Relevant bibliographies by topics / Movie Lens 1M data

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Academic literature on the topic 'Movie Lens 1M data'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Movie Lens 1M data"

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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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Book chapters on the topic "Movie Lens 1M data"

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Gunjal, S. N., Yadav S K, and Kshirsagar D B. "A Distributed Item Based Similarity Approach for Collaborative Filtering on Hadoop Framework." In Intelligent Systems and Computer Technology. IOS Press, 2020. http://dx.doi.org/10.3233/apc200176.

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Now a day’s multiple website provides millions of product to their on-line users. Due to the interaction of millions customer with the e-commerce websites creates massive volume of data. Recommendation system is a dynamic data capturing system filters massive volume of information generated through the interaction of users to web-portals & generate suggestion that fits the user expectations. Recommendation framework is data filtering tools that make use of algorithms and user rating data to recommend the most relevant items to a particular user. Collaborative filtering is one of the succes
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Bathrinath S., Saranyadevi S., Thirumalai Kumaran S., and Saravanasankar S. "PageRank Algorithm-Based Recommender System Using Uniformly Average Rating Matrix." In Handbook of Research on Green Engineering Techniques for Modern Manufacturing. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5445-5.ch006.

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Applications of web data mining is the prediction of user behavior with respect to items. Recommender systems are being applied in knowledge discovery techniques to the problem of making decisions on personalized recommendation of information. Traditional CF approaches involve the amount of effort increases with number of users. Hence, new recommender systems need to be developed to process high quality recommendations for large-scale networks. In this chapter, a model for UAR matrix construction method for item rank calculations, a Page Rank-based item ranking approach are proposed. The analy
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Conference papers on the topic "Movie Lens 1M data"

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Krstić, I. B., B. M. Obradović, and M. M. Kuraica. "Spatio-temporal dynamics of a microsecond pulsed glow discharge." In International Meeting on Data for Atomic and Molecular Processes in Plasmas: Advances in Standards and Modelling. Institute of Physics Belgrade, 2024. https://doi.org/10.69646/aob241122.

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As a type of pulsed discharges, microsecond pulsed glow discharges (µs-PGD) brought an improvement of the analytical characteristics of the GD sources for several optical and mass spectrometric methods. Advantages of µs-PGD are increasing signal outputs and the temporal resolution of analytical species from concomitant species in the discharge plasma. Another benefit of µs-PGD is that even though instantaneous power is higher, the average power can be significantly lower than in millisecond PGD, resulting in reduced thermal stress of the analyzing sample. The higher instantaneous power of the
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