Movie Recommendation Research Articles

Movie Recommendation System with Sentiment Analysis

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

Benchmarking for Recommender System (MFRISE)

The advent of the internet age offers overwhelming choices of movies and shows to viewers which create need of comprehensive Recommendation Systems (RS). Recommendation System will suggest best content to viewers based on their choice using the methods of Information Retrieval, Data Mining and Machine Learning algorithms. The novel Multifaceted Recommendation System Engine (MFRISE) algorithm proposed in this paper will help the users to get personalized movie recommendations based on multi-clustering approach using user cluster and Movie cluster along with their interaction effect. This will add value to our existing parameters like user ratings and reviews. In real-world scenarios, recommenders have many non-functional requirements of technical nature. Evaluation of Multifaceted Recommendation System Engine must take these issues into account in order to produce good recommendations. The paper will show various technical evaluation parameters like RMSE, MAE and timings, which can be used to measure accuracy and speed of Recommender system. The benchmarking results also helpful for new recommendation algorithms. The paper has used MovieLens dataset for purpose of experimentation. The studied evaluation methods consider both quantitative and qualitative aspects of algorithm with many evaluation parameters like mean squared error (MSE), root mean squared error (RMSE), Test Time and Fit Time are calculated for each popular recommender algorithm (NMF, SVD, SVD++, SlopeOne, Co- Clustering) implementation. The study identifies the gaps and challenges faced by each above recommender algorithm. This study will also help researchers to propose new recommendation algorithms by overcoming identified research gaps and challenges of existing algorithms.

Intra-list similarity and human diversity perceptions of recommendations: the details matter

The diversity of the generated item suggestions can be an important quality factor of a recommender system. In offline experiments, diversity is commonly assessed with the help of the intra-list similarity (ILS) measure, which is defined as the average pairwise similarity of the items in a list. The similarity of each pair of items is often determined based on domain-specific meta-data, e.g., movie genres. While this approach is common in the literature, it in most cases remains open if a particular implementation of the ILS measure is actually a valid proxy for the human diversity perception in a given application. With this work, we address this research gap and investigate the correlation of different ILS implementations with human perceptions in the domains of movie and recipe recommendation. We conducted several user studies involving over 500 participants. Our results indicate that the particularities of the ILS metric implementation matter. While we found that the ILS metric can be a good proxy for human perceptions, it turns out that it is important to individually validate the used ILS metric implementation for a given application. On a more general level, our work points to a certain level of oversimplification in recommender systems research when it comes to the design of computational proxies for human quality perceptions and thus calls for more research regarding the validation of the corresponding metrics.

An optimal context-aware content-based movie recommender system using genetic algorithm: a case study on MovieLens dataset

ABSTRACT Most research on movie recommender systems has been conducted with Collaborative Filtering (CF) methods. The lack of sufficient information about users’ interests in bootstrapping is one of the most critical problems of the CF method. Using a content-based filtering method can mitigate some of these problems. On the other hand, recent research has proven that utilising contextual information about the movie, such as genre, actors, and cast, can increase the efficiency of the recommender system. This paper uses a combined Genetic Algorithm (GA) and content-based filtering to find the best combination of genre, cast, and crew weights. We first convert each movie’s contextual information into a descriptive vector, including cast, crew, and genre. Then, we calculate the distance between each pair of sentence vectors using two separate approaches, fully connected and metadata-based. Finally, with GA, we tune the weight of each of the contextual information of movies to maximise the recommender system’s efficiency. Performance evaluation on the well-known MovieLens dataset shows that GA can improve the Precision@k criterion by producing fewer, more accurate recommendations. Weight adjustment by GA improves the F-Measure metric by approximately 58%. This, in turn, can improve Precision and Recall metrics. Also, the GA offers a higher correct recommendation rate than other methods.

Forecasting movie rating using k-nearest neighbor based collaborative filtering

<p><span lang="EN-US">Expressing reviews in the form of sentiments or ratings for item used or movie seen is the part of human habit. These reviews are easily available on different social websites. Based on interest pattern of a user, it is important to recommend him the items. Recommendation system is playing a vital role in everyone’s life as demand of recommendation for user’s interest increasing day by day. Movie recommendation system based on available ratings for a movie has become interesting part for new users. Till today, a lot many recommendation systems are designed using several machine learning algorithms. Still, sparsity problems, cold start problem, scalability, grey sheep problem are the hurdles for the recommendation systems that must be resolved using hybrid algorithms. We proposed in this paper, a movie rating system using a k-nearest neighbor (KNN-based) collaborative filtering (CF) approach. We compared user’s ratings for different movies to get top K users. Then we have used this top K set to find missing ratings by user for a movie using CF. Our proposed system when evaluated for various criteria shows promising results for movie recommendations compared with existing systems.</span></p>

Sustainable Development: A Semantics-aware Trends for Movies Recommendation System using Modern NLP

Abstract Recommendation systems are an important feature in the proposed virtual life, where users are often stuck with choices most of the time and need help to be able to find what they are looking for. In this work, contentbased techniques have been employed in the proposed recommender system in two ways, a deep review for content and features contents such as (cast, crew, keywords, and genres) has been conducted. A preprocessing stage using TF-IDF and CountVectorizer methods have been employed efficiently to prepare the dataset for any similarity measurements. Cosine similarity algorithm has been employed as well with and without sigmoid and linear kernals. The achieved result proves that similarities between movies using TF-IDF with - Cosine similarity (sigmoid kernel) overcomes the TF-IDF with - Cosine similarity (linear_kernel) and Cosine similarity with CountVectorizer in collaborative filtering. The accuracy values of different machine learning models are validated with K-fold Cross Validator techniques. The performance evaluation has been measured using ROOT Mean Square Error and Mean Absolute Error. Five Machine learning algorithms (NormalPredictor, SVD, KNNBasic (with k=20 and K=10), KNNBasic (with sim_options), and NMF (in several rating scales)). Accuracies are finally been validated with 3 folds from each validator. The best achieved RMSE and MAE scores are using SVD (RMSE = 90%) and (MAE = 69%), followed by KNNBasic (with sim_options, K= 20), NMF, KNNBasic (K=20), KNNBasic (K=10), ending with KNNBasic(sim_options, K= 10). Keywords: Recommendation System, Sustainable Development, Artificial Intelligence, Collaborative Filtering, Content-Based, Cosine Similarity, Movies Recommendation, NLP, Machine Learning Application.

Personalized Relationships-Based Knowledge Graph for Recommender Systems with Dual-View Items

The knowledge graph has received a lot of interest in the field of recommender systems as side information because it can address the sparsity and cold start issues associated with collaborative filtering-based recommender systems. However, when incorporating entities from a knowledge graph to represent semantic information, most current KG-based recommendation methods are unaware of the relationships between these users and items. As such, the learned semantic information representation of users and items cannot fully reflect the connectivity between users and items. In this paper, we present the PRKG-DI symmetry model, a Personalized Relationships-based Knowledge Graph for recommender systems with Dual-view Items that explores user-item relatedness by mining associated entities in the KG from user-oriented entity view and item-oriented entity view to augment item semantic information. Specifically, PRKG-DI utilizes a heterogeneous propagation strategy to gather information on higher-order user-item interactions and an attention mechanism to generate the weighted representation of entities. Moreover, PRKG-DI provides a score feature as a filter for individualized relationships to evaluate users’ potential interests. The empirical results demonstrate that our approach significantly outperforms several state-of-the-art baselines by 1.6%, 2.1%, and 0.8% on AUC, and 1.8%, 2.3%, and 0.8% on F1 when applied to three real-world scenarios for music, movie, and book recommendations, respectively.

Knowledge-aware hierarchical attention network for recommendation

Knowledge graphs have been introduced into recommender systems due to the rich connectivity information. Many knowledge-aware recommendation methods use graph neural networks (GNNs) to capture the high-order structural and semantic information of knowledge graphs. However, previous GNN-based methods have the following limitations: (1) they fail to make full use of the neighborhood information of entities and (2) they ignore the importance of user interaction sequences on reflecting user preferences. As such, these models are insufficient for generating accurate representations of users and items. In this study, we propose a Knowledge-aware Hierarchical Attention Network (KHAN) to provide better recommendation. Specifically, the proposed model mainly consists of an item encoder and a user encoder. The item encoder is equipped with a hierarchical attention network, which is used to generate entity (item) representations by carefully aggregating neighborhood information of entities. The user encoder is also designed to learn more informative user representations from user interaction sequences using multi-head self-attention. The learned user representations are then combined with user representations introduced in the item encoder through a gating mechanism to generate the final user representations. Extensive experiments on two real-world datasets about movie and restaurant recommendation demonstrate the effectiveness of our model.

Movie Reveiw Generator

This Android app will help the people to know about Semantics of the movies. In this project, we propose a Movie Recommendation System by combining Naive Bayes Algorithm with Collaborative filtering. The recommendation system is able to predict the everchanging area of interest or tastes of users about the movies. In this project, we propose a Movie Recommendation System by combining Naive Bayes Algorithm with Collaborative filtering. The recommendation system is able to predict the everchanging area of interest or tastes of users about the movies.

Towards Tangible Algorithms: Exploring the Experiences of Tangible Interactions with Movie Recommender Algorithms

Artificial Intelligence (AI) supports many of our everyday activities and decisions. However, personalized algorithmic recommendations often produce adverse experiences due to a lack of awareness, control, or transparency. While research has directed solutions on graphical user interfaces (GUIs), there are no explorations of Tangible User Interfaces (TUIs) to improve the experience with such systems, despite the valid existing academic arguments in favor of this exploration. Therefore, centering on transparency and control, we analyzed how 18 users of movie recommender systems perceived four different TUIs using individual co-design sessions and post-interview questionnaires. Through thematic analysis, we identified seven design considerations while designing TUIs to interact with algorithmic movie recommender systems: (1) Distinctions between TUIs and GUIs; (2) TUIs replacing predominant interfaces; (3) Preference for single-device TUIs; (4) The relevance of granular control for TUIs; (5) Apparent transparency limitations of TUIs; (6) TUIs and algorithmic social computing; and (7) Overview of specific design choices, including advantages and disadvantages of soft, hard, rounded, cubic, and humanoid interfaces. These findings inspired Recffy: the first functional TUI designed to enhance awareness and control in personalized movie recommendations. Based on this study, we propose the concept of Tangible Algorithms: TUIs dedicated to enhancing the interaction of algorithmic systems and their profiling processes or decisions in a specific context. Furthermore, we describe the relevance of tangible algorithms and design guidelines to promote them in diverse AI contexts. Finally, we invite the HCI and CSCW community to continue exploring tangible algorithms to address the interaction with algorithmic systems, including the collaborative and social computing dynamics they can promote in diverse AI contexts.

Movie Recommendation Using Clustering and Nearest Neighbour

Due to the abundance of items available and online information, a user cannot easily choose which product is ideal for him. A recommender system assists users in finding what is best for them. A recommender system uses information about a user's activity. It uses it to suggest movies to users based on their individual interests. This paper provides an overview of a recommender system that uses the K-means and KNN algorithm. Without wasting time exploring, both algorithms rapidly and effectively recommend movies to customers based on their likes. There are many uses for recommender systems worldwide. K-means algorithm is used to get beyond some of the restrictions of content-based and collaborative work. The K-means algorithm creates clusters of individuals with similar interests, and KNN, which includes nearest neighbors, recommends movies to each group. This is used in well-known fields like books, news, music, videos, and movies, among others. These search engines allow users to find movies of their choice. K-mean, KNN, and hybrid algorithms have been covered in this study. K-means algorithm results based on metrics like "average Genre Rating" and "User Movie Rating". The RMSE feature has been used to KNN algorithm. A hybrid algorithm combines the two algorithms mentioned above. K-means is given an input, and the output of this method serves as the input for the KNN algorithm, which is more accurate than both K-means and KNN.

A probabilistic perspective on nearest neighbor for implicit recommendation

Over the past years, the recommender systems community invented several novel approaches that reached better and better prediction accuracy. Sequential recommendation, such as music recommendation, has seen large improvements from neural network-based models such as recurrent neural networks or transformers. When no sequential information is available or not relevant, such as for book, movie, or product recommendation, however, the classic k-nearest neighbor algorithm appears to remain competitive, even when compared to much more sophisticated methods. In this paper, we attempt to explain the inner workings of the nearest neighbor using probabilistic tools, treating similarity as conditional probability and presenting a novel model for explaining and removing popularity bias. First, we provide a probabilistic formulation of similarity and the classic prediction formula. Second, by modeling user behavior as a combination of personal preference and global influence, we are able to explain the presence of popularity bias in the predictions. Finally, we utilize Bayesian inference to construct a theoretically grounded variant of the widely used inverse frequency scaling, which we use to mitigate the effect of popularity bias in the predictions. By replacing the formerly ad hoc choices of nearest neighbor with probabilistically founded counterparts, we are able to improve prediction accuracy over a variety of data sets and gain an increased understanding of the theory behind the method.

Towards more trustworthy predictions: A hybrid evidential movie recommender system

Recommender Systems (RSs) are considered as popular tools that have revolutionized the e-commerce and digital marketing. Their main goal is predicting the users’ future preferences and providing accessible and personalized recommendations. However, uncertainty can spread at any level throughout the recommendation process, which may affect the results. In fact, the ratings given by the users are often unreliable. The final provided predictions itself may also be pervaded with uncertainty and doubt. Obviously, the reliability of the predictions cannot be fully certain and trustworthy. For the system to be effective, recommendations must inspire trust in the system and provide reliable and credible recommendations. The user may speculate about the uncertainty pervaded behind the given recommendation. He could tend to a reliable recommendation offering him a global overview about his preferences rather than an inappropriate one that contradicts his activities and objectives. While such imperfection cannot be ignored, traditional RSs are rarely able to deal with the uncertainty spreading around the prediction process, which may affect the credibility, the transparency and the trustworthiness of the current RS. Thus, in this paper, we opt for the uncertain framework of the belief function theory (BFT), which allows us to represent, quantify and manage imperfect evidence. By using the BFT, the users’ preferences and the interactions between the neighbors can be represented under uncertainty. Evidence from different information sources can then be combined leading to more reliable results. The proposed approach is a hybrid evidential movie RS that uses different data sources and delivers a personalized user-interface allowing a global overview of the possible future preferences. This representation would increase the users’ confidence towards the system as well as their satisfaction. Experiments are performed on MovieLens and their additional features provided by the Internet Movie Database (IMDb) and Rotten Tomatoes. The new approach achieves promising results compared to traditional approaches in terms of MAE, NMAE and RMSE. It also reaches interesting Precision, Recall and F-measure values of respectively, 0.782, 0.792 and 0.787.

Netflix Movie Recommendation System Using Collaborative Filtering With K-Means Clustering Method on Twitter

Nowadays, the development of technology is very rapid, so watching movies at home has become a means of entertainment. Netflix is one of the platforms for watching movies and provides various movie titles. However, because of the many movie titles, it makes it difficult for users to determine the movie they want to watch. The solution to this problem is to provide a recommendation system that can provide movie recommendations to watch. Collaborative filtering is a method that exists in the recommendation system by providing recommendations based on the ratings given by other users. Collaborative filtering is divided into two, namely based on items (item-based) and based on users (user-based). Twitter is a social media used to write posts called tweets. For this system, tweets serve as data that will be processed into ratings. This research was conducted using k-means clustering with collaborative filtering and collaborative filtering only. By using a dataset obtained from Twitter by crawling data and added with ratings from IMDb, Rotten Tomatoes, and Metacritic. Which resulted in a dataset with 35 users, 785 movie titles, and 6184 reviews. Then preprocessing the data with text processing, polarity, and labeling. And get the dataset that will be used for this experiment. The results of this research test show that k-means clustering with collaborative filtering gets the best results with the best prediction of 2.8466, getting an MAE value of 0.5029, and an RMSE value of 0.6354

Automatic Movie Tag Generation System for Improving the Recommendation System

As the content industry develops, the demand for movie content is increasing. Accordingly, the content industry is actively developing super-personalized recommendation systems that match consumers’ tastes. In this paper, we study automatic generation of movie tags to improve the movie recommendation system. We extracted background sounds from movie trailer videos, analyzed the sounds using STFT (Short-Time Fourier transform) and major audio attribute features, and created a genre prediction model. The experimental results show that the pre-collected dataset and the data extracted via the model are similar when compared. In this research, we suggest the methodology of an automatic genre prediction system for movie information from trailer videos. This will help to reduce the time and effort for metadata generation for a recommendation system.

Movie Recommender System using Machine Learning

In this research, we propose a movie recommender system that can recommend movies to both new and existing customers. It searches movie databases for all of the relevant data, such as popularity and beauty that is required for a recommendation. We apply both content-based and collaborative filtering and evaluate their advantages and disadvantages. To build a system that delivers more exact movie recommendations, we employ hybrid filtering, which is a combination of the outcomes of these two processes. The recommendation engines are also used for business purposes and to make strategies for organizations. Due to the growing demands of customers and user’s recommendation systems plays a huge role. These recommender systems also help us to utilize our time in the busy world by giving us more relevant searches. These systems are generally used with the movie’s websites or with many commercial applications and are of great use. This type of recommendation system can be also used for precise results. It will make movies suggestions more relevant as per the need of the users.

Wingman: A New Movie Recommendation System Algorithm

In this rising new era, the world is growing faster and so are we. People nowadays need effortless services, and recommendation system is one of them. They are present and used everywhere, whether it's advertising, e-commerce or any other domain. Companies such as Netflix, YouTube, Amazon, Spotify rely on recommendation systems to boost their sales. Moreover, it increases the engagement of users with a particular app or website as it tends to show more relevant individualized content. It basically takes the user’s past history as an input and then using filtering techniques it predicts the most suitable item/content for the user as an output. The main objective of this paper is to predict movies most desired by the user with as much accuracy as possible. In this paper, I will provide a brief description of various filtering methods and the particular techniques that I used to achieve the desired result. I have used Tf-Idf to quantify the overview of the movies, then did a pairwise similarity for the Content Based Filtering. This allows the user to watch movies based on their watch history according to the plot of the movies. For Collaborative Filtering, I have used Matrix Factorization to generate real-time recommendations based on user-movie rating matrix. Correlation Coefficient is used to calculate the score. The higher the score, the more likely the movie is liked by the user. I have used the voting system for Hybrid Filtering. Weighted Rating Score is calculated with the help of the votes given to the movies by the users. Then a popularity feature is also used to generate more meaningful movie recommendations. MinMax Scalar is used to normalize the data, and then fifty percent priority is given to both Weighted Rating Score and popularity and a final score is calculated. Using this final score, popular or top movies are generated. The databases used are retrieved from MovieLens and Kaggle. Consequently, my proposed system will generate recommendations based on the plot of the movie using tf-idf and cosine similarity, based on user-movie rating matrix and correlation coefficient, based on voting and popularity basis.

Evolutionary Search With Multiview Prediction for Dynamic Multiobjective Optimization

Dynamic multiobjective optimization problem (DMOP) denotes the multiobjective optimization problem which varies over time. As changes in DMOP may exist some patterns that are predictable, to solve DMOP, a number of research efforts have been made to develop evolutionary search with prediction approaches to estimate the changes of the problem. A common practice of existing prediction approaches is to predict the change of Pareto-optimal solutions (POS) based on the historical solutions obtained in the decision space. However, the change of a DMOP may occur in both decision and objective spaces. Prediction only in the decision space thus may not be able to give the proper estimation of the problem change. Taking this cue, in this article, we propose an evolutionary search with multiview prediction for solving DMOP. In contrast to existing prediction methods, the proposed approach conducts prediction from the views of both decision and objective spaces. To estimate dynamic changes in DMOP, a kernelized autoencoding model is derived to perform the multiview prediction in a reproducing kernel Hilbert space (RKHS), which holds a closed-form solution. To examine the performance of the proposed method, comprehensive empirical studies on the commonly used DMOP benchmarks, as well as a real-world case study on the movie recommendation problem, are presented. The obtained experimental results verified the efficacy of the proposed method for solving both benchmark and real-world DMOPs.

Personalized Recommendations of Products to Users

Many organizations utilize recommendation systems to increase their profitability and win over their customers, including Facebook, which suggests friends, LinkedIn, which promotes employment, Spotify, which recommends music, Netflix, which recommends movies, and Amazon, which recommends purchases. When it comes to movie recommendation system, suggestions are made based on user similarities (collaborative filtering) or by considering a specific user's behavior (content-based filtering) that he or she wishes to interact with. Using TF-IDF, cosine similarity method for content-based filtering, and deep learning for a collaborative approach, this study compares two movie recommendation system. The proposed systems are evaluated by calculating the precision and recall values. On a small dataset, a content-based filtering methodology had a precision of 5.6% whereas a collaborative approach had a precision of 57%. Collaborative filtering clearly worked better than content-based filtering. Future improvements involve creating a single hybrid recommendation system that combines a collaborative and content-based approach to improve the outcomes.

ENTERTAINMENT RECOMMENDATION

The purpose of a recommender system is to suggest relevant items to users. To achieve this task, there exist two major categories of methods : collaborative filtering methods and content based methods. This project is an entertainment recommendation system. This is a web application where the user can register, and login. After the login , the user will see an dashboard and in the dashboard there are 2 buttons , one is movie recommendation and the other one is music recommendation and.. In the movie recommendation page , top 15 movies based on the ratings will be there and in the music recommendation page , top 15 music based on the atingswill be there. Then the user can logoff from the application