 to 34. Cluster 2 has a representative group of males, sales or marketing aged 35 to 44. It is shown that user from different collection has various preferred movie genre. The preferred movie genre in Cluster 0 is action, adventure, comedy, drama, and war. Cluster 1 has preferred comedy, crime, drama, horror, romance, and sci-fi movie genres. Cluster 2 has chosen action, comedy, drama, film-noir, mystery, and thriller movie genres. This research has contributed to the demographic filtering studies as an alternative solution for future technical development work.

Content-Based Movie Recommendation System: An Enhanced Approach to Personalized Movie Recommendations

With the exponential growth of digital media platforms and the vast amount of available movie content, users are often overwhelmed when selecting movies that match their preferences. Recommender systems have emerged as an effective solution to assist users in discovering relevant and enjoyable movies. Among these systems, content-based recommendation approaches have gained popularity due to their ability to recommend items based on the content characteristics of movies, such as genres, actors, directors, and plot summaries. The first stage of our system involves the collection and preprocessing of movie metadata from various sources, including genres, actors, directors, and plot summaries. Feature extraction techniques are applied to transform the textual information into meaningful representations that capture the essential characteristics of each movie. Next, a content-based filtering algorithm is employed to compute similarity scores between the user's movie preferences and the extracted features of the available movies. The proposed approach contributes to the advancement of movie recommendation systems and has the potential to enhance user engagement and satisfaction in movie selection.

Algorithm Improvement of Movie Recommendation System based on Hybrid Recommendation Algorithm

In recent years, the Internet has developed rapidly, and in the face of thousands of data and information, it has become very critical for users to find the information that is of high value to them in the mass of information, and the recommendation system is one of the most effective ways to solve this information overload phenomenon. In this paper, the current movie recommendation algorithm is improved by using an item-based collaborative filtering algorithm for the similarity measure of items in the item-based recommendation process; In the recommendation process, two more applicable recommendation methods are considered: collaborative filtering content-based recommendation and matrix decomposition-based recommendation. It saves users time in searching, viewing and filtering, while discovering information about their potential movie preferences.