Movie Recommendation System Research Articles

Comparison measures for Pythagorean $ m $-polar fuzzy sets and their applications to robotics and movie recommender system

<abstract><p>The perception of comparison measures is vitally significant in more or less every scientific field. They have many practical implementations in areas such as medicine, molecular biology, management, meteorology, etc. In this article, novel similarity, distance, and correlation comparison measures for Pythagorean $ m $-polar fuzzy sets are proposed. The leading qualities of these comparison measures are investigated. The numerical examples are provided to demonstrate their formulation. In P$ m $FSs, elements are allowed to duplicate finitely, which supports the usage of the measures put forward in here-and-now situations where we ponder time and again to reach some decision. The three algorithms are proposed to discuss the applications of comparison measures for P$ m $FSs in robotics and movie recommender systems.</p></abstract>

Movie Recommender System Using Parameter Tuning of User and Movie Neighbourhood via Co-Clustering

Recommender System is a information filtering tool to filter the relevant information from given information in the present era of big data. Movie Recommender System is machine learning based autonomous tool that filters the movies from big movie database like netflix, amazon etc according to user preferences. The main focus of this paper is Partitional Weighted co-clustering for Movie Recommender System. The primary objective of this research article is to fine tune the parameters of user and movie neighborhoods by setting different values for row clusters number and column clutsers number parameters of co-clustering. Test results obtained from the Movie database show that the proposed method can bring more accurate personalized recommendations for the movie as compared to existing methods of the order of 7.91%.

Sequence Recommendation based on Deep Learning

Sequence recommendation systems have become increasingly popular in various fields such as movies and social media. These systems aim to predict a user's preferences and interests based on their past behavior and provide them with personalized recommendations. Deep learning, particularly Recurrent Neural Networks (RNNs), have emerged as a powerful tool for sequence recommendation. In this research, we explore the effectiveness of RNNs in movie and Instagram recommendation systems. We investigate and compare the performance of different types of RNNs, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in recommending movies and Instagram posts to users based on their browsing history. Additionally, we study the impact of incorporating additional information such as user's demographics and Instagram hashtags on the performance of the recommendation system. We also evaluate the performance of RNN-based movie and Instagram recommendation systems in comparison to traditional approaches, such as collaborative filtering and content-based filtering, in terms of accuracy and personalization. The findings of this research provide insights into the effectiveness of RNNs in movie and Instagram recommendation systems and contribute to the development of more accurate and personalized recommendations for users.

Task-Specific and Graph Convolutional Network based Multi-modal Movie Recommendation System in Indian Setting

Nowadays the Recommendation System, a subclass of information filtering system does not require any introduction, and the movie recommendation system plays a vital role in the streaming platform where many movies are needed to analyze before showcasing a perfectly matched subset of them to its users. Most of the available datasets contain the rating information of user-movie pairs and this is the reason of regression-based works that predict the rating value for a user-movie pair. We have also found that there is no work on the Indian regional language-based dataset containing no users’ feedback in the rating scale.In this paper, we have introduced a recommendation system for the Indian language-based multi-modal Hindi movies’ dataset where users’ feedback is from the three different classes, i) Dislike, ii) Like, and iii) Neutral. Here, we have used the Flickscore dataset and added the audio-video information of the trailers of its movies for making it multi-modal. Besides that, we have investigated the performance of a classification-based model having two modules, (i) Task-Specific (TS) and (ii) Graph Convolutional Network (GCN). The performance of different combinations of these modules is tested on different modalities of the dataset. We have tested its performance in cold-start scenarios also. Modality wise different embedding processes have been introduced here and the experimental results tried to conclude how the model works in uni-modal, bi-modal, and all-modal information of movies in an information system where no rating information is present.

MOVIE RECOMMENDATION SYSTEM: A REVIEW

Whether we are choosing a series on an OTT platform or searching for a movie, online recommendation engines have influenced our decisions. They are far from perfect and are still very much in the early phases of development. Specifically, we provide bandy movie recommendation algorithms in this study. Furthermore, we discuss key exploratory articles that have helped these systems overcome certain challenges and critically evaluate some work on movie recommendation systems. Even though there have been improvements, recommendation systems still require effort to improve their ability to provide reliable suggestions for a larger variety of operations.

Movie Recommendation Using Hybrid-Based Approach

The field of recommendation systems has been rapidly growing due to the increasing amount of data available on the internet. Movie recommendation systems are one of the most widely used applications of recommendation systems. In this paper, we propose a hybrid-based approach to movie recommendation systems. The proposed approach combines content-based filtering and collaborative filtering techniques to provide better recommendations. The content-based filtering technique uses movie features such as genre, director, actors, and plot to recommend similar movies to the users. The collaborative filtering technique uses the user's past behaviour and other users' behaviour to recommend movies to the user. We evaluated the proposed hybrid approach on the Cosine Similarity and SVD dataset to achieve better results compared to the individual content-based and collaborative filtering techniques.

DCF-MLSTM: a deep security content-based filtering scheme using multiplicative BiLSTM for movie recommendation system

Recently, the demand for online and offline recommendation systems has increased drastically. These systems are widely used in tourism, music, and video or movie recommendations. Currently, online movie streaming applications have gained huge attention. Providing better recommendations to the user is a challenging task for these applications. The content-based filtering (CBF) recommender system is a promising technique for these systems. However, traditional systems suffer from challenges such as cold-start problems, sparsity, and scalability. Consequently, we strengthen content-based recommendation algorithms by enriching the user-related and relevant product models with effective tendencies. The majority of previous work on classifiers has been in recommendation systems. To overcome these issues, we present a deep learning model that uses a deep neural network mechanism and a multiplicative BiLSTM model. This scheme uses embedding, weight updating, and preference learning processes to improve the recommendation system's performance. The performance of the proposed approach is measured in terms of MAE, MAP, Precision, Recall, and F-measure. The comparative performance shows that the proposed approach achieves better performance when compared with state-of-art movie recommendation techniques.

Enhancing Movie Recommendation Systems with Deep Learning and Sentiment Analysis

Enhancing Movie Recommendation Systems with Deep Learning and Sentiment Analysis

Personalized Movie Recommendation System Based on DDPG: Application and Analysis of Reinforcement Learning in User Preferences

Personalized Movie Recommendation System Based on DDPG: Application and Analysis of Reinforcement Learning in User Preferences

Collaborative Filtering with Temporal Features for Movie Recommendation System

Nowadays, recommender systems play a vital role in every human being's life due to the time retrieving the items. The matrix factorization (MF) technique is one of the main methods among collaborative filtering (CF) techniques that have been widely used after the Netflix competition. Traditional MF techniques are static in nature. However, the perception and popularity of products are constantly changing with time. Similarly, the users’ tastes are changed with time. Hence, traditional MF cannot handle the dynamic effect of the user-item interaction. To tackle the temporal and dynamic effect of user-item interaction, we proposed a collaborative filtering model for movie recommendations that include temporal effects. To justify the significance of the proposed technique, we evaluated our model on a standard dataset (Movielens) and compared it with state-of-art models. The exploratory outcomes signify that the proposed technique obtains a better result than a state-of-art model with an improvement of 1.35% and 1.28% on ML-100K and 1M datasets, respectively.

A Movie Recommender System Based on User Profile and Artificial Bee Colony Optimization.

In this study, a new algorithm for recommending movies to viewers has been proposed. To do this, the suggested method employs data mining techniques. The proposed method includes three steps for generating recommendations: "preprocessing of user profile information," "feature extraction," and "recommendation." In the first step of proposed method, the user information will be examined and transformed into a form that can be handled in the next phases. In the second step of the proposed method, user attributes are then extracted as a collection of their individual qualities, as well as the average rating of each user for various genres. The bee colony optimization algorithm is then used to select the optimal features. Finally, in the third step of the proposed method, the ratings of similar users are utilized to offer movies to the target user, and the similarities between various users are determined using the characteristics calculated for them, as well as the Euclidean distance criteria. The proposed method was evaluated using the MovieLens database, and its output was assessed in terms of precision and recall criteria; these results show that the proposed method will increase the precision by an average of 1.39% and the recall by 0.8% compared to the compared algorithms.

DCF-MLSTM- A Deep security Content-Based Filtering Scheme Using Multiplicative BiLSTM for Movie Recommendation System

DCF-MLSTM- A Deep security Content-Based Filtering Scheme Using Multiplicative BiLSTM for Movie Recommendation System

A Meta-learning Based Generative Model with Graph Attention Network for Multi-Modal Recommender Systems

With increased data in various e-commerce domains or in different online streaming platforms like Amazon Prime, Netflix, etc, it has become more challenging to give a personalized recommendation to a user. In terms of the movie recommendation system, different multimodal information such as audio, video, and text are effectively fused together to tackle this problem. However, in real-world scenarios this information may not always be present, leading to the problem of missing modality. Existing works in Recommender Systems (RSs) may fail to recommend effectively if their model is not susceptible to missing modality. In this work, we propose a meta-learning model to deal with the missing modality problem. Our proposed model tries to reconstruct the missing features with the help of the existing modalities and once reconstructed, we pass them to novel trident architecture for solving the recommendation task. We have performed experiments on the newly created multimodal version of the MovieLens dataset. Additionally, we have incorporated a new modality where we extracted subtitles of the movies to increase the effectiveness of our missing modality model.

Review of Movie Recommender Systems Based on Deep Learning

With the development of the network, society has moved into the data era, and the amount of data is exploding, we need a tool to help users find corresponding data collections based on their interests, and recommender systems were born for this purpose. In the movie field, recommender systems suggest items that users may like, improving the efficiency of finding movies and optimizing the user experience thus driving the growth of the movie industry. Machine learning is a multi-disciplinary science that focuses on how to improve the performance of algorithms by continuously reorganizing existing knowledge structures in a way that mimics human learning. Deep learning is a research direction in the field of machine learning that has achieved results in many areas that far surpass previous related techniques. In order to better provide personalized services to users and improve the accuracy of the system’s recommendations, it is necessary to integrate deep learning techniques into the recommender system to optimize the system’s performance. In this paper, we review different approaches in deep learning based recommender systems.

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.

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>

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.

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.