Movie Recommendation Research Articles

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

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

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

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

Movie Recommendation System Using Optimized RNN Approach.

This paper proposes a movie recommendation system that utilizes an optimized Recurrent Neural Network (RNN) approach. The proposed system is designed to provide users with personalized movie recommendations based on their previous movie preferences and the sentiments. The system works by taking user input, analyzing their movie preferences using content-based filtering techniques, and generating a list of recommended movies. The RNN architecture used in this system is optimized using a combination of techniques such as dropout regularization, early stopping, and parameter tuning. The proposed optimization techniques aim to reduce overfitting, improve convergence speed, and increase the model's overall accuracy. To evaluate the effectiveness of the proposed approach, we conducted experiments on the Movie Lens dataset. The results indicate that the optimized RNN-based movie recommendation system outperforms other existing recommendation systems such as collaborative filtering, content-based filtering, and standard RNN models. Furthermore, the proposed system achieved a significant improvement in accuracy and provided highly personalized recommendations to users. Overall, the proposed movie recommendation system using optimized RNN approach is a promising solution for providing personalized movie recommendations to users. It can be implemented in various platforms such as movie streaming websites, social media, and other movie-related platforms to improve the user experience and increase engagement

Sentiment Analysis for Movie Recommendation

Our research proposes a movie recommendation system integrating sentiment analysis with LSTM neural networks. By extracting sentiments from user reviews, LSTM captures nuanced preferences, enhancing recommendation accuracy. Leveraging real-world movie datasets, our approach outperforms existing methods, offering personalized recommendations aligned with user sentiments. Through this fusion of NLP and deep learning, we strive to streamline movie selection, providing users with a more tailored and satisfying experience. Keywords—Sentiment Analysis, Natural Language Processing,, Long Short-Term Memory, Recurrent Neural Network

Netflix Movies Recommendation System

Netflix is a leading global entertainment company with over 247 million paying customers who can access TV series, films, and games in over 190+ countries. Many languages and genres are offered for the content. As much as they like, whenever and wherever they wish, participants are free to change their plans and play, pause, and resume viewing. With the exponential growth of content on streaming platforms like Netflix, users often face the challenge of finding relevant and enjoyable movies.The "Cold-Start" issue is a recommendation system issue wherein new users or items have little data, making it challenging to provide appropriate recommendations. In response, this paper proposes a data-driven movie recommendation system tailored for Netflix. Leveraging machine learning techniques, user preferences, and historical data, the model aims to enhance the user experience by providing personalized movierecommendations. This study explores algorithms such as collaborative filtering, content-based filtering, and hybrid models to achieve accurate and effective movie recommendations. While content-based filtering uses item features to suggest shows or movies a user has previously enjoyed, collaborative filtering examines user behavior and preferences to produce suggestions. Even with insufficient data, the system can deliver more accurate recommendations for new users or things by combining various approaches. By making recommendations based on both item features and user behavior, this hybrid approach helps to reduce the cold start issue.

A parallel quantum eigensolver for quantum machine learning

Eigensolvers have a wide range of applications in machine learning. Quantum eigensolvers have been developed for achieving quantum speedup. Here, we propose a parallel quantum eigensolver (PQE) for solving a set of machine learning problems, which is based on quantum multi-resonant transitions that simultaneously trigger multiple energy transitions in the systems on demand. PQE has a polylogarithmic cost in problem size under certain circumstances and is hardware efficient, such that it is implementable in near-term quantum computers. As a verification, we utilize it to construct a collaborative filtering quantum recommendation system and implement an experiment of the movie recommendation tasks on a nuclear spin quantum processor. As a result, our recommendation system accurately suggests movies to the user that he/she might be interested in. We further demonstrate the applications of PQE in classification and image completion. In the future, our work will shed light on more applications in quantum machine learning.

Tackling the cold start issue in movie recommendations with a refined epsilon-greedy approach

With the rapid growth of the Internet and the consequent surge in data, the current era is characterized by information overload. As the domain of data processing and storage expands, recommendation systems have become pivotal tools in navigating this deluge, assisting users in filtering through vast information landscapes. A notable segment of this is movie recommendation systems. As living standards rise, so does the demand for cinematic experiences. Enhancing and refining the methodologies of these recommendation systems is, therefore, of significant value. However, a consistent challenge is the cold start problem encountered when new users join. Without prior viewing records or preferences, these users pose a dilemma for the system: how to offer relevant recommendations without historical data? Addressing this challenge, this paper proposes a unique method grounded in the N-armed bandit model, introducing an enhanced Epsilon-greedy algorithm specifically designed for movie recommendations for such users. By adjusting dynamically based on real-time user feedback, the algorithm aims to continuously hone its recommendation quality, ensuring a consistently better user experience.

Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeans

A flood of information has occurred, making it challenging for people to find and filter their favorite items. Recommendation systems (RSs) have emerged as a solution to this problem; however, traditional Appenrecommendation systems, including collaborative filtering, and content-based filtering, face significant challenges such as data scalability, data scarcity, and the cold-start problem, all of which require advanced solutions. Therefore, we propose a ranking and enhancing sequence movie recommendation system that utilizes the combination model of deep learning to resolve the existing issues. To mitigate these challenges, we design an RSs model that utilizes user information (age, gender, occupation) to analyze new users and match them with others who have similar preferences. Initially, we construct sequences of user behavior to effectively predict the potential next target movie of users. We then incorporate user information and movie sequence embeddings as input features to reduce the dimensionality, before feeding them into a transformer architecture and multilayer perceptron (MLP). Our model integrates a transformer layer with positional encoding for user behavior sequences and multi-head attention mechanisms to enhance prediction accuracy. Furthermore, the system applies KMeans clustering to movie genre embeddings, grouping similar movies and integrating this clustering information with predicted ratings to ensure diversity in the personalized recommendations for target users. Evaluating our model on two MovieLens datasets (100 Kand 1 M) demonstrated significant improvements, achieving RMSE, MAE, precision, recall, and F1 scores of 1.0756, 0.8741, 0.5516, 0.3260, and 0.4098 for the 100 K dataset, and 0.9927, 0.8007, 0.5838, 0.4723, and 0.5222 for the 1 M dataset, respectively. This approach not only effectively mitigates cold-start and scalability issues but also surpasses baseline techniques in Top-N item recommendations, highlighting its efficacy in the contemporary environment of abundant data.

Movie Recommendation System Using KNN, Cosine Similarity and Collaborative Filtering

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

Hybrid knowledge-infused collaborative filtering for enhanced movie clustering and recommendation

This article proposes an enhanced knowledge-based collaborative filtering model for movie recommendation services to address the limitations of collaborative filtering in capturing the diverse preferences and specific characteristics of movies. The proposed model integrates external knowledge sources, such as movie plots and reviews, to enrich the recommendation process. By leveraging this additional information, the model can better understand movies’ unique features and attributes, improving recommendation accuracy and relevance. The knowledge-based features are extracted and incorporated into the collaborative filtering framework, enhancing the model’s ability to match user preferences with movie characteristics. Experiments are conducted using the MovieLens dataset to evaluate the proposed model. The MAE and RMSE metrics are employed to assess the quality of recommendations. Comparative analyses are conducted against various baseline approaches, including popularity-based, CF-based, content-based, and hybrid recommendation models. The experimental results demonstrate the effectiveness of the proposed knowledge-based collaborative filtering model. The proposed model consistently outperforms the baselines, providing more accurate and personalized recommendations.

Exploring the relationship between users characteristics and movie recommendations using a KNN-based recommender system

The central aim of this research is to elucidate the degree to which demographic variables, including but not limited to age and gender, bear on the performance of movie recommendation algorithms within film recommendation systems. Further, we endeavor to uncover any existent correlations between these user characteristics and the resultant outputs of such systems. Leveraging the expansive dataset available via MovieLens, we employ a linear regression model to ascertain the four critical variables (age, gender, occupation, and the average user rating for films previously watched) that have the most profound influence on movie recommendation algorithms. Once these salient factors have been determined, we assign their respective weights and incorporate these into a KNN algorithm. We then subject the resultant model to rigorous testing to verify the accuracy of our results and to ascertain whether the integration of these weighted elements enhances the overall precision of the movie recommendation system. While extant literature predominantly focuses on the amalgamation of KNN with other algorithms, our study charts a novel course by using linear regression. This methodology allows us to intuitively illustrate the relationship between user demographics and the movie recommendation system and enables us to evaluate whether emphasizing certain characteristics can augment the system's effectiveness. Our findings suggest that of all the user characteristics examined, the mean of users ratings for movies previously watched exerts the greatest influence on the outputs of the movie recommendation system. Moreover, incorporating weights reflective of the average user ratings across all movie features within the KNN algorithm can significantly bolster the accuracy of the resultant movie recommendations.

Dynamic matrix factorization-based collaborative filtering in movie recommendation services

Movies are a primary source of entertainment, but finding specific content can be challenging given the exponentially increasing number of movies produced each year. Recommendation systems are extremely useful for solving this problem. While various approaches exist, Collaborative Filtering (CF) is the most straightforward. CF leverages user input and historical preferences to determine user similarity and suggest movies. Matrix Factorization (MF) is one of the most popular Collaborative Filtering (CF) techniques. It maps users and items into a joint latent space, using a vector of latent features to represent each user or item. However, traditional MF techniques are static, while user cognition and product variety are constantly evolving. As a result, traditional MF approaches struggle to accommodate the dynamic nature of user-item interactions. To address this challenge, we propose a Dynamic Matrix Factorization CF model for movie recommendation systems (DMF-CF) that considers the dynamic changes in user interactions. To validate our approach, we conducted evaluations using the standard MovieLens dataset and compared it to state-of-the-art models. Our preliminary findings highlight the substantial benefits of DMF-CF, which outperforms recent models on the MovieLens-100K and MovieLens-1M datasets in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics.

Movie and Music Recommendation System based on Facial Expressions

Abstract: This paper depicts a novel approach of recommendation system created to extract user’s facial expressions and analyze it using Convolutional Neural Network. CNN is a type of artificial neural network which primarily is used for image recognition and processing by recognizing patterns in images. Initially, an inbuilt webcam will operate to detect user’s facial expression which reduces system’s cost compared to alternative methods. The main concern in existing recommendation systems is manual sorting, so to avoid it, this model is proposed. ‘Movie and Music Recommendation System based on Facial expressions’ provides a way to automatically play the music and movie without spending much time browsing it. The most common problem we all face is, whenever we hop onto third party movies and music platforms, we find tons of options and get stuck into the confusion of what to watch in terms of movies and spend half of our time surfing the options and then we decide to watch something specific. Computer vision is used to understand people in images and videos. This research project introduces an ingenious recommendation system that capitalizes on facial expressions in real time, customize movie and music suggestions through emotion analysis. Haar Cascade algorithm and CNN targets to detect facial feature detection. The database of movies is routed to IMDB site and songs to Spotify API. Training of dataset is done on FER 2013 sample images. It has an input layer, convolutional layer, dense layer and output layer. After the emotions are detected, mood classification is done (for song: tone, valence, timber; for movie: happy, surprise, sad, neutral, anger.) The model is displayed on a website created using Html and CSS with Flask as interface. This system yields better performance results. This recommendation system is exhibited on a website which is user friendly created with HTML and CSS with Flask.

Incorporating Domain-Specific Traits into Personality-Aware Recommendations for Financial Applications

The general personality traits, notably the Big-Five personality traits, have been increasingly integrated into recommendation systems. The personality-aware recommendations, which incorporate human personality into recommendation systems, have shown promising results in general recommendation areas including music, movie, and e-commerce recommendations. On the other hand, the number of research delving into the applicability of personality-aware recommendations in specialized domains such as finance and education remains limited. In addition, these domains have unique challenges in incorporating personality-aware recommendations as domain-specific psychological traits such as risk tolerance and behavioral biases play a crucial role in explaining user behavior in these domains. Addressing these challenges, this study addresses an in-depth exploration of personality-aware recommendations in the financial domain, specifically within the context of stock recommendations. First, this study investigates the benefits of deploying general personality traits in stock recommendations through the integration of personality-aware recommendations with user-based collaborative filtering approaches. Second, this study further verifies whether incorporating domain-specific psychological traits along with general personality traits enhances the performance of stock recommender systems. Thirdly, this paper introduces a personalized stock recommendation model that incorporates both general personality traits and domain-specific psychological traits as well as transaction data. The experimental results show that the proposed model outperformed baseline models in financial stock recommendations.

Hybrid collaborative filtering using matrix factorization and XGBoost for movie recommendation

Nowadays, e-commerce platforms, such as Amazon, Flipkart, Netflix and YouTube, extensively use recommender systems (RS) techniques. Collaborative filtering (CF) is used widely among all RS techniques. A CF analyzes the user’s preference from past data, like ratings, and then suggests actual items to the intended user. The existing techniques compute the similarity between users/items and predict the ratings. However, most of them indicate the user’s preference for the items using a single technique, which may produce poor results. This paper proposes a hybrid CF technique to enhance the movie recommendation (HCFMR). The HCFMR consists of two modules. The first module finds the prediction score with the help of matrix factorization (MF) and passes the prediction score as input to the prediction algorithm, i.e., extreme gradient boosting (XGBoost). The second module generates handcrafted features, such as similar users and movies, along with the user, item and global average. Finally, these features are supplied to the XGBoost to predict the rating score of the movie and recommend the topmost movie to the user. We conduct various simulations on real-world datasets to verify the effectiveness of the proposed technique against the baseline techniques. The exploratory outcomes signify that the HCFMR technique outperforms the baselines and provides a better prediction on the benchmark datasets.

Movie Recommendation System: Real-time Evaluation

In today's e-commerce time, it has become very troublesome task to find any content of online movies, songs, books , restaurants etc. A practical recommendation scheme now very helpful and important. Nowadays, movie recommendation system are also available( Netflix, Movie lens, Amazon movie recommendation system etc.) But using a method of combination of content based and collaborative filtering, a recommender system worked and finding a customized output to user. Key Words: Content-based, Collaborative Filtering, Recommender System, Machine Learning.

Comparison of Graph Distance Measures for Movie Similarity Using a Multilayer Network Model.

Graph distance measures have emerged as an effective tool for evaluating the similarity or dissimilarity between graphs. Recently, there has been a growing trend in the application of movie networks to analyze and understand movie stories. Previous studies focused on computing the distance between individual characters in narratives and identifying the most important ones. Unlike previous techniques, which often relied on representing movie stories through single-layer networks based on characters or keywords, a new multilayer network model was developed to allow a more comprehensive representation of movie stories, including character, keyword, and location aspects. To assess the similarities among movie stories, we propose a methodology that utilizes a multilayer network model and layer-to-layer distance measures. We aim to quantify the similarity between movie networks by verifying two aspects: (i) regarding many components of the movie story and (ii) quantifying the distance between their corresponding movie networks. We tend to explore how five graph distance measures reveal the similarity between movie stories in two aspects: (i) finding the order of similarity among movies within the same genre, and (ii) classifying movie stories based on genre. We select movies from various genres: sci-fi, horror, romance, and comedy. We extract movie stories from movie scripts regarding character, keyword, and location entities to perform this. Then, we compute the distance between movie networks using different methods, such as the network portrait divergence, the network Laplacian spectra descriptor (NetLSD), the network embedding as matrix factorization (NetMF), the Laplacian spectra, and D-measure. The study shows the effectiveness of different methods for identifying similarities among various genres and classifying movies across different genres. The results suggest that the efficiency of an approach on a specific network type depends on its capacity to capture the inherent network structure of that type. We propose incorporating the approach into movie recommendation systems.

Long-term and short-term memory network based movie comment sentiment analysis

This paper proposes an emotional analysis method of movie reviews based on Long-term and Short-term Memory(LSTM) Network model. Emotional analysis is widely used in movie recommendation system, which can recommend and judge movies by understanding the audiences emotional response to movies. However, due to the characteristics of movie text and the complexity of emotional expression, traditional methods such as machine learning have limitations and shortcomings in emotional analysis. However, the LSTM models better memory is utilized by the method proposed in this paper and the ability to capture the long-term correlation in movie texts, which obviously improves the accuracy and reliability of emotional analysis, and demonstrates the advantages of the LSTM model in emotional analysis compared to the traditional model. Future research can further explore other deep learning models and algorithms, so as to make emotional analysis more accurate and provide users with reliable movie recommendation information.

Decoding sentiment: A sentiment analysis model for movie reviews

Sentiment analysis of movie reviews can provide valuable insights into movie reactions and preferences. To this end, this study proposes the Convolutional Long Short-Term Memory (ConvLSTM) neural network for movie review sentiment analysis. ConvLSTM can efficiently capture sequential information due to its recurrent neural network characteristics. Specifically, the movie review data are first tokenized. Next, the ConvLSTM analysis model is constructed additionally by fine-tuning its parameters to optimize the performance. The ConvLSTM model consists of multiple storage units that retain contextual information, enabling the model to identify long-distance dependencies in the text. The network is trained using a combination of positive and negative movie reviews, and the training process involves adjusting the model weights to minimize the classification error. Experimental results demonstrate the effectiveness of the proposed method in accurately predicting movie review sentiment. It outperforms traditional machine learning methods in sentiment analysis tasks. The findings demonstrate the potential of LSTM-based sentiment analysis in various applications such as movie recommendation systems and market research. This study's findings help advance the development of sentiment analysis techniques and are of great relevance in understanding and catering to audience preferences in the movie industry.

Hyperparameter optimization strategy for Multi-Armed Bandits: Genre recommendation in MovieLens dataset

Due to the challenge of balancing exploration and exploitation in Multi-Armed Bandits (MAB) problems, it is rather challenging to determine the optimal exploration length for diverse datasets. This paper presents an in-depth investigation of the hyperparameter setting for the Explore-Then-Commit (ETC) algorithm, emphasizing an enforced exploration strategy to guarantee adequate exploration of each arm. This investigation will be realized in the context of movie recommendation systems, specifically utilizing the MovieLens 1M dataset. Two key hyperparameters are scrutinized: the horizon, which sets the overall timeframe of the algorithm's execution, and the number of times each arm is explored. The study systematically varies these parameters to study their influence on cumulative regret, a measure of the opportunity cost each time a non-optimal arm is sampled. The empirical examination of the ETC algorithm, conducted employing the MovieLens 1M dataset, has yielded significant discernments pertaining to the dynamic configuration of exploration lengths tailored to particular datasets. The empirical findings underscore that judicious fine-tuning of the ETC algorithm's hyperparameters, under a defined horizon of 50,000 and an exploration length of 500, engenders commendable competitive performance. This optimized configuration notably excels in the domain of genre recommendations, particularly manifesting enhanced proficiency in suggesting genres characterized by the most elevated average ratings. This research accentuates and reinforces the comprehension of hyperparameters under the exploration and exploitation dilemma, thereby providing a structured pathway for the forthcoming applicability of MAB in the wider realm of recommendation systems.