Movie Recommendation System Research Articles

A social hybrid recommendation system using LSTM and CNN

AbstractWith the ever‐increasing use of Internet and social networks that generate a vast amount of information, there is a serious need for recommendation systems. In this article, we propose a recommender system utilizing deep neural networks that simultaneously considers both the users' ratings to the movies and the visual features of the movie poster and trailer. For this purpose, a hybrid movie recommender system, RSLCNet, has been developed using CNN and LSTM architectures. The proposed system considers the dynamics of users' interests in the collaborative filtering engine using the LSTM network that receives user‐rating sequences. In the content‐based filtering engine, utilizing CNN, the visual features of movie posters and trailers are extracted, and along with the actors and the directors, similar movies are recommended to the user. Moreover, each user's social influence is calculated employing the social information available on the user's Twitter account and used in the average movie rating to improve the effectiveness of the content‐based filtering part. The required datasets have been collected from MovieTweetings, Mise‐en‐scène, and OMDB. The evaluation results show that the accuracy and effectiveness of the proposed approach have been improved in terms of MAE and RMSE compared to the best available methods.

Movie recommendation and sentiment analysis using machine learning

In the modern world, where technology is at the forefront of every industry, there has been an overload of information and data. Thus, a recommendation system comes in handy to deal with this large volume of data and filter out the useful information which is fast and relevant to the user's choice. This paper describes an approach to a movie recommendation system using Cosine Similarity to recommend similar movies based on the one chosen by the user. Although the existing recommendation systems get the job done, it does not justify if the movie is worth spending time on. To enhance the user experience, this system performs sentiment analysis on the reviews of the movie chosen using machine learning. Two of the supervised machine learning algorithms Naïve Bayes (NB) Classifier and Support Vector Machine (SVM) Classifier are used to increase the accuracy and efficiency. This paper also gives a comparison between NB and SVM on the basis of parameters like Accuracy, Precision, Recall and F1 Score. The accuracy score of SVM came out to be 98.63% whereas accuracy score of NB is 97.33%. Thus, SVM outweighs NB and proves to be a better fit for Sentiment Analysis.

A hybrid big data movies recommendation model based k-nearest neighbors and matrix factorization

On the subject of broadcasting the information, finding someone’s favorite book or movie in a sea of data containing books and movies has become a crucial issue. In an era when there are so many genres and types of movies and books, the customer may find it difficult to choose which to discover in the first place. Thus, personalized recommendation systems play an important role because of the value that is attributed to movies and books nowadays, and considering that there are so many to choose from that the user may not be able to have a specific target. In this context, our proposed work, design and implement a prototype of movie recommendation system while taking into consideration the real requirement for the search of movies and books. The research of movie recommendation system by using the k-nearest neighbors approach and collaborative filtering algorithm are adopted to extract the criteria for a good use case on recommender systems. At last, the results are as what was expected as they showed that the system has a good recommendation effect.

Movie Recommendation System Using Knowledge-Based Filtering and K-Means Clustering

The movie recommender system has an important role in providing movie recommendations for users, but new users have difficulty choosing movies that are given by the recommender system because of the cold start problem. This study aims to overcome the cold start problem using a knowledge-based recommender system, i.e association rule mining using an apriori algorithm. The apriori algorithm aims to extract correlations between product itemsets, but the problem in the apriori algorithm is the large number of association rules that make the complex computation. To overcome this problem, we combine the apriori algorithm and k-means to produce more accurate recommendations, because the items are grouped before the recommendation process using the k-means algorithm. In this study, we use a dataset of movies and ratings from the Kaggle website. This study uses a minimum value of 0.5 confidence, and a minimum value of 4 lifts. To produce the best itemset in the form of antecedents and consequents of the Beauty and the Beast item with The Passion of Joan of Arc which has a value of 0.107981 support, 0.779661 confidence, 4.151695 lift

CHATBOT MOVIE RECOMMENDER SYSTEM

Collaborative filtering is one of the most powerful customization methods guiding the adaptive web Matrix Factorization and Deep Learning, Collaborative Filtering with the Restricted Boltzmann Machine (RBMS), Big Data Matrix Factorization with Spark Cluster on AWS/EC2, and Machine Learning Effective Learning Strategies. It claims that user satisfaction can only be increased if the movie recommender system understands the users' tastes and openness to new experiences, and so provides personalized recommendations. Currently, our research shows that persons with a low level of openness to experience prefer correct suggestions over fortuitous (for them) ones. Instead, persons who are open to new experiences have no potent feelings about correct or constructive (user) orders. In expansion, to look at if our chatbot movie recommender system improves user satisfaction, this study shows how much the Users' happiness with our recommender system is determined by the manner of engagement (conventional, conversational, or chatbot). According to the findings, the "Chatbot Movie Recommender System" has a beneficial influence on user happiness. KEYWORDS: recommender system, content- based, collaborative filtering, similarity, movie, user.

A hybrid approach towards movie recommendation system with collaborative filtering and association rule mining

There is a huge information stockpile available on the internet. But the available information still throws a stiff challenge to users while selecting the needed information. Such an issue can be solved by applying information filtering for locating the required information through a Recommender System. While using a RS, the users find it easy to curate and collect relevant information out of massive databanks. Though various types of RS are currently available, yet the RS developed by Collaborative Filtering techniques has proven to be the most suitable for many problems. Among the various Recommended Systems available, movie recommendation system is the most widely used one. In this system, the recommendations will be made based on the similarities in the characteristics as exhibited by users / items. The movie recommendation system contains a huge list of user objects and item objects. This paper combines Collaborative Filtering Technique with association rules mining for better compatibility and assurance while delivering better recommendations. Hence, in the process, the produced recommendations can be considered as strong recommendations. The hybridization involving both collaborative filtering and association rules mining can provide strong, high-quality recommendations, even when enough data is unavailable. This article combines various recommendations for creating a movie recommendation system by using common filtering techniques and data mining techniques

Using Movie Genres in Neural Network Based Collaborative Filtering Movie Recommendation System to Reduce Cold Start Problem

In a recommendation system, a cold start problem occurs when the system is unable to make any inferences for users or items for which it has not yet acquired adequate data. This research proposes a modified version of the Neural Collaborative Filtering model that has revolutionized collaborative filtering recommendation system with its implementation of neural network in the model. The proposed model is called Neural Collaborative Filtering Modified (NCFM). NCFM addresses the cold start problem by reducing the model dependency on users and items interactions with the integration of item feature (movie genres in this case) before the concatenation step in the NCF model. Hit ratio@10 and normalized discounted cumulative gain@10 were used as the evaluation method because they closely represents real-world recommendation scenario. Performance of both models were tested and compared using two movie rating dataset MovieLens1M provided by GroupLens and a modified version of MovieLens1M with significantly less amount of data to simulate the cold start problem called MovieLensCold. The results shows that NCFM can outperforms NCF by more than two times over or around 133% performance increase on MovieLensCold and around 15% increase on MovieLens1M dataset. Keywords— movie recommendation system, cold start problem, collaborative filtering, NCFM, neural network based recommendation system

Face Emotion Recognition based Recommendation System

Face recognition technology has gotten a lot of press because of its wide range of applications and market potential. It is used in a variety of fields, including surveillance systems, digital video editing, and other technical advancements. In the fields of tourism, music, video, and film, these systems have overcome the burden of irrelevant knowledge by taking into account user desires and emotional states. Advice systems, emotion recognition, and machine learning are proposed as thematic categories in the analysis. Our vision is to develop a method for recommending new content that is based on the emotional reactions of the viewers. Music is a form of art that is thought to have a stronger connection to a person's emotions. It has the unique potential to boost one's mood, and video streaming services are becoming more prevalent in people's lives, necessitating the development of better video recommendation systems that respond to their users in a customised manner. Furthermore, many users will believe that travel would be a method to help them cope with their ongoing emotions. Our project aims to create a smart travel recommendation system based on the user's emotional state. This project focuses on developing an efficient music, video, movie, and tourism recommendation system that uses Facial Recognition techniques to assess the emotion of users. The system's overall concept is to identify facial expression and provide music, video, and movie recommendations based on the user's mood.

Movie Recommendation System

Abstract: Now a day’s recommendation system has changed the style of searching the things of our interest. This is information filtering approach that is used to predict the preference of that user. The most popular areas where recommender system is applied are books, news, articles, music, videos, movies etc. In this paper we have proposed a movie recommendation system named MOVREC. It is based on collaborative filtering approach that makes use of the information provided by users, analyzes them and then recommends the movies that is best suited to the user at that time. The recommended movie list is sorted according to the given to these movies by previous users and it uses K-means algorithm for this purpose. MOVREC also help users to find the movies of their choices based on the movie experience of other users in efficient and effective manner without wasting much time in useless browsing. This system has been developed in PHP using Dreamweaver 6.0 and Apache Server 2.0. The presented recommender system generates recommendations using various types of knowledge and data about users, the available items, and previous transactions stored in customized databases. The user can then browse the recommendations easily and find a movie of their choice. Keywords: Recommender System, Machine Learning, Collaborative Filtering, Content based Filtering

SGCF: Inductive Movie Recommendation System with Strongly Connected Neighborhood Sampling

User and item embeddings are key resources for the development of recommender systems. Recent works has exploited connectivity between users and items in graphs to incorporate the preferences of local neighborhoods into embeddings. Information inferred from graph connections is very useful, especially when interaction between user and item is sparse. In this paper, we propose graphSAGE Collaborative Filtering (SGCF), an inductive graph-based recommendation system with local sampling weight. We conducted an experiment to investigate recommendation performance for SGCF by comparing its performance with baseline and several SGCF variants in Movielens dataset, which are commonly used as recommendation system benchmark data. Our experiment shows that weighted SGCF perform 0.5% higher than benchmark in NDCG@5 and NDCG@10, and 0.8% in NDCG@100. Weighted SGCF perform 0.79% higher than benchmark in recall@5, 0.4% increase for recall@10 and 1.85% increase for recall@100. All the improvements are statistically significant with p-value 0.05.

Neural model based collaborative filtering for movie recommendation system

Neural model based collaborative filtering for movie recommendation system

Improving Graph-Based Movie Recommender System Using Cinematic Experience

With the advent of many movie content platforms, users face a flood of content and consequent difficulties in selecting appropriate movie titles. Although much research has been conducted in developing effective recommender systems to provide personalized recommendations based on customers’ past preferences and behaviors, not much attention has been paid to leveraging users’ sentiments and emotions together. In this study, we built a new graph-based movie recommender system that utilized sentiment and emotion information along with user ratings, and evaluated its performance in comparison to well known conventional models and state-of-the-art graph-based models. The sentiment and emotion information were extracted using fine-tuned BERT. We used a Kaggle dataset created by crawling movies’ meta-data and review data from the Rotten Tomatoes website and Amazon product data. The study results show that the proposed IGMC-based models coupled with emotion and sentiment are superior over the compared models. The findings highlight the significance of using sentiment and emotion information in relation to movie recommendation.

A Matrix Decomposition Model Based on Feature Factors in Movie Recommendation System

Matrix Factorization (MF) is one of the most successful Collaborative Filtering (CF) techniques used in recommender systems due to its effectiveness and ability to deal with very large user-item rating matrix. Among them, matrix decomposition method mainly uses the interactions records between users and items to predict ratings. Based on the characteristic attributes of items and users, this paper proposes a UISVD++ model that fuses the type attributes of movies and the age attributes of users into MF framework. Project and user representations in MF are enriched by projecting each user's age attribute and each movie's type attribute into the same potential factor space as users and items. Finally, the MovieLens-100K and MovieLens-1M datasets were used to compare with the traditional SVD++ and other models. The results show that the proposed model can achieve the best recommendation performance and better predict user ratings under all backgrounds.

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

The movie is one of the integral components of our everyday entertainment. The worldwide movie industry is one of the most growing and significant industries and seizing the attention of people of all ages. It has been observed in the recent study that only a few of the movies achieve success. Uncertainty in the sector has created immense pressure on the film production stakeholder. Moviemakers and researchers continuously feel it necessary to have some expert systems predicting the movie success probability preceding its production with reasonable accuracy. A maximum of the research work has been conducted to predict the movie popularity in the post-production stage. To help the movie maker estimate the upcoming film and make necessary changes, we need to conduct the prediction at the early stage of movie production and provide specific observations about the upcoming movie. This study has proposed a content-based (CB) movie recommendation system (RS) using preliminary movie features like genre, cast, director, keywords, and movie description. Using RS output and movie rating and voting information of similar movies, we created a new feature set and proposed a CNN deep learning (DL) model to build a multiclass movie popularity prediction system. We also proposed a system to predict the popularity of the upcoming movie among different audience groups. We have divided the audience group into four age groups junior, teenage, mid-age and senior. This study has used publicly available Internet Movie Database (IMDb) data and The Movie Database (TMDb) data. We had implemented a multiclass classification model and achieved 96.8% accuracy, which outperforms all the benchmark models. This study highlights the potential of predictive and prescriptive data analytics in information systems to support industry decisions.

Ensemble Learning Based Collaborative Filtering with Instance Selection and Enhanced Clustering

Recommender system is a tool to suggest items to the users from the extensive history of the user's feedback. Though, it is an emerging research area concerning academics and industries, where it suffers from sparsity, scalability, and cold start problems. This paper addresses sparsity, and scalability problems of model-based collaborative recommender system based on ensemble learning approach and enhanced clustering algorithm for movie recommendations. In this paper, an effective movie recommendation system is proposed by Classification and Regression Tree (CART) algorithm, enhanced Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm and truncation method. In this research paper, a new hyper parameters tuning is added in BIRCH algorithm to enhance the cluster formation process, where the proposed algorithm is named as enhanced BIRCH. The proposed model yields quality movie recommendation to the new user using Gradient boost classification with broad coverage. In this paper, the proposed model is tested on Movielens dataset, and the performance is evaluated by means of Mean Absolute Error (MAE), precision, recall and f-measure. The experimental results showed the superiority of proposed model in movie recommendation compared to the existing models. The proposed model obtained 0.52 and 0.57 MAE value on Movielens 100k and 1M datasets. Further, the proposed model obtained 0.83 of precision, 0.86 of recall and 0.86 of f-measure on Movielens 100k dataset, which are effective compared to the existing models in movie recommendation.

Three-way Naive Bayesian collaborative filtering recommendation model for smart city

Smart city construction has penetrated all aspects of human life. People can continue to feel the convenience of life brought by new technologies and new services. In order to further optimize the smart city system, aiming at the sparsity of scoring data and the difficulty of two-way decisions to deal with uncertain decisions in the recommendation algorithm, a three-way Naive Bayesian Collaborative Filtering Recommendation model (3NBCFR) for smart city is constructed by integrating Naive Bayesian, three-way decisions and collaborative filtering algorithm. Firstly, we consider the influence of item attributes on user scoring. Naive Bayesian classifier is used to score unrated items, which can effectively fill in the missing values in the score matrix and solve the problem of data sparsity. Secondly, to ensure sustainable recommendations for uncertain goals, the three-way decisions is introduced into the collaborative filtering recommendation system, three-way recommendation rules are formulated, and 3NBCFR is constructed. Finally, the model is applied to the movie recommendation system. The experiment on Movielens shows that compared with the traditional collaborative filtering recommendation algorithm, 3NBCFR algorithm reduces the recommendation cost and improves the recommendation quality. At the same time, it lays a foundation for promoting the construction of smart city.

RECOMMENDATION ALGORITHM USING DATA CLUSTERING

Recommender systems play a vital role in the marketing of various goods and services. Despite the intensive growth of the theory of recommendation algorithms and a large number of their implementations, many issues remain unresolved; in particular, scalability, quality of recommendations in conditions of sparse data, and cold start. A modified collaborative filtering algorithm based on data clustering with the dynamic determination of the number of clusters and initial centroids has been developed. Data clustering is performed using the k-means method and is applied to group similar users aimed at increase of the quality of the recommendation results. The number of clusters is calculated dynamically using the silhouette method, the determination of the initial centroids is not random, but relies on the number of clusters. This approach increases the performance of the recommender system and increases the accuracy of recommendations since the search for recommendations will be carried out within one cluster where all elements are already similar. Recommendation algorithms are software-implemented for the movie recommendation system. The software implementation of various methods that allow the user to receive a recommendation for a movie meeting their preferences is carried out: a modified algorithm, memory and neighborhood-based collaborative filtering methods. The results obtained for input data of 100, 500 and 2500 users under typical conditions, data sparsity and cold start were analyzed. The modified algorithm shows the best results – from 35 to 80 percent of recommendations that meet the user's expectations. The drop in the quality of recommendations for the modified algorithm is less than 10 per cent when the number of users increases from 100 to 2500, which indicates a good level of scalability of the developed solution. In the case of sparse data (40 percent of information is missing), the quality of recommendations is 60 percent. A low quality (35 percent) of recommendations was obtained in the case of a cold start – this case needs further investigation. Constructed algorithms can be used in rating recommender systems with the ability to calculate averaged scores for certain attributes. The modified recommendation algorithm is not tied to this subject area and can be integrated into other software systems.

An Emotion-Based Design Approach to Tackle Cold Start Challenge of Movie Recommendation System

An Emotion-Based Design Approach to Tackle Cold Start Challenge of Movie Recommendation System

A Movie Recommendation System Based on A Convolutional Neural Network

Abstract: Due to its vast applications in several sectors, the recommender system has gotten a lot of interest and has been investigated by academics in recent years. The ability to comprehend and apply the context of recommendation requests is critical to the success of any current recommender system. Nowadays, the suggestion system makes it simple to locate the items we require. Movie recommendation systems are intended to assist movie fans by advising which movie to see without needing users to go through the time-consuming and complicated method of selecting a film from a large number of thousands or millions of options. The goal of this research is to reduce human effort by recommending movies based on the user's preferences. This paper introduces a method for a movie recommendation system based on a convolutional neural network with individual features layers of users and movies performed by analyzing user activity and proposing higher-rated films to them. The proposed CNN approach on the MovieLens-1m dataset outperforms the other conventional approaches and gives accurate recommendation results. Keywords: Recommender system, convolutional neural network, movielens-1m, cosine similarity, Collaborative filtering, content-based filtering.

Edge data based trailer inception probabilistic matrix factorization for context-aware movie recommendation

The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data. The visual information in the movie trailer is a significant part of the movie recommender system. However, due to the complexity of visual information extraction, data sparsity cannot be remarkably alleviated by merely using the rough visual features to improve the rating prediction accuracy. Fortunately, the convolutional neural network can be used to extract the visual features precisely. Therefore, the end-to-end neural image caption (NIC) model can be utilized to obtain the textual information describing the visual features of movie trailers. This paper proposes a trailer inception probabilistic matrix factorization model called Ti-PMF, which combines NIC, recurrent convolutional neural network, and probabilistic matrix factorization models as the rating prediction model. We implement the proposed Ti-PMF model with extensive experiments on three real-world datasets to validate its effectiveness. The experimental results illustrate that the proposed Ti-PMF outperforms the existing ones.