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Articles de revues sur le sujet « Movie Lens 1M data »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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Faleh Mahdi, Roaa. "Increasing the Effectiveness of Prediction in Recommendation Engines Based on Collaborative Filtering." Bilad Alrafidain Journal for Engineering Science and Technology 3, no. 1 (2024): 47–58. http://dx.doi.org/10.56990/bajest/2024.030104.

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In the era of information abundance, the demand for personalized content recommendations has become paramount. Recommendation engines, particularly those employing collaborative filtering, play a pivotal role in delivering tailored suggestions based on user preferences. As technology evolves, the need to enhance the effectiveness of prediction algorithms within these engines becomes increasingly crucial. This research endeavors to contribute to this evolving landscape by delving into collaborative filtering methodologies, identifying challenges, and proposing novel strategies to elevate the ac

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Achuthananda, Reddy Polu Bhumeka Narra Dheeraj Varun Kumar Reddy Buddula Hari Hara Sudheer Patchipulusu Navya Vattikonda and Anuj Kumar Gupta. "Evaluating Machine Learning Approaches for Personalized Movie Recommendations: A Comprehensive Analysis." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN MULTIDISCIPLINARY EDUCATION 3, no. 12 (2024): 1972–80. https://doi.org/10.5281/zenodo.15349260.

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Platforms and movie theatres provide a large range of movies that need to be filtered to each user's tastes. For thisobjective, recommender systems are a useful tool. This research presents a novel hybrid recommender system for personalizedmovie suggestions, which integrates content-based methods with collaborative filtering. This study develops a personalized movierecommendation system utilizing the MovieLens 1M dataset, comprising user ratings for a diverse set of movies. The research dataundergoes separation into training segments that constitute 80% of the total sample while testing compri

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Ayesha Siddique, M Kamran Abid, Muhammad Fuzail, and Naeem Aslam. "Movies Rating Prediction using Supervised Machine Learning Techniques." International Journal of Information Systems and Computer Technologies 3, no. 1 (2024): 40–56. http://dx.doi.org/10.58325/ijisct.003.01.0062.

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Social media are the most enormous and fast data technology on the internet. A massive amount of data is generated from the internet day by day. The efficient processing of such massive records is hard, so we require a system that learns from these facts and makes a useful prediction like machine learning. Machine learning strategies make the systems learn it. Applying K-nearest neighbors (KNN), Support Vector Machine (SVM), as well as Random Forest Model (RF), three supervised machine learning approaches, we attempted to develop a model for a movie recommendation system. This research not onl

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Onuean, Kittisak, Sunantha Sodsee, and Phayung Meesad. "Top-k Recommended Items: Applying Clustering Technique for Recommendation." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 12, no. 2 (2019): 106–17. http://dx.doi.org/10.37936/ecti-cit.2018122.130537.

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This research proposes the Top-k Items Recommendation System which uses clustering techniques based on memory-based collaborative filtering technique. Currently, data sparsity and quantity of system are problems in memory-based collaborative filtering technique. We offer recommend or show some items set for user’s preference. In this research, we propose methods for recommended items set to user preference on data sparsity, movie lens datasets (1M) consisting of 671 users and 163,949 product items were used by determining the preference level between 1 and 5 and user satisfaction levels of all

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B, Sreeja. "Movie Lens – Movie Recommendation System Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33379.

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Recommendation systems, the best way to deal with information overload, are widely utilized to provide users with personalized content and services with high efficiency. Many recommendation algorithms have been researched and deployed extensively in various e-commerce applications, including the movie streaming services over the last decade. However, sparse data cold-start problems are often encountered in many movie recommendation systems. In this paper, we reported a personalized multimodal movie recommendation system based on multimodal data analysis and deep learning. The real-world MovieL

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Chetana, V. Lakshmi, Raj Kumar Batchu, Prasad Devarasetty, Srilakshmi Voddelli, and Varun Prasad Dalli. "Effective movie recommendation based on improved densenet model." Multiagent and Grid Systems 19, no. 2 (2023): 133–47. http://dx.doi.org/10.3233/mgs-230012.

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In recent times, recommendation systems provide suggestions for users by means of songs, products, movies, books, etc. based on a database. Usually, the movie recommendation system predicts the movies liked by the user based on attributes present in the database. The movie recommendation system is one of the widespread, useful and efficient applications for individuals in watching movies with minimal decision time. Several attempts are made by the researchers in resolving these problems like purchasing books, watching movies, etc. through developing a recommendation system. The majority of rec

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Kumar, M. Sandeep, and Prabhu J. "Hybrid Model for Movie Recommendation System Using Fireflies and Fuzzy C-Means." International Journal of Web Portals 11, no. 2 (2019): 1–13. http://dx.doi.org/10.4018/ijwp.2019070101.

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In the era of Big Data, extremely complicated data is delivered from the system, of which it is impossible to collect the correct information with an online platform. In this research work, it provides a hybrid model for a movie-based recommender system; based on meta-heuristic firefly algorithm and fuzzy c-means (FCM) clustering technique to evaluate rating of a movie for a specific user based on the similarity of users and historical data. The firefly algorithm was employed in the movie lens dataset to get the initial cluster and also to initialize the position of clusters. FCM is used to cl

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Shrivastava, Vineet, and Suresh Kumar. "Hesitant fuzzy clustering with convolutional spiking neural network for movie recommendations." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 3 (2024): 1849. http://dx.doi.org/10.11591/ijeecs.v36.i3.pp1849-1856.

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The movie recommender system is one of the most influential and practical tools for aiding individuals in quickly selecting films to watch. Despite numerous academic efforts to employ recommender systems for various purposes, such as movie-watching and book-buying, many studies have overlooked user-specific movie recommendations. This paper introduces a novel approach for movie recommendations that combines the hesitant fuzzy clustering with a convolutional spiking neural network movie recommender system. The initial step involves acquiring input data from benchmark datasets like MovieLens 100

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Vineet, Shrivastava Suresh Kumar. "Hesitant fuzzy clustering with convolutional spiking neural network for movie recommendations." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 3 (2024): 1849–56. https://doi.org/10.11591/ijeecs.v36.i3.pp1849-1856.

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The movie recommender system is one of the most influential and practical tools for aiding individuals in quickly selecting films to watch. Despite numerous academic efforts to employ recommender systems for various purposes, such as movie-watching and book-buying, many studies have overlooked user-specific movie recommendations. This paper introduces a novel approach for movie recommendations that combines the hesitant fuzzy clustering with a convolutional spiking neural network movie recommender system. The initial step involves acquiring input data from benchmark datasets like MovieLens 100

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Manjeet Singh and Namita Goyal. "Collaborative Filtering Movie Recommendation System." International Journal for Modern Trends in Science and Technology 6, no. 12 (2021): 471–73. http://dx.doi.org/10.46501/ijmtst061291.

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Recommendation System plays an important role in today’s era of e-commerce. From OTT platforms to the shopping application and music application everywhere we see that after watching a movie or buying an item, listening a song we are recommended with some other movie or item or song. Most of the time we select our next movie, item or song from the recommended one. In this paper I will give you a brief description of collaborative and user-based filtering. The data used in this research is taken from Movie Lens. The result obtained contains some movie recommendations.

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Poornima, S., and M. Geethanjali. "Shilling Attack Detection in User Based Recommendation System." Data Analytics and Artificial Intelligence 3, no. 2 (2023): 85–94. http://dx.doi.org/10.46632/daai/3/2/17.

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The majority of the existing unsupervised methods for detecting shilling attacks are based on user rating patterns, ignoring the differences in rating behavior between legitimate users and attack users. These methods have low accuracy in detecting different shilling attacks without having any prior knowledge of the attack types. We provide a novel unsupervised shilling assault detection technique based on an examination of user rating behavior in order to overcome these constraints. By first examining the deviation of rating tendencies on each item, we are able to determine the target item(s)

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Subekti, Adaninggar Septi. "I'm not, like, the best dragon, ya know?: A Deconstructionist Reading of Disney’s Raya and the Last Dragon." ProTVF 8, no. 1 (2024): 36. http://dx.doi.org/10.24198/ptvf.v8i1.47577.

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Background: Raya and the Last Dragon is the first Disney movie inspired by traditional Southeast Asian cultures, somewhat under-represented in mainstream media. Hence, the movie is worth further investigation. Purpose: The study intends to read Disney’s Raya and the Last Dragon using a deconstructionist lens. Methods: The study used qualitative analysis of audio-visual materials. The auditory and visual aspects of the movie are examined using a deconstructionist lens by watching and rewatching the movie with occasional note-taking. Data are presented in the forms of characters’ dialogues, and

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Mbura, Issa. "Crux of the Bongo Movie from a Digital Disruption Lens." Umma: The Journal of Contemporary Literature and Creative Arts 9, no. 2 (2022): 68–92. http://dx.doi.org/10.56279/ummaj.v9i2.4.

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This paper reports the findings of a study that had explored digital disruption as an analytical lens developed based on the constructs of two theories: the digital disruption theory and the disruptive innovation theory. The study had employed unstructured in-depth interviews, direct observation and virtual ethnographic to consult media experts, pioneer filmmakers, Bongo Movie’ producers, movies library’s keepers, movie retailers, movie translators (deejays) and social network sites (SNS) to collect data. Based on the study findings, the paper argues that the shift in technological paradigm, s

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Muhammad Waseem, Rehan Ashfaq, Zainab Waheed, and Maryam Nawaz. "Diamonds and Dependency: A Case study of Blood Diamond." Indus Journal of Social Sciences 3, no. 1 (2025): 202–12. https://doi.org/10.59075/ijss.v3i1.602.

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This study focuses on elements of dependency in the movie "Blood Diamond with the lens of Raul Prebisch's dependency theory in the movie "Blood Diamond”. The study implements qualitative data analysis, guided by the principles of dependency theory. The data for research is collected in the form of dialogues from the movie "Blood Diamond" and the dialogues are discussed in detail with contextual analysis to uncover the elements of dependency. The findings reveal that the film serves as a tool to understand the cycles of exploitation of resources-rich countries by wealthier nations which causes

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Neisya, Neisya, Fitria Aprilia, and Elga Triwulandari. "BODY DISCIPLINE IN THE MOVIE “TURNING RED”: FOUCALDIAN DISCOURSE." Lire Journal (Journal of Linguistics and Literature) 8, no. 1 (2024): 33–44. http://dx.doi.org/10.33019/lire.v8i1.214.

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This research explores the concept of body discipline and its implications in the movie "Turning Red," focusing on the character Meilin Lee and her portrayal of female regulation. Using the qualitative method and analytical approach inspired by Creswell & Guetterman. For collecting the data, the researchers watched the Turning Red movie repeatedly and highlighted the issue related to the research problem through the dialogues and scenes in the movie. Meanwhile, in analyzing the data, the researchers classified and analyzed the types of body discipline and its impacts towards Meilin Lee thr

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Martinez, Victor R., Krishna Somandepalli, and Shrikanth Narayanan. "Boys don’t cry (or kiss or dance): A computational linguistic lens into gendered actions in film." PLOS ONE 17, no. 12 (2022): e0278604. http://dx.doi.org/10.1371/journal.pone.0278604.

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Contemporary media is full of images that reflect traditional gender notions and stereotypes, some of which may perpetuate harmful gender representations. In an effort to highlight the occurrence of these adverse portrayals, researchers have proposed machine-learning methods to identify stereotypes in the language patterns found in character dialogues. However, not all of the harmful stereotypes are communicated just through dialogue. As a complementary approach, we present a large-scale machine-learning framework that automatically identifies character’s actions from scene descriptions found

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Alshammari, Gharbi, Stelios Kapetanakis, Abdullah Alshammari, Nikolaos Polatidis, and Miltos Petridis. "Improved Movie Recommendations Based on a Hybrid Feature Combination Method." Vietnam Journal of Computer Science 06, no. 03 (2019): 363–76. http://dx.doi.org/10.1142/s2196888819500192.

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Recommender systems help users find relevant items efficiently based on their interests and historical interactions with other users. They are beneficial to businesses by promoting the sale of products and to user by reducing the search burden. Recommender systems can be developed by employing different approaches, including collaborative filtering (CF), demographic filtering (DF), content-based filtering (CBF) and knowledge-based filtering (KBF). However, large amounts of data can produce recommendations that are limited in accuracy because of diversity and sparsity issues. In this paper, we

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Mujeeb Ur Rehman and Palwasha. "Comparison of Contrast Sensitivity in Subjects Wearing Spectacles and Contact Lenses." Journal of Clinical and Community Ophthalmology 1, no. 02 (2023): 58–62. https://doi.org/10.71177/jcco.v1i02.14.

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Aims: The purpose of this study was to compare the contrast sensitivity level in people wearing soft contact lenses and spectacles. Study design: Cross sectional Duration and Study Setting: Six months, Ophthalmology Department, Hayatabad Medical Complex, Peshawar. Methods: Data were taken from participants having myopic refractive error only, wearing soft contact lenses and spectacles for correction of their myopia. All the participants underwent comprehensive eye examination. Contrast Sensitivity (CS) were measured with Lea contrast sensitivity chart for each eye separately at three meters, t

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Patra, Sukanya, and Boudhayan Ganguly. "Improvising Singular Value Decomposition by KNN for Use in Movie Recommender Systems." Journal of Operations and Strategic Planning 2, no. 1 (2019): 22–34. http://dx.doi.org/10.1177/2516600x19848956.

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Online recommender systems are an integral part of e-commerce. There are a plethora of algorithms following different approaches. However, most of the approaches except the singular value decomposition (SVD), do not provide any insight into the underlying patterns/concepts used in item rating. SVD used underlying features of movies but are computationally resource-heavy and performs poorly when there is data sparsity. In this article, we perform a comparative study among several pre-processing algorithms on SVD. In the experiments, we have used the MovieLens 1M dataset to compare the performan

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Elzeheiry, Salma Adel, N. E. Mekky, A. Atwan, and Noha A. Hikal. "An enhanced framework for solving cold start problem in movie recommendation systems." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1628–37. https://doi.org/10.11591/ijeecs.v24.i3.pp1628-1637.

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Recommendation systems (RSs) are used to obtain advice regarding decision-making. RSs have the shortcoming that a system cannot draw inferences for users or items regarding which it has not yet gathered sufficient information. This issue is known as the cold start issue. Aiming to alleviate the user’s cold start issue, the proposed recommendation algorithm combined tag data and logistic regression classification to predict the probability of the movies for a new user. First using alternating least square to extract product feature, and then diminish the feature vector by combining princi

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Elzeheiry, Salma Adel, N. E. Mekky, A. Atwan, and Noha A. Hikal. "An enhanced framework for solving cold start problem in movie recommendation systems." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1628. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1628-1637.

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<p>Recommendation systems (RSs) are used to obtain advice regarding decision-making. RSs have the shortcoming that a system cannot draw inferences for users or items regarding which it has not yet gathered sufficient information. This issue is known as the cold start issue. Aiming to alleviate the user’s cold start issue, the proposed recommendation algorithm combined tag data and logistic regression classification to predict the probability of the movies for a new user. First using alternating least square to extract product feature, and then diminish the feature vector by combining pri

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Gomathy, Dr C. K. "A Comparing Collaborative Filtering and Hybrid Recommender System for E-Commerce." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 635–38. http://dx.doi.org/10.22214/ijraset.2021.38844.

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Abstract: Here we are building an collaborative filtering matrix factorization based hybrid recommender system to recommend movies to users based on the sentiment generated from twitter tweets and other vectors generated by the user in their previous activities. To calculate sentiment data has been collected from twitter using developer APIs and scrapping techniques later these are cleaned, stemming, lemetized and generated sentiment values. These values are merged with the movie data taken and create the main data frame.The traditional approaches like collaborative filtering and content-based

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Ali, Yasher, Osman Khalid, Imran Ali Khan, et al. "A hybrid group-based movie recommendation framework with overlapping memberships." PLOS ONE 17, no. 3 (2022): e0266103. http://dx.doi.org/10.1371/journal.pone.0266103.

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Recommender Systems (RS) are widely used to help people or group of people in finding their required information amid the issue of ever-growing information overload. The existing group recommender approaches consider users to be part of a single group only, but in real life a user may be associated with multiple groups having conflicting preferences. For instance, a person may have different preferences in watching movies with friends than with family. In this paper, we address this problem by proposing a Hybrid Two-phase Group Recommender Framework (HTGF) that takes into consideration the pos

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N., Sai Kiran, Karthikeyan B., Suresh N., Sravan Kumar B., and Kanimozhi J. "Substance Based Separating in a Film Idea Framework." International Journal of Computational Intelligence in Control 12, no. 2 (2020): 258–63. https://doi.org/10.5281/zenodo.7485673.

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Every day, various types of data are uploaded into the internet from all over the world since its inception. It is extremely difficult to retrieve data from the internet for a specific data. The data results after browsing may not be topic dependent, oriented, or related. Recommender Systems are used to determine the context of specific data. The primary subcategories of recommender systems are as follows: Options include content-based filtering, collaborative filtering, and a hybrid strategy. In this study, we do experiments on a Movie lens data set using Item-based Collaborative filtering. U

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-, Rifda Faizatur Rahmah, and Fitri Rakhmawati. "CULTURAL VALUE REPRESENTATION IN AVATAR: THE LAST AIRBENDER MOVIE POSTERS: MULTIMODAL DISCOURSE ANALYSIS." Lire Journal (Journal of Linguistics and Literature) 9, no. 2 (2025): 308–25. https://doi.org/10.33019/lire.v9i2.409.

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This research aims to explore the representational meaning and cultural values depicted in Avatar: The Last Airbender 2024 movie posters through the lens of Multimodal Discourse Analysis and Halliday’s Systemic Functional Grammar. While previous studies on Avatar have focused on its philosophical themes, cultural representation, and narrative elements, by analyzing visual and textual elements in promotional materials, such as movie posters, in conveying cultural values, this gap will be address in this research. The study was chosen for its strong ties to East Asian cultural traditions, includ

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Yang, Xu, Ziyi Huan, Yisong Zhai, and Ting Lin. "Research of Personalized Recommendation Technology Based on Knowledge Graphs." Applied Sciences 11, no. 15 (2021): 7104. http://dx.doi.org/10.3390/app11157104.

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Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algor

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Danev, Radostin, Hirofumi Iijima, Mizuki Matsuzaki, and Sohei Motoki. "Fast and accurate defocus modulation for improved tunability of cryo-EM experiments." IUCrJ 7, no. 3 (2020): 566–74. http://dx.doi.org/10.1107/s205225252000408x.

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Current data collection strategies in electron cryo-microscopy (cryo-EM) record multiframe movies with static optical settings. This limits the number of adjustable parameters that can be used to optimize the experiment. Here, a method for fast and accurate defocus (FADE) modulation during movie acquisition is proposed. It uses the objective lens aperture as an electrostatic pole that locally modifies the electron beam potential. The beam potential variation is converted to defocus change by the typically undesired chromatic aberration of the objective lens. The simplicity, electrostatic princ

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Apsari, Ni Made Cintya Dwi, and Gusti Ayu Made Rai Suarniti. "Moral Values of the Main Character in Jennifer Lee’s Wish (2023)." Journal of Social Work and Science Education 6, no. 2 (2025): 738–46. https://doi.org/10.52690/jswse.v6i2.1213.

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This study identifies the moral values embodied by the main character in the script of Wish (2023), a movie written by Jennifer Lee. The research applies the theoretical framework of Linda and Richard Eyre’s Teaching Your Children Values (1993), supported by Carol K. Sigelman and David R. Shaffer’s Life Span Human Development (1995). A qualitative descriptive method was employed, with the movie script serving as the primary data source. Data were collected through repeated readings and note-taking techniques to extract dialogues reflecting moral values. The analysis reveals that the main chara

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Et al., AL-Bakri. "A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures." Baghdad Science Journal 16, no. 1 (2019): 0263. http://dx.doi.org/10.21123/bsj.16.1.(suppl.).0263.

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Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate

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Et al., AL-Bakri. "A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures." Baghdad Science Journal 16, no. 1(Suppl.) (2019): 0263. http://dx.doi.org/10.21123/bsj.2019.16.1(suppl.).0263.

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Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate

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Gohzali, Hernawati, and Dita Maria Panjaitan. "Movie Recommendation System Model using Bisecting K-Means Technique and Collaborative Filtering." Journal of Multimedia Trend and Technology 3, no. 2 (2024): 95–104. http://dx.doi.org/10.35671/jmtt.v3i2.71.

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In the current film industry, the competition is very big. We can see it in online streaming content through the ratings obtained. Film itself is a visual work that is packaged as a product of public entertainment for a specific purpose. However, there are also many films that are considered not to meet the audience's expectations. Even the films presented are sometimes illegal or pirated films. We can also find out whether a film is recommended or not. The problem is that viewers rarely understand how to see recommendations or even provide appropriate film recommendations. This study aims to

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Wang, Sitan, Xinyi Wu, Lintong Zhao, Yuru Xue, Ruihan Xia, and Zhuofan Sun. "Improved time-aware debiasing models for recommender systems." Applied and Computational Engineering 6, no. 1 (2023): 1560–76. http://dx.doi.org/10.54254/2755-2721/6/20230384.

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Recommender systems are affected by user selection bias during user interactions. Previously, Huang, Jin et al. proposed the TMF-DANCER model to deal with the fact that selection bias is dynamic, and the popularity of an item and user preferences may change drastically over time. However, the previous time-aware methods did not consider the continuity of time, and performing collaborative filtering with matrix factorization may not be able to capture the complex structure of user interaction data. Our methods enhance the effectiveness of time-aware debiasing by utilizing the time-varying seque

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Ramon B, Jay, Arroyo Lauder R, and Atienza Juelyn P. Romero. "EUPHEMISMS IN PHILIPINE CULTURE POTRAYED IN FILIPINO MOVIES." SIGEH ELT : Journal of Literature and Linguistics 3, no. 2 (2023): 41–58. http://dx.doi.org/10.36269/sigeh.v3i2.2065.

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This study explores the euphemisms in Philippine culture portrayed in Filipino movies. This study attempts to answer the research questions: (1) What euphemisms are used in the movies (a) Remington and the Curse of Zombadings and (b) Die Beautiful along with metaphor, idioms, and hyperbole? and (2) What are the functions of the euphemisms used in the two movies? The researchers analyzed the data using the lens of latent content analysis and qualitative analysis to unpack the meanings and relationships. The data were taken from the script supported by the transcription method. The method of dat

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Liao, Xiaofeng, Xiangjun Li, Qingyong Xu, Hu Wu, and Yongji Wang. "Improving Ant Collaborative Filtering on Sparsity via Dimension Reduction." Applied Sciences 10, no. 20 (2020): 7245. http://dx.doi.org/10.3390/app10207245.

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Recommender systems should be able to handle highly sparse training data that continues to change over time. Among the many solutions, Ant Colony Optimization, as a kind of optimization algorithm modeled on the actions of an ant colony, enjoys the favorable characteristic of being optimal, which has not been easily achieved by other kinds of algorithms. A recent work adopting genetic optimization proposes a collaborative filtering scheme: Ant Collaborative Filtering (ACF), which models the pheromone of ants for a recommender system in two ways: (1) use the pheromone exchange to model the ratin

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Gong, Jibing, Xinghao Zhang, Qing Li, et al. "A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous Modeling." Applied Sciences 11, no. 16 (2021): 7418. http://dx.doi.org/10.3390/app11167418.

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To provide more accurate and stable recommendations, it is necessary to combine display information with implicit information and to dig out potential information. Existing methods only consider explicit feedback information or implicit feedback information unilaterally and ignore the potential information of explicit feedback information and implicit feedback information, which is also crucial to the accuracy of the recommendation system. However, the traditional Heterogeneous Information Networks (HIN) recommendation ignores the attribute information in the meta-path and the interaction betw

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Li, Panying, Yuxuan Han, Xiumei Wen, and Fanxing Meng. "Improvement and Research of Collaborative Filtering Algorithm Based on Penalty Factor." Journal of Physics: Conference Series 2209, no. 1 (2022): 012026. http://dx.doi.org/10.1088/1742-6596/2209/1/012026.

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Abstract The rapid development of the Internet has pushed society into the era of information explosion, and people are faced with more and more information screening and choices. A recommendation system is an effective way to process massive amounts of information, and it is also a tool that can make recommendations based on user behavior. Traditional collaborative filtering algorithms generally use the cosine similarity formula to calculate the similarity between users or items to make recommendations. Due to the popularity of the Internet, more and more popular items have appeared. The appe

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Nnabuife, Chika Onyinye, Zitta Nankyen Dombut, and Nwodu Gloria Eberechi. "Film Representation of Igbo Cultural Widowhood Practices in Anambra State. An Audience Analysis of a Nollywood Movie: Glory of a Widow." European Journal of Theoretical and Applied Sciences 2, no. 4 (2024): 112–47. http://dx.doi.org/10.59324/ejtas.2024.2(4).11.

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This study examines the representation of Igbo cultural widowhood practices in Anambra State through the lens of the Nollywood film "Glory of a Widow". Employing structural functionalist theory and feminist film theory, this qualitative research utilized focus group discussions to gather data from audiences. Findings reveal that Igbo cultural practices surrounding widowhood continue to be prevalent in contemporary society, perpetuating gender inequality and discrimination. The study identifies the need for amendments of existing laws and policies to protect the rights of widows. The film's por

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Chika, Onyinye Nnabuife, Nankyen Dombut Zitta, and Gloria Eberechi Nwodu. "Film Representation of Igbo Cultural Widowhood Practices in Anambra State. An Audience Analysis of a Nollywood Movie: Glory of a Widow." European Journal of Theoretical and Applied Sciences 2, no. 4 (2024): 112–47. https://doi.org/10.59324/ejtas.2024.2(4).11.

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This study examines the representation of Igbo cultural widowhood practices in Anambra State through the lens of the Nollywood film "Glory of a Widow". Employing structural functionalist theory and feminist film theory, this qualitative research utilized focus group discussions to gather data from audiences. Findings reveal that Igbo cultural practices surrounding widowhood continue to be prevalent in contemporary society, perpetuating gender inequality and discrimination. The study identifies the need for amendments of existing laws and policies to protect the rights of widows. The film's por

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Kim, Anh Dao, Phi Hung Truong, and Duc Sinh Hoang. "The Influence of Product Placement on Luxury Tourism: An S-O-R Model Approach." International Conference on Tourism Research 8, no. 1 (2025): 402–10. https://doi.org/10.34190/ictr.8.1.3509.

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Product placement has developed into a quite successful marketing tool. Product placement helps businesses and locales to seamlessly fit into the storyline; it also helps brands and destinations to mix in visually driven media like movies, thereby producing a naturally applied advertising approach. The product placement in tourism serves as an effective tool for promoting locations, goods, and services. This study examines product placement in the 2018 film Crazy Rich Asians to explore how cinematic portrayals influence destination promotion through the Stimulus-Organism-Response (S-O-R) Model

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Amori, Karima Ismail, and Rand Ahmed Adeeb. "Absorber Diameter Effect on the Thermal Performance of Solar Steam Generator." Journal of Engineering 22, no. 4 (2016): 127–46. http://dx.doi.org/10.31026/j.eng.2016.04.09.

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In this work, a convex lens concentrating solar collector is designed and manufactured locally by using 10 convex lenses (concentrator) of a diameter 10cm and one Copper absorber tube of a diameter 12.5mm and 1mm in thickness 1m length. Two axes manual Tracking system also constructed to track the sun continuously in two directions. The experiments are made on 17th of May 2015 in climatic conditions of Baghdad. The experimental data are fed to a computer program to solve the thermal performing equation, to find efficiency and actual useful energy. Then this data is used in numerical CFD softwa

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TR, Mahesh, and V Vinoth Kumar. "Clustering Techniques for Recommendation of Movies." International Journal of Data Informatics and Intelligent Computing 1, no. 2 (2022): 16–22. http://dx.doi.org/10.59461/ijdiic.v1i2.17.

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A recommendation system employs a variety of algorithms to provide users with recommendations of any kind. The most well-known technique, collaborative filtering, involves users with similar preferences although it is not always as effective when dealing with large amounts of data. Improvements to this approach are required as the dataset size increases. Here, in our suggested method, we combine a hierarchical clustering methodology with a collaborative filtering algorithm for making recommendations. Additionally, the Principle Component Analysis (PCA) method is used to condense the dimensions

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Abimbola Adetola Stephen-Adesina. "Portrayal of Selected African Dialects and Accents in The Woman King." Creative Saplings 3, no. 10 (2024): 72–88. https://doi.org/10.56062/gtrs.2024.3.10.785.

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This study aims to examine the portrayal of selected African dialects and accents in The Woman King, a Hollywood film by mainly African actors. Directors, dialect, and accent coaches view these tools as vital in the overall development of screen productions in an educative and informative manner, harmonizing the character’s cultural, emotional, physiological, and psychological as they align with dialogue, identity, language, and idiolect. The study purposively selected actors in The Woman King through a content analysis lens. The data was collected through a qualitative and quantitative resear

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Thillainayagam, Venkatesan, Ramkumar Thirunavukarasu, and J. Arun Pandian. "Enhanced Multi-Level Recommender System Using Turnover-Based Weighting for Predicting Regional Preferences." Computers 14, no. 7 (2025): 294. https://doi.org/10.3390/computers14070294.

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In the realm of recommender systems, the prediction of diverse customer preferences has emerged as a compelling research challenge, particularly for multi-state business organizations operating across various geographical regions. Collaborative filtering, a widely utilized recommendation technique, has demonstrated its efficacy in sectors such as e-commerce, tourism, hotel management, and entertainment-based customer services. In the item-based collaborative filtering approach, users’ evaluations of purchased items are considered uniformly, without assigning weight to the participatory data so

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Agosto, Vonzell. "Scripted Curriculum: What Movies Teach about Dis/ability and Black Males." Teachers College Record: The Voice of Scholarship in Education 116, no. 4 (2014): 1–24. http://dx.doi.org/10.1177/016146811411600412.

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Background/Context Tropes of dis/ability in the movies and master-narratives of Black males in education and society are typically treated in isolation. Furthermore, education research on Hollywood movies has typically focused on portrayals of schools, principals, and teachers even though education professionals are exposed to a broader range of movies. Analyses of dis/ability tropes in the media also tend to ignore how they work in multiples and intersect with narratives of other social identities such as race and gender. Focus of Study This article examines the complexity of portrayals of Bl

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Alrashidi, Muhammad, Roliana Ibrahim, and Ali Selamat. "Hybrid CNN-based Recommendation System." Baghdad Science Journal 21, no. 2(SI) (2024): 0592. http://dx.doi.org/10.21123/bsj.2024.9756.

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Recommendation systems are now being used to address the problem of excess information in several sectors such as entertainment, social networking, and e-commerce. Although conventional methods to recommendation systems have achieved significant success in providing item suggestions, they still face many challenges, including the cold start problem and data sparsity. Numerous recommendation models have been created in order to address these difficulties. Nevertheless, including user or item-specific information has the potential to enhance the performance of recommendations. The ConvFM model i

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Soligo, Marta, and David R. Dickens. "Rest in Fame: Celebrity Tourism in Hollywood Cemeteries." Tourism Culture & Communication 20, no. 2 (2020): 141–50. http://dx.doi.org/10.3727/109830420x15894802540214.

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This research is a critical study of tourism at four cemeteries in the Los Angeles area between 2013 and 2019: Hollywood Forever, Forest Lawn in Glendale, Forest Lawn in Hollywood, and Pierce Brothers Westwood Village Memorial Park Cemetery. We examined these venues through the lens of celebrity tourism, since they are known as "Hollywood memorial parks," hosting the graves of some of the most famous stars in the world. Through participant observation, informal conversations, and content analysis of texts we aimed to understand how the relationship between these venues and the entertainment in

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Unnithan, Chandana, and Arthur Tatnall. "Actor-Network Theory (ANT) Based Visualisation of Socio-Technical Facets of RFID Technology Translation." International Journal of Actor-Network Theory and Technological Innovation 6, no. 1 (2014): 31–53. http://dx.doi.org/10.4018/ijantti.2014010103.

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In the early 2000s, Information Systems researchers in Australia had begun to emphasise socio-technical approaches in innovation adoption of technologies. The ‘essentialist' approaches to adoption (for example, Innovation Diffusion or TAM), suggest an essence is largely responsible for rate of adoption (Tatnall, 2011) or a new technology introduced may spark innovation. The socio-technical factors in implementing an innovation are largely flouted by researchers and hospitals. Innovation Translation is an approach that purports that any innovation needs to be customised and translated in to con

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"Analysis of Movie Recommendation System Data Sets using machine learning techniques." Journal of Innovative Computing and Emerging Technologies 2, no. 2 (2021). http://dx.doi.org/10.56536/jicet.v2i2.27.

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Multimedia has emerged as one of the top entertainment source due to cheap and uninterrupted availability of high internet speeds. “Movie recommendation system have attracted much research interest within the field of recommendation systems. Two widely used techniques, one is collaborative filtering (CF) and second is content-based (CB). However, the accuracy performance of any hybrid system which utilizes more advantage of both systems to better results. Movie recommendation systems has suffered from different problems, such as “, Sparsity, Grey sheep problem, Cold start problem, Long-tail pr

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Matheus, Zamberlan. "Test Movie Lens 100k." September 30, 2020. https://doi.org/10.5281/zenodo.4060424.

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Sun, Guohu, and Yixin Chen. "A two-stage deep semantic model for movie recommendation with long-term user profiling on Spark." Journal of Computational Methods in Sciences and Engineering, July 24, 2025. https://doi.org/10.1177/14727978251361866.

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With the growth of the film market, users are often overwhelmed by irrelevant information, making it difficult to make accurate movie choices. To address this, we propose an improved deep structured semantic model (DSSM) movie recommendation algorithm based on Spark technology. The algorithm employs two DSSMs: one to extract users’ long-term preferences and another for final movie recommendations. The preference model integrates explicit and implicit user interaction features, while the recommendation model utilizes recent viewing history and search behavior. Experiments were conducted on the

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