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Journal articles on the topic 'Movie Recommendation System'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 June 2025

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1

Shishodia, Dinesh. "Movie Recommendation System." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 4919–24. http://dx.doi.org/10.22214/ijraset.2021.35929.

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This paper represents the overview of Approaches and techniques used in Movie Recommendation system. Recommendation system is used by many companies like Netflix, Amazon, Flipkart etc. It makes the user experience better and decrease the user efforts. It plays a very vital role in our day-to-day life. It is used in recommending Movies, Articles, News, Books, Music, Videos, People (Online Dating) etc. It learns from the user past behavior and based on that behavior it recommends item to the user. Likewise, in Movie Recommendation system movie is recommended to the user on the basis of movies watched, liked, rated by the user. In year 2020, approximate 10,000 movie were launched according to IDMB data. It saves a lot of times and efforts of the user by suggesting movies according to user taste and user don’t have to select a movie from a large set of movies.
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B, Adithya. "Movie Recommendation System." International Journal for Research in Applied Science and Engineering Technology 8, no. 11 (2020): 120–22. http://dx.doi.org/10.22214/ijraset.2020.32064.

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., Darshini M., Abishay Raina ., Rakshit Mysore Lokesh ., Mohammed Noorulla Khan Durrani ., and T. H. Sreenivas . "MOVIE RECOMMENDATION SYSTEM." International Journal of Engineering Applied Sciences and Technology 03, no. 11 (2019): 39–41. http://dx.doi.org/10.33564/ijeast.2019.v03i11.008.

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Raj, Kunal, Atulya Abhinav Das, Antariksh Guha, Parth Sharma, and Mohana Kumar S. "Movie Recommendation System." International Journal of Computer Sciences and Engineering 7, no. 4 (2019): 1024–28. http://dx.doi.org/10.26438/ijcse/v7i4.10241028.

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Li, Bo, Yibin Liao, and Zheng Qin. "Precomputed Clustering for Movie Recommendation System in Real Time." Journal of Applied Mathematics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/742341.

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A recommendation system delivers customized data (articles, news, images, music, movies, etc.) to its users. As the interest of recommendation systems grows, we started working on the movie recommendation systems. Most research efforts in the fields of movie recommendation system are focusing on discovering the most relevant features from users, or seeking out users who share same tastes as that of the given user as well as recommending the movies according to the liking of these sought users or seeking out users who share a connection with other people (friends, classmates, colleagues, etc.) and make recommendations based on those related people’s tastes. However, little research has focused on recommending movies based on the movie’s features. In this paper, we present a novel idea that applies machine learning techniques to construct a cluster for the movie by implementing a distance matrix based on the movie features and then make movie recommendation in real time. We implement some different clustering methods and evaluate their performance in a real movie forum website owned by one of our authors. This idea can also be used in other types of recommendation systems such as music, news, and articles.
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Manavi, Vallari, Anjali Diwate, Priyanka Korade, and Anita Senathi. "MoView Engine : An Open Source Movie Recommender." ITM Web of Conferences 32 (2020): 03008. http://dx.doi.org/10.1051/itmconf/20203203008.

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Recommendation is an ideology that works as choice-based system for the end users. Users are recommended with their favorite movies based on history of other watched movies or based on the category of the movies. These types of recommendations are becoming popular because of their ability to think and react as human brain. For this purpose, deep learning or artificial intelligence comes into picture. It is the ability to think as a human brain as give the output best suited to the end users liking. This paper focuses on implementing the recommendation system of movies using deep learning with neural network model using the activation function of SoftMax to give an experience to users as friendly recommendation. Moreover, this paper focuses on different scenarios of recommendation like the recommendation based on history, genre of the movie etc.
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Verma, Rupal. "Movie Recommendation System by Using Collaborative Filtering." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 888–92. http://dx.doi.org/10.22214/ijraset.2021.38084.

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Abstract: This is the era of modern technology where we are all surrounded and covered by technology. This eases our daily life and saves our time and one of the most important techniques that played a very important role in our day-to-day life is the recommendation system. The recommendation system is used in various fields like it is used to recommend products, books, videos, movies, news, and many more. In this paper, we use a Recommendation system for movies we built or a movie recommendation system. It is based on a collaborative filtering approach that makes use of the information provided by the users, analyzes them and recommends movies according to the taste of users. The recommended movie list sorted according to the ratings given to this system is developed in python by using pycharm IDE and MYSQL for database connectivity. The presented recommendation system generates recommendations using various types of knowledge and data about users. Our Recommendation system recommends movies to each and every user by their previous searching history. Here we use some searching techniques as well. We also tried to overcome the cold start problem we use Movielens database. Keywords: Collaborative-filtering, Content-based filtering, Clustering, Recommendation system searching technique, Movies
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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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M, Shobana. "Movie Recommendation System using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 4925–29. http://dx.doi.org/10.22214/ijraset.2021.35990.

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A movie recommendation is important in our social life due to its strength in providing enhanced entertainment. Such a system can suggest a set of movies to users based on their interest, or the popularities of the movies. A recommendation system is used for the purpose of suggesting items to purchase or to see. They direct users towards those items which can meet their needs through cutting down large database of Information. A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item. They are primarily used in commercial applications. MOVREC also help users to find the movies of their choices based on the movie experience of other users in efficient and effective manner without wasting much time in useless browsing
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Deshmukh, Puja, and Geetanjali Kale. "Music and Movie Recommendation System." International Journal of Engineering Trends and Technology 61, no. 3 (2018): 178–81. http://dx.doi.org/10.14445/22315381/ijett-v61p229.

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Malve, Gandhali, Lajree Lohar, Tanay Malviya, and Shirish Sabnis. "Movie Recommenadation System." International Journal on Recent and Innovation Trends in Computing and Communication 9, no. 4 (2021): 13–16. http://dx.doi.org/10.17762/ijritcc.v9i4.5460.

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Today the amount of information in the internet growth very rapidly and people need some instruments to find and access appropriate information. One of such tools is called recommendation system. Recommendation systems help to navigate quickly and receive necessary information. Many of us find it difficult to decide which movie to watch and so we decided to make a recommender system for us to better judge which movie we are more likely to love. In this project we are going to use Machine Learning Algorithms to recommend movies to users based on genres and user ratings. Recommendation system attempt to predict the preference or rating that a user would give to an item.
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Ibrahim, Muhammad, and Imran Bajwa. "Design and Application of a Multi-Variant Expert System Using Apache Hadoop Framework." Sustainability 10, no. 11 (2018): 4280. http://dx.doi.org/10.3390/su10114280.

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Movie recommender expert systems are valuable tools to provide recommendation services to users. However, the existing movie recommenders are technically lacking in two areas: first, the available movie recommender systems give general recommendations; secondly, existing recommender systems use either quantitative (likes, ratings, etc.) or qualitative data (polarity score, sentiment score, etc.) for achieving the movie recommendations. A novel approach is presented in this paper that not only provides topic-based (fiction, comedy, horror, etc.) movie recommendation but also uses both quantitative and qualitative data to achieve a true and relevant recommendation of a movie relevant to a topic. The used approach relies on SentiwordNet and tf-idf similarity measures to calculate the polarity score from user reviews, which represent the qualitative aspect of likeness of a movie. Similarly, three quantitative variables (such as likes, ratings, and votes) are used to get final a recommendation score. A fuzzy logic module decides the recommendation category based on this final recommendation score. The proposed approach uses a big data technology, “Hadoop” to handle data diversity and heterogeneity in an efficient manner. An Android application collaborates with a web-bot to use recommendation services and show topic-based recommendation to users.
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You, Sang-Hyun, Jeawon Park, and Jaehyun Choi. "Personal Preference Based Movie Recommendation System." International Journal of Multimedia and Ubiquitous Engineering 9, no. 9 (2016): 11–18. http://dx.doi.org/10.14257/ijmue.2016.11.9.02.

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Vibhandik, Ghanashyam. "Movie Recommendation System using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 4778–81. http://dx.doi.org/10.22214/ijraset.2021.35741.

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Movies are very significant in our lives. It is one of the many forms of entertainment that we encounter in our daily lives. It is up to the individual to decide whatever type of film they choose to see, whether it is a comedy, romantic film, action film, or adventure film. However, the issue is locating acceptable content, as there is a large amount of information created each year. As a result, finding our favourite film is really difficult. The goal of this research is to improve the regular filtering technique's performance and accuracy. A recommendation system can be implemented using a variety of approaches. Content-based filtering and collaborative filtering strategies are employed in this work. The content-based filtering approach analyses the user's history/past behaviour and recommends a list of comparable movies depending on their input. K-NN algorithms and collaborative filtering are also employed in this paper to improve the accuracy of the results. Cosine similarity is utilised in this work to quickly discover comparable information. The correctness of the cosine angle is measured by cosine similarity. People may quickly find their favourite movie content thanks to all of this.
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Desai, Harshali. "Movie Recommendation System through Movie Poster using Deep Learning Technique." International Journal for Research in Applied Science and Engineering Technology 9, no. 4 (2021): 1574–81. http://dx.doi.org/10.22214/ijraset.2021.33947.

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Ng, Yiu-Kai. "MovRec: a personalized movie recommendation system for children based on online movie features." International Journal of Web Information Systems 13, no. 4 (2017): 445–70. http://dx.doi.org/10.1108/ijwis-05-2017-0043.

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Purpose The purpose of this study is to suggest suitable movies for children among the various multimedia selections available these days. Multimedia have a significant impact on the social and psychological development of children who are often explored to inappropriate materials, including movies that are either accessible online or through other multimedia channels. Even though not all movies are bad, there are negative effects of offensive languages, violence and sexuality as exhibited in movies. Parents and guidance of children need all the help they can get to promote the healthy use of movies these days. Design/methodology/approach To offer parents appropriate movies of interest to their youths, the authors have developed MovRec, a personalized movie recommender for children, which is designed to provide educational and suitable entertaining opportunities for children. MovRec determines the appealingness of a movie for a particular user based on its children-appropriate score computed by using the backpropagation model, pre-defined category using latent Dirichlet allocation, its predicted rating using matrix factorization and sentiments based on its users’ reviews, which along with its like/dislike count and genres, yield the features considered by MovRec. MovRec combines these features by using the CombMNZ model to rank and recommend movies. Findings The performance evaluation of MovRec clearly demonstrates its effectiveness and its recommended movies are highly regarded by its users. Originality/value Unlike Amazon and other online movie recommendation systems, such as Common Sense Media, Internet Movie Database and TasteKid, MovRec is unique, as to the best of the authors’ knowledge, MovRec is the first personalized children movie recommender.
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Rismala, Rita, Rudy Prabowo, and Agung Toto Wibowo. "Pairwise Preference Regression on Movie Recommendation System." Indonesian Journal on Computing (Indo-JC) 4, no. 1 (2019): 57. http://dx.doi.org/10.21108/indojc.2019.4.1.255.

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Recommendation System is able to help users to choose items, including movies, that match their interests. One of the problems faced by recommendation system is cold-start problem. Cold start problem can be categorized into three types, they are: recommending existed item for new user, recommending new item for existed user, and recommending new item for new user. Pairwise preference regression is a method that directly deals with cold-start problem. This method can suggest a recommendation, not only for users who have no historical rating, but also for those who only have less demographic info. From the experiment result, the best score of Normalized Discounted Cumulative Gain (nDGC) from the system is 0.8484. The standard deviation of rating resulted by the recommendation system is 1.24, the average is 3.82. Consequently, the distribution of recommendation result is around rating 5 to 3. Those results mean that this recommendation system is good to solving cold-start problem in movie recommendation system.
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Wang, Yibo, Mingming Wang, and Wei Xu. "A Sentiment-Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework." Wireless Communications and Mobile Computing 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/8263704.

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Movie recommendation in mobile environment is critically important for mobile users. It carries out comprehensive aggregation of user’s preferences, reviews, and emotions to help them find suitable movies conveniently. However, it requires both accuracy and timeliness. In this paper, a movie recommendation framework based on a hybrid recommendation model and sentiment analysis on Spark platform is proposed to improve the accuracy and timeliness of mobile movie recommender system. In the proposed approach, we first use a hybrid recommendation method to generate a preliminary recommendation list. Then sentiment analysis is employed to optimize the list. Finally, the hybrid recommender system with sentiment analysis is implemented on Spark platform. The hybrid recommendation model with sentiment analysis outperforms the traditional models in terms of various evaluation criteria. Our proposed method makes it convenient and fast for users to obtain useful movie suggestions.
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Zhubatkhan, A. Y., Z. A. Buribayev, S. S. Aubakirov, M. D. Dilmagambetova, and S. A. Ryskulbek. "COMPARISON MODELS OF MACHINE LEARNING FOR MOVIE RECOMMENDATION SYSTEMS." PHYSICO-MATHEMATICAL SERIES 335, no. 1 (2021): 26–31. http://dx.doi.org/10.32014/2021.2224-5294.4.

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The trend of the Internet makes the presentation of the right content for the right user inevitable. To this end, recommendation systems are used in areas such as music, books, movies, travel planning, e-commerce, education, and more. One of the most popular recommendation systems in the world is Netflix, which generated record profits during quarantine in the first quartile of 2020. The systematic approach of recommendations is based on the history of user selections, likes and reviews, each of which is interpreted to predict future user selections. This article provides a meaningful analysis of various recommendation systems, such as content-based, collaborative filtering and popularity. We reviewed 7 articles published from 2005 to 2019 to discuss issues related to existing models. The purpose of this article is to compare machine learning algorithms in the Surprise library for a recommendation system. Recommendation system has been implemented and quality has been evaluated using the MAE and RMSE metrics.
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Zhubatkhan, A. Y., Z. A. Buribayev, S. S. Aubakirov, M. D. Dilmagambetova, and S. A. Ryskulbek. "COMPARISON MODELS OF MACHINE LEARNING FOR MOVIE RECOMMENDATION SYSTEMS." PHYSICO-MATHEMATICAL SERIES 335, no. 1 (2021): 26–31. http://dx.doi.org/10.32014/2021.2518-1726.4.

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The trend of the Internet makes the presentation of the right content for the right user inevitable. To this end, recommendation systems are used in areas such as music, books, movies, travel planning, e-commerce, education, and more. One of the most popular recommendation systems in the world is Netflix, which generated record profits during quarantine in the first quartile of 2020. The systematic approach of recommendations is based on the history of user selections, likes and reviews, each of which is interpreted to predict future user selections. This article provides a meaningful analysis of various recommendation systems, such as content-based, collaborative filtering and popularity. We reviewed 7 articles published from 2005 to 2019 to discuss issues related to existing models. The purpose of this article is to compare machine learning algorithms in the Surprise library for a recommendation system. Recommendation system has been implemented and quality has been evaluated using the MAE and RMSE metrics.
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Komurlekar, Runali. "Movie Recommendation Model from Data through Online Streaming." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 1549–51. http://dx.doi.org/10.22214/ijraset.2021.37495.

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Abstract: With the Pandemic era and easy availability of internet, potential of digital movie and tv series industry is in huge demand. Hence it has led to developing an automatic movie recommendation engine and has become a popular issue. Some of these problems can be solved or at least be minimized if we take the right decisions on what kind of movies to ignore, what movies to consider. This paper examines the recommendations that are obtained with considering the sample movies that have never got an above-average rating, where average rating is defined here as the mid-value between 0 and maximum rating used, for example, 2.5 in 1 to 5 rating scale. The technique used is “collaborative filtering”. Comparison of different pre-training model, it is tried to maximize the effectiveness of semantic understanding and make the recommendation be able to reflect meticulous perception on the relationship between user utilisation and user preference. Keywords: movie recommendation system, user similarity, user similarity, consumption pattern
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Parthasarathy, Jayaraman, and Ramesh Babu Kalivaradhan. "Collaborative Filtering-Based Recommendation System Using Time Decay Model." International Journal of e-Collaboration 17, no. 3 (2021): 85–100. http://dx.doi.org/10.4018/ijec.2021070106.

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Online collaborative movie recommendation systems attempt to help customers accessing their favourable movies by gathering exactly comparable neighbors between the movies from their chronological identical ratings. Collaborative filtering-based movie recommendation systems require viewer-specific data, and the need for collecting viewer-specific data diminishes the effectiveness of the recommendation. To solve this problem, the authors employ an effective multi-armed bandit called upper confidence bound, which is applied to automatically recommend the movies for the users. In addition, the concept of time decay is provided in a mathematical definition that redefines the dynamic item-to-item similarity. Then, two patterns of time decay are analyzed, namely concave and convex functions, for simulation. The experiment test the MovieLens 100K dataset. The proposed method attains a maximum F-measure of 98.45 whereas the existing method reaches a minimum F-measure of only 95.60. The presented model adaptively responds to new users, can provide a better service, and generate more user engagement.
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Li, Min, Yingming Zeng, Yue Guo, and Yun Guo. "A Movie Recommendation System Based on Differential Privacy Protection." Security and Communication Networks 2020 (December 16, 2020): 1–10. http://dx.doi.org/10.1155/2020/6611463.

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In the past decades, the ever-increasing popularity of the Internet has led to an explosive growth of information, which has consequently led to the emergence of recommendation systems. A series of cloud-based encryption measures have been adopted in the current recommendation systems to protect users’ privacy. However, there are still many other privacy attacks on the local devices. Therefore, this paper studies the encryption interference of applying a differential privacy protection scheme on the data in the user’s local devices under the assumption of an untrusted server. A dynamic privacy budget allocation method is proposed based on a localized differential privacy protection scheme while taking the specific application scene of movie recommendation into consideration. What is more, an improved user-based collaborative filtering algorithm, which adopts a matrix-based similarity calculation method instead of the traditional vector-based method when computing the user similarity, is proposed. Finally, it was proved by experimental results that the differential privacy-based movie recommendation system (DP-MRE) proposed in this paper could not only protect the privacy of users but also ensure the accuracy of recommendations.
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Dhiwar S., Ms Pooja. "Movie Review System using Sentiment Analysis and Social Networking Platforms (SNPs)." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 1492–99. http://dx.doi.org/10.22214/ijraset.2021.35308.

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Today online social networking platforms SNPs have become an integral part of or life where we share a lot of information of all the things, we do in life from shopping to movie watching. With ever growing use of SNPs recommendation systems have emerged as a hot trend for applications in e-commerce and digital media. These recommendation systems are useful as well as misguiding. Today digital media use has increased tremendously with increase in internet speeds. But users do not get proper review of a movie and a user is lured to watch a substandard movie which he does not intended to do, thus costing a user time and money. So, there is a need of developing a movie review which will give correct reviews of a digital content like movies so he can only movies which he intends to do. So, we are studying various techniques authored by various authors and create a good movie review system of our own. The first technique we studied and intends to use is movie recommendation system using tweets. The second study is movie recommendation using similarity measures. The third study does find a public shamming using SNP. These techniques are useful and we propose to use some part of each in our new movie review framework by improving the techniques drawbacks. The new framework will be a combination of data from more than one SNP and using natural language processing and machine learning on the data. We are going to use two machine learning algorithms SVM and Naïve Bayes for this purpose. For natural language processing of SNP data, we are going to use OPEN-NLP. We intend to use SNPs such as Twitter and any other movie database like IMDB etc. for data on the movie. The movie will be classified in three classes bad, good and excellent. The results from each algorithm SVM and Naïve bayes will be analyzed for each SNP and try to give user a more accurate movie review by combining all the reviews together and classes accuracy and show overall prediction results with a rating. To get more accurate results for each movie we are going to create a dataset for each movie for demonstration and will not depend on a single combined dataset as keywords for each movie may be different. We are going to combine datasets for each movie from multiple SNPs.
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Jain, Arushi, and Vishal Bhatnagar. "Movie Analytics for Effective Recommendation System using Pig with Hadoop." International Journal of Rough Sets and Data Analysis 3, no. 2 (2016): 82–100. http://dx.doi.org/10.4018/ijrsda.2016040106.

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Movies have been a great source of entertainment for the people ever since their inception in the late 18th century. The term movie is very broad and its definition contains language and genres such as drama, comedy, science fiction and action. The data about movies over the years is very vast and to analyze it, there is a need to break away from the traditional analytics techniques and adopt big data analytics. In this paper the authors have taken the data set on movies and analyzed it against various queries to uncover real nuggets from the dataset for effective recommendation system and ratings for the upcoming movies.
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Vlachos, Michail, and Daniel Svonava. "Recommendation and visualization of similar movies using minimum spanning dendrograms." Information Visualization 12, no. 1 (2012): 85–101. http://dx.doi.org/10.1177/1473871612439644.

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Exploration of graph structures is an important topic in data mining and data visualization. This work presents a novel technique for visualizing neighbourhood and cluster relationships in graphs; we also show how this methodology can be used within the setting of a recommendation system. Our technique works by projecting the original object distances onto two dimensions while carefully retaining the ‘backbone’ of important distances. Cluster information is also overlayed on the same projected space. A significant advantage of our approach is that it can accommodate both metric and non-metric distance functions. Our methodology is applied to a visual recommender system for movies to allow easy exploration of the actor–movie bipartite graph. The work offers intuitive movie recommendations based on a selected pivot movie and allows the interactive discovery of related movies based on both textual and semantic features.
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Nath, Asoke, Adityam Ghosh, and Arion Mitra. "Building a Movie Recommendation System using SVD algorithm." International Journal of Computer Sciences and Engineering 6, no. 11 (2018): 727–29. http://dx.doi.org/10.26438/ijcse/v6i11.727729.

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Raghuwanshi, S. K., and R. K. Pateriya. "Movie Recommendation System Content-Based and Collaborative Filtering." International Journal of Computer Sciences and Engineering 6, no. 4 (2018): 476–81. http://dx.doi.org/10.26438/ijcse/v6i4.476481.

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Priscilla, S., and C. Naveena. "Social Balance Theory Based Hybrid Movie Recommendation System." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4022–25. http://dx.doi.org/10.1166/jctn.2020.9012.

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Suggesting people exactly according to their likeness is most challenging in today’s generation. Present websites fail to provide the recommendation that is appropriate for people. There are several reasons such as there is either inadequate information about people or absence of feedback from the movies that they have watched. In this Situation considering those few/Sparse scores that are given that are collected from the people a socially balanced concept came into picture. Socially balanced theory Concept (hybrid) uses a integrated recommendation by combining both substance—oriented and community organized approach i.e., recommends based on both on viewers as well as movie. Socially balance theory helps to get better suggestion even when there is less information or inappropriate content by finding the opponent for the end users later discover the end users companion i.e., “opponents opponent is a companion” rule in social balance theory. So that suggestions can be based on both customer as well as goods based. For this, initially grouping the community is required to find the similarity between them. Finally the workability of integrated—recommendation is evaluated by considering film lens dataset – 10 M.
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Sadhasivam, Jayakumar, Juan Manuel Cera, R. Deepa, et al. "Movie recommendation system using clustering mining with Python." Journal of Physics: Conference Series 1964, no. 4 (2021): 042073. http://dx.doi.org/10.1088/1742-6596/1964/4/042073.

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Siddiquee, Mahfuzur Rahman, Naimul Haider, and Rashedur M. Rahman. "Movie Recommendation System Based on Fuzzy Inference System and Adaptive Neuro Fuzzy Inference System." International Journal of Fuzzy System Applications 4, no. 4 (2015): 31–69. http://dx.doi.org/10.4018/ijfsa.2015100103.

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One of most prominent features that social networks or e-commerce sites now provide is recommendation of items. However, the recommendation task is challenging as high degree of accuracy is required. This paper analyzes the improvement in recommendation of movies using Fuzzy Inference System (FIS) and Adaptive Neuro Fuzzy Inference System (ANFIS). Two similarity measures have been used: one by taking account similar users' choice and the other by matching genres of similar movies rated by the user. For similarity calculation, four different techniques, namely Euclidean Distance, Manhattan Distance, Pearson Coefficient and Cosine Similarity are used. FIS and ANFIS system are used in decision making. The experiments have been carried out on Movie Lens dataset and a comparative performance analysis has been reported. Experimental results demonstrate that ANFIS outperforms FIS in most of the cases when Pearson Correlation metric is used for similarity calculation.
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Rao, P. Rama. "Movie Recommending System Using Collaborative Filtering." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1034–38. http://dx.doi.org/10.22214/ijraset.2021.36377.

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Movies are one of the sources of entertainment, but the problem is in finding the content of our choice because content is increasing every year. However, recommendation systems plays here an important role for finding the content of desired domain in these situations. The aim of this paper is to improve the accuracy and performance of a filtration techniques existed. There are several methods and algorithms existed to implement a recommendation system. Content-based filtering is the simplest method, it takes input from the users, checks the movie and its content and recommends a list of similar movies. In this paper, to prove the effectiveness of our system, K-NN algorithms and collaborative filtering are used. Here, the usage of cosine similarity is done for recommending the nearest neighbours.
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33

Sharma, Mugdha, Laxmi Ahuja, and Vinay Kumar. "A Novel Rule based Data Mining Approach towards Movie Recommender System." Journal of information and organizational sciences 44, no. 1 (2020): 157–70. http://dx.doi.org/10.31341/jios.44.1.7.

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The proposed research work is an effort to provide accurate movie recommendations to a group of users with the help of a rule-based content-based group recommender system. The whole approach is categorized into 2 phases. In phase 1, a rule- based approach has been proposed which considers the users’ viewing history to provide the Rule Base for every individual user. In phase 2, a novel group recommendation system has been proposed which considers the ratings of the movies as per the rule base generated in phase 1. Phase 2 also considers the weightage of every individual member of the group to provide the accurate movie recommendation to that particular group of users. The results of experimental setup also establish the fact that the proposed system provides more accurate outcomes in terms of precision and recall over other rule learning algorithms such as C4.5.
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34

Hwang, Tae-Gyu, and Sung Kwon Kim. "Movie Recommendation through Multiple Bias Analysis." Applied Sciences 11, no. 6 (2021): 2817. http://dx.doi.org/10.3390/app11062817.

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A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.
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35

Ibrahim, Muhammad, Imran Sarwar Bajwa, Riaz Ul-Amin, and Bakhtiar Kasi. "A Neural Network-Inspired Approach for Improved and True Movie Recommendations." Computational Intelligence and Neuroscience 2019 (August 4, 2019): 1–19. http://dx.doi.org/10.1155/2019/4589060.

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In the last decade, sentiment analysis, opinion mining, and subjectivity of microblogs in social media have attracted a great deal of attention of researchers. Movie recommendation systems are the tools, which provide valuable services to the users. The data available online are growing gradually because the online activities of users or viewers are increasing day by day. Because of this, big data, analytics, and computational issues have raised. Therefore, we have to improve recommendations services upon the traditional one to make the recommendation system significant and efficient. This article presents the solution for these issues by producing the significant and efficient recommendation services using multivariates (ratings, votes, Twitter likes, and reviews) of movies from multiple external resources which are fetched by the web bot and managed by the Apache Hadoop framework in a distributed manner. Reviews are analyzed by a deep semantic analyzer based on the recurrent neural network (RNN/LSTM attention) with user movie attention (UMA) to produce the emotion. The proposed recommender evaluates multivariates and produces a more significant movie recommendation list according to the taste of the user on a mobile app in an efficient way.
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36

Nguyen, Sang Thi Thanh, and Bao Duy Tran. "Long Short-Term Memory Based Movie Recommendation." Science & Technology Development Journal - Engineering and Technology 3, SI1 (2020): SI1—SI9. http://dx.doi.org/10.32508/stdjet.v3isi1.540.

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Recommender systems (RS) have become a fundamental tool for helping users make decisions around millions of different choices nowadays – the era of Big Data. It brings a huge benefit for many business models around the world due to their effectiveness on the target customers. A lot of recommendation models and techniques have been proposed and many accomplished incredible outcomes. Collaborative filtering and content-based filtering methods are common, but these both have some disadvantages. A critical one is that they only focus on a user's long-term static preference while ignoring his or her short-term transactional patterns, which results in missing the user's preference shift through the time. In this case, the user's intent at a certain time point may be easily submerged by his or her historical decision behaviors, which leads to unreliable recommendations. To deal with this issue, a session of user interactions with the items can be considered as a solution. In this study, Long Short-Term Memory (LSTM) networks will be analyzed to be applied to user sessions in a recommender system. The MovieLens dataset is considered as a case study of movie recommender systems. This dataset is preprocessed to extract user-movie sessions for user behavior discovery and making movie recommendations to users. Several experiments have been carried out to evaluate the LSTM-based movie recommender system. In the experiments, the LSTM networks are compared with a similar deep learning method, which is Recurrent Neural Networks (RNN), and a baseline machine learning method, which is the collaborative filtering using item-based nearest neighbors (item-KNN). It has been found that the LSTM networks are able to be improved by optimizing their hyperparameters and outperform the other methods when predicting the next movies interested by users.
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37

Behera, Rabi Narayan, and Sujata Dash. "A Particle Swarm Optimization based Hybrid Recommendation System." International Journal of Knowledge Discovery in Bioinformatics 6, no. 2 (2016): 1–10. http://dx.doi.org/10.4018/ijkdb.2016070101.

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Due to rapid digital explosion user shows interest towards finding suggestions regarding a particular topic before taking any decision. Nowadays, a movie recommendation system is an upcoming area which suggests movies based on user profile. Many researchers working on supervised or semi-supervised ensemble based machine learning approach for matching more appropriate profiles and suggest related movies. In this paper a hybrid recommendation system is proposed which includes both collaborative and content based filtering to design a profile matching algorithm. A nature inspired Particle Swam Optimization technique is applied to fine tune the profile matching algorithm by assigning to multiple agents or particle with some initial random guess. The accuracy of the model will be judged comparing with Genetic algorithm.
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38

Ishida, Yuto, Takahiro Uchiya, and Ichi Takumi. "Design and evaluation of a movie recommendation system showing a review for evoking interested." International Journal of Web Information Systems 13, no. 1 (2017): 72–84. http://dx.doi.org/10.1108/ijwis-12-2016-0073.

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Purpose In recent years, e-commerce (EC) sites dealing in various goods and services have increased along with internet popularity. Now, very few EC recommendation systems present a concrete reason for their recommendations. Therefore, because user preferences strongly influence outcomes, evaluation and selection are difficult for items, such as books, movies and luxury goods. The purpose of this paper is evoking interest by showing the review as a reason for a user’s decision-making factor. This paper aims to presents the development and introduction of a recommendation system that presents a review adapted to user preference. Design/methodology/approach The system presents a review to the user, which indicates the reason for matching the item contents and user preferences. Thereby, this system enables the creation of personalized reasons for recommendations. Findings Recommendation sentences conforming to user preferences are effective for item selection. Even with a simple method, in this paper, it was possible to present a review which is an item selection factor sufficient for the user. Originality/value This system can show a recommendation sentence that conforms to a user’s preferences merely from a user profile with the tag data of a product. This paper dealt in movies, but it can easily be applied even for other items.
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39

Raigoza, Jaime, and Vikrantsinh Karande. "A Study and Implementation of a Movie Recommendation System in a Cloud-based Environment." International Journal of Grid and High Performance Computing 9, no. 1 (2017): 25–36. http://dx.doi.org/10.4018/ijghpc.2017010103.

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The availability of huge amounts of data in recent years have led users to being faced with an overload of choices. The outcome is a growth on the importance of recommendation systems due to their ability to solve this choice overload problem, by providing users with the most relevant products from many possible choices. For producing recommendations, things like a user's psychological profile, their browsing history and movie ratings from other users can be considered. To determine how strongly two user's behavior are related to each other, a Pearson correlation coefficient value is often calculated. In this paper, we study the recommendation system on a proposed cloud based environment to produce a list of recommended movies based on a user's profile information. Based on the Software-as-a-Service (SaaS) model implemented, we discuss the concepts such as collaborative filtering and content-based filtering. Given a MovieLens data-set, our results indicate that the proposed approach can provide a high performance in terms of precision, and generate more reliable and personalized movie recommendations, when given a greater number of movies rated by a user. An evaluation was done under minimal known data, which commonly leads to the cold-start problem.
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40

Wang, Zan, Xue Yu, Nan Feng, and Zhenhua Wang. "An improved collaborative movie recommendation system using computational intelligence." Journal of Visual Languages & Computing 25, no. 6 (2014): 667–75. http://dx.doi.org/10.1016/j.jvlc.2014.09.011.

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41

Karuppiah, Marimuthu, Hamid Reza Karimi, D. Malathi, V. Vijayakumar, R. Logesh, and V. Subramaniyaswamy. "Effective user preference mining-based personalised movie recommendation system." International Journal of Computer Aided Engineering and Technology 13, no. 3 (2020): 371. http://dx.doi.org/10.1504/ijcaet.2020.10029314.

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42

Subramaniyaswamy, V., R. Logesh, D. Malathi, V. Vijayakumar, Hamid Reza Karimi, and Marimuthu Karuppiah. "Effective user preference mining-based personalised movie recommendation system." International Journal of Computer Aided Engineering and Technology 13, no. 3 (2020): 371. http://dx.doi.org/10.1504/ijcaet.2020.109521.

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43

崔, 苹. "Personalized Movie Recommendation System Based on LDA Theme Extension." Computer Science and Application 08, no. 06 (2018): 860–66. http://dx.doi.org/10.12677/csa.2018.86095.

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44

Subramaniyaswamy, V., R. Logesh, M. Chandrashekhar, Anirudh Challa, and V. Vijayakumar. "A personalised movie recommendation system based on collaborative filtering." International Journal of High Performance Computing and Networking 10, no. 1/2 (2017): 54. http://dx.doi.org/10.1504/ijhpcn.2017.083199.

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45

Vijayakumar, V., Anirudh Challa, M. Chandrashekhar, V. Subramaniyaswamy, and R. Logesh. "A personalised movie recommendation system based on collaborative filtering." International Journal of High Performance Computing and Networking 10, no. 1/2 (2017): 54. http://dx.doi.org/10.1504/ijhpcn.2017.10003762.

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46

Kumar, Sudhanshu, Kanjar De, and Partha Pratim Roy. "Movie Recommendation System Using Sentiment Analysis From Microblogging Data." IEEE Transactions on Computational Social Systems 7, no. 4 (2020): 915–23. http://dx.doi.org/10.1109/tcss.2020.2993585.

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47

Farhan, S. A. Azeem. "Building A Movie Recommendation System Using Collaborative Filtering With TF-IDF." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 181–91. http://dx.doi.org/10.22214/ijraset.2021.37817.

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Abstract: The recommendation problem involves the prediction of a set of items that maximize the utility for users. As a solution to this problem, a recommender system is an information filtering system that seeks to predict the rating given by a user to an item. There are theree types of recommendation systesms namely Content based, Collaborative based and the Hybrid based Recommendation systems. The collaborative filtering is further classified into the user based collaborative filtering and item based collaborative filtering. The collaborative filtering (CF) based recommendation systems are capable of grasping the interaction or correlation of users and items under consideration. We have explored most of the existing collaborative filteringbased research on a popular TMDB movie dataset. We found out that some key features were being ignored by most of the previous researches. Our work has given significant importance to 'movie overviews' available in the dataset. We experimented with typical statistical methods like TF-IDF , By using tf-idf the dimensions of our courps(overview and other text features) explodes, which creates problems ,we have tackled those problems using a dimensionality reduction technique named Singular Value Decomposition(SVD). After this preprocessing the Preprocessed data is being used in building the models. We have evaluated the performance of different machine learning algorithms like Random Forest and deep neural networks based BiLSTM. The experiment results provide a reliable model in terms of MAE(mean absolute error) ,RMSE(Root mean squared error) and the Bi-LSTM turns out to be a better model with an MAE of 0.65 and RMSE of 1.04 ,it generates more personalized movie recommendations compared to other models. Keywords: Recommender system, item-based collaborative filtering, Natural Language Processing, Deep learning.
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48

Lavanya, R., and B. Bharathi. "Movie Recommendation System to Solve Data Sparsity Using Collaborative Filtering Approach." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 5 (2021): 1–14. http://dx.doi.org/10.1145/3459091.

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With the increase in numbers of multimedia technologies around us, movies and videos on social media and OTT platforms are growing, making it confusing for users to decide which one to watch for. For this, movie recommendation systems are widely used. It has been observed that two-thirds of the films watched on Netflix are the recommended ones to its users. The target of this work is to use implicit feedback given by other users to recommend movies, i.e., ratings given by them. Implicit feedback will help to enhance Data Sparsity as for a replacement logged-in user, the system won't have details of their past liked movies. So, matching the similarity with other users is often a plus point to recommend movies that they would like. The anticipated result will depend upon the positive attitude; i.e., if the predicted rating is high, then it'll be recommended; otherwise it'll not be recommended. The performance of the methodology is measured with accuracy and precision values for different strategies. It gives the best accuracy and highest precision values using Logistic Regression (LR) and lowest recall value as compared to other algorithms. This technique gives an accuracy, precision, and recall value of 81.9%, 69.82%, and 32.5%, respectively, using LR.
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49

Nosshi, Anthony, Aziza Saad Asem, and Mohammed Badr Senousy. "Hybrid Recommender System Using Emotional Fingerprints Model." International Journal of Information Retrieval Research 9, no. 3 (2019): 48–70. http://dx.doi.org/10.4018/ijirr.2019070104.

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With today's information overload, recommender systems are important to help users in finding needed information. In the movies domain, finding a good movie to watch is not an easy task. Emotions play an important role in deciding which movie to watch. People usually express their emotions in reviews or comments about the movies. In this article, an emotional fingerprint-based model (EFBM) for movies recommendation is proposed. The model is based on grouping movies by emotional patterns of some key factors changing in time and forming fingerprints or emotional tracks, which are the heart of the proposed recommender. Then, it is incorporated into collaborative filtering to detect the interest connected with topics. Experimental simulation is conducted to understand the behavior of the proposed approach. Results are represented to evaluate the proposed recommender.
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Awan, Mazhar Javed, Rafia Asad Khan, Haitham Nobanee, et al. "A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach." Electronics 10, no. 10 (2021): 1215. http://dx.doi.org/10.3390/electronics10101215.

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In this era of big data, the amount of video content has dramatically increased with an exponential broadening of video streaming services. Hence, it has become very strenuous for end-users to search for their desired videos. Therefore, to attain an accurate and robust clustering of information, a hybrid algorithm was used to introduce a recommender engine with collaborative filtering using Apache Spark and machine learning (ML) libraries. In this study, we implemented a movie recommendation system based on a collaborative filtering approach using the alternating least squared (ALS) model to predict the best-rated movies. Our proposed system uses the last search data of a user regarding movie category and references this to instruct the recommender engine, thereby making a list of predictions for top ratings. The proposed study used a model-based approach of matrix factorization, the ALS algorithm along with a collaborative filtering technique, which solved the cold start, sparse, and scalability problems. In particular, we performed experimental analysis and successfully obtained minimum root mean squared errors (oRMSEs) of 0.8959 to 0.97613, approximately. Moreover, our proposed movie recommendation system showed an accuracy of 97% and predicted the top 1000 ratings for movies.
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