Статті в журналах: "HYBRID MOVIE"

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Nosshi, Anthony, Aziza Asem, and Mohamed Badr Senousy. "Hybrid Recommender System via Personalized Users’ Context." Cybernetics and Information Technologies 19, no. 1 (March 1, 2019): 101–15. http://dx.doi.org/10.2478/cait-2019-0006.

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Abstract In movie domain, finding the appropriate movie to watch is a challenging task. This paper proposes a recommender system that suggests movies in cinema that fit the user’s available time, location, mood and emotions. Conducted experiments for evaluation showed that the proposed method outperforms the other baselines.

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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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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 (July 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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Tripathi, Jyoti, Sunita Tiwari, Anu Saini, and Sunita Kumari. "Prediction of movie success based on machine learning and twitter sentiment analysis using internet movie database data." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 3 (March 1, 2023): 1750. http://dx.doi.org/10.11591/ijeecs.v29.i3.pp1750-1757.

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Nowadays, predicting the success of a new movie is a crucial task. In this work, the hybrid approach considers the movie features as well as sentiment expressed in the movie review to predict the success rate of a movie. Multiple movie features such as title, director, star cast, and writer. Are considered for prediction. The related raw data is collected from the internet movie database (IMDb) website and after pre-processing, the collected data is used to generate the supervised machine learning model. Different supervised learning models are compared and the one with the best results is used further. The mean squared error, root mean squared error and r2 score of the models generated are comparable with existing models. Further, sentiment analysis of the movie-related tweets is performed. The accuracy of best sentiment analysis model is 88.47%. Finally, the two models are combined to give the success prediction rating of new movies and the results of the hybrid model are encouraging. The proposed model may be used to find the top-rated movies of a particular calendar year.

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Bohra, Sneha, Amit Gaikwad, and Ghanapriya Singh. "Hybrid Machine Learning Based Recommendation Algorithm for Multiple Movie Dataset." Indian Journal Of Science And Technology 16, no. 37 (October 9, 2023): 3121–28. http://dx.doi.org/10.17485/ijst/v16i37.2065.

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6

Mohile, Sara, Hemant Ramteke, Pragati Shelgaonkar, Hritika Phule, and M. M. Phadtare. "A Movie Recommender System Using Hybrid Approach: A Review." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 1834–37. http://dx.doi.org/10.22214/ijraset.2022.41014.

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Abstract: The topic of this paper is movie suggestions. Because of its ability to provide improved entertainment, a movie recommendation is vital in our social lives. Users can be recommended a set of movies depending on their interests or admiration for the films by such a system. A recommendation system is used to make suggestions for things to buy or see. They employ a big collection of information to steer consumers to the things that will best match their needs. A recommender system, also known as a recommendation system, is a type of material filtering system that attempts to forecast a user's "rating" or "preference" for an item. They're mostly employed for commercial purposes. MOVREC also assists users in efficiently and effectively locating movies of their choice based on the movie experiences of other users, without wasting time in pointless searching. Keywords: Filtering, Recommendation System, Recommender.

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Lekakos, George, and Petros Caravelas. "A hybrid approach for movie recommendation." Multimedia Tools and Applications 36, no. 1-2 (December 21, 2006): 55–70. http://dx.doi.org/10.1007/s11042-006-0082-7.

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Jadhav, Prof Rupali. "Implementing a Movie Recommendation System in Machine Learning Using Hybrid Approach." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 6601–3. http://dx.doi.org/10.22214/ijraset.2023.53204.

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Abstract: In this paper, we have proposed a movie recommendation system using hybrid recommendation system. Today, there are a lot of recommendation systems available which are practically implemented in various websites and mobile apps. Variety exists in types of recommendation systems, user interfaces but most importantly, the accuracy of the recommendation systems. Determining a user’s possible future preference of movie or TV shows to watch is a complex task which requires a lot of relevant user data such as watch history of user, genres liked by the user, favorite actor or director, etc. Hence, the aim of this proposed system is to refine the search engine and make it more enhanced and accurate in terms of prediction. The system recommends the movies graphically based on both, user preference and similarity of individual user with other users. It also shows top rated movies worldwide and updates the recommendation after every choice of movie or show by the user

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Ez-zahout, Abderrahmane, Hicham Gueddah, Abir Nasry, Rabie Madani, and Fouzia Omary. "A hybrid big data movies recommendation model based k-nearest neighbors and matrix factorization." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (April 1, 2022): 434. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp434-441.

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On the subject of broadcasting the information, finding someone’s favorite book or movie in a sea of data containing books and movies has become a crucial issue. In an era when there are so many genres and types of movies and books, the customer may find it difficult to choose which to discover in the first place. Thus, personalized recommendation systems play an important role because of the value that is attributed to movies and books nowadays, and considering that there are so many to choose from that the user may not be able to have a specific target. In this context, our proposed work, design and implement a prototype of movie recommendation system while taking into consideration the real requirement for the search of movies and books. The research of movie recommendation system by using the k-nearest neighbors approach and collaborative filtering algorithm are adopted to extract the criteria for a good use case on recommender systems. At last, the results are as what was expected as they showed that the system has a good recommendation effect.

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10

Huang, Yi-Ting, and Ping-Feng Pai. "Using the Least Squares Support Vector Regression to Forecast Movie Sales with Data from Twitter and Movie Databases." Symmetry 12, no. 4 (April 15, 2020): 625. http://dx.doi.org/10.3390/sym12040625.

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Due to the rapid prominence and popularity of social media, social broadcasting networks with voluntary information sharing have become one of the most powerful ways to spread word-of-mouth opinions, and thus, have influence on consumers’ preferences toward products. Therefore, sentiment analysis data from social media have become more important in forecasting product sales. For the movie industry, the opinions expressed on social media have increasing impacts on movie sales. In addition, some databases, such as the Box Office Mojo and Internet Movie Database (IMDb), contain structured data for predicting movie sales. Thus, three categories of data—data of movie databases, data of tweets, and hybrid data including movies databases and tweets—are employed symmetrically in this study. The aim of this study is to employ the least squares support vector regression (LSSVR) to forecast movie sales worldwide according to these three forms of data. In addition, three other forecasting techniques—namely, the back propagation neural network (BPNN), the generalized regression neural network (GRNN), and the multivariate linear regression (MLR) model—were used to forecast movie sales with the three types of data. The empirical results show that the LSSVR model with hybrid data can obtain more accurate results than the other forecasting models with all data types. Thus, forecasting movie sales using the LSSSVR model with data containing movie databases and tweets is a feasible and prospective method to forecast movie sales.

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Adikara, Putra Pandu, Yuita Arum Sari, Sigit Adinugroho, and Budi Darma Setiawan. "Movie recommender systems using hybrid model based on graphs with co-rated, genre, and closed caption features." Register: Jurnal Ilmiah Teknologi Sistem Informasi 7, no. 1 (January 30, 2021): 31. http://dx.doi.org/10.26594/register.v7i1.2081.

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A movie recommendation is a long-standing challenge. Figuring out the viewer’s interest in movies is still a problem since a huge number of movies are released in no time. In the meantime, people cannot enjoy all available new releases or unseen movies due to their limited time. They also still need to choose which movies to watch when they have spare time. This situation is not good for the movie business too. In order to satisfy people in choosing what movies to watch and to boost movie sales, a system that can recommend suitable movies is required, either unseen in the past or new releases. This paper focuses on the hybrid approach, a combination of content-based and collaborative filtering, using a graph-based model. This hybrid approach is proposed to overcome the drawbacks of combination in the content-based and collaborative filtering. The graph database, Neo4j is used to store the collaborative features, such as movies with its genres, and ratings. Since the movie’s closed caption is rarely considered to be used in a recommendation, the proposed method evaluates the impact of using this syntactic feature. From the early test, the combination of collaborative filtering and content-based using closed caption gives a slightly better result than without closed caption, especially in finding similar movies such as sequel or prequel.

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Soni, Karan, Rinky Goyal, Bhagyashree Vadera, and Siddhi More. "A Three Way Hybrid Movie Recommendation Syste." International Journal of Computer Applications 160, no. 9 (February 15, 2017): 29–32. http://dx.doi.org/10.5120/ijca2017913026.

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13

Sharma, Saurabh, and Harish Kumar Shakya. "Hybrid Movie Recommendation System Using Machine Learning." International Journal of Emerging Technology and Advanced Engineering 13, no. 1 (January 5, 2023): 100–123. http://dx.doi.org/10.46338/ijetae0123_12.

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This research suggests a hybrid movie recommendation system and optimization approach based on weighted classification and user collaborative filtering algorithm to address the issue that the single model of the standard recommendation system cannot adequately reflect user preferences. The top-N personalized movie recommendations are made by fusing the weighted classification model with the local recommendation model, which is trained based on user clustering, and the sparse linear model, which serves as the fundamental recommendation model. The scoring matrix is transformed into a low-dimensional, dense item category preference matrix based on item category preference, multiple cluster centers are obtained, the distance between each cluster center and the target user is calculated, and the target user is categorized into the closest cluster. Finally, a suggestion list is created using the collaborative filtering algorithm to forecast the scores for the target user's unrated items. The highdimensional rating matrix is transformed into a lowdimensional item category preference matrix, which further reduces the sparsity of the data. The items are then grouped based on item category preference. The recommendation algorithm suggested in this article addresses some of the limitations of a single algorithm model and enhances the suggestion effect, according to experiments using the MovieLens movie dataset.

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14

Panyatip, Tammanoon, Manasawee Kaenampornpan, and Phatthanaphong Chomphuwiset. "Conceptual framework of recommendation system with hybrid method." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 3 (September 1, 2023): 1696. http://dx.doi.org/10.11591/ijeecs.v31.i3.pp1696-1704.

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Recommendation system relies on information of user preference and user behavior in order to recommend the useful information. The existing recommendation systems still have problems for new users and new items. This research proposes a new hybrid method to develop the conceptual framework of recommendation system that deals with new user and new movie data. The data used consists of a data from MovieLens and the internet movie database (IMDB). This work introduces a hybrid recommendation system which based on a combination of content-based filtering (CBF) and collaborative filtering (CF). Pre-filtering data is performed by finding an optimal number of clusters by calculating the total within cluster sum of square. In order to reduce the complexity of data and increase the relevance of the user-item ratings, the fuzzy c-mean (FCM) is employed. Then the similarity is calculated by using item-based method, the K-nearest neighbors and weight sum of the rating are applied. Finally, to recommend the movies, the research found that for new user data the precision is at 85% and mean absolute eror (MAE) value 2.1011. For new item data, the result of research obtains the precision at 87% and MAE value 2.0031. In conclusion, the new hybrid method developed can recommend movie efficiently.

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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 (November 30, 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 filtering have limitations like it requires previous user activities for performing recommendations. To reduce this dependency hybrid is used which combines both collaborative and content based filtering techniques with the sentiment generated above. Keywords: machine learning, natural language processing, movie lens data, root mean square equation, matrix factorization, recommenders system, sentiment analysis

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Roy, Arighna, and Simone A. Ludwig. "Genre based hybrid filtering for movie recommendation engine." Journal of Intelligent Information Systems 56, no. 3 (February 18, 2021): 485–507. http://dx.doi.org/10.1007/s10844-021-00637-w.

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Gogri, Meet, Dharmil Chheda, and Vinit Solani. "Movie Recommendation Using Deep Learning with Hybrid Approach." Aksh - The Advance Journal 1, no. 2 (September 30, 2020): 1–4. http://dx.doi.org/10.51916/aksh.2020.v01i02.001.

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Bahl, Dushyant, Vaibhav Kain, Akshay Sharma, and Mugdha Sharma. "A novel hybrid approach towards movie recommender systems." Journal of Statistics and Management Systems 23, no. 6 (July 29, 2020): 1049–58. http://dx.doi.org/10.1080/09720510.2020.1799503.

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19

Balakrishnan, Vimala, and Hossein Arabi. "HyPeRM: A HYBRID PERSONALITY-AWARE RECOMMENDER FOR MOVIE." Malaysian Journal of Computer Science 31, no. 1 (January 25, 2018): 48–62. http://dx.doi.org/10.22452/mjcs.vol31no1.4.

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20

Priscilla, S., and C. Naveena. "Social Balance Theory Based Hybrid Movie Recommendation System." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 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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Dharaniya, R., and G. V. Uma. "Hybrid Genre Recognition Based on Movie Script Features." Journal of Computational and Theoretical Nanoscience 14, no. 10 (October 1, 2017): 5133–37. http://dx.doi.org/10.1166/jctn.2017.6933.

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Kumar, N. Suresh, and Pothina Praveena. "Evolution of hybrid distance based kNN classification." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (June 1, 2021): 510. http://dx.doi.org/10.11591/ijai.v10.i2.pp510-518.

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The evolution of classification of opinion mining and user review analysis span from decades reaching into ubiquitous computing in efforts such as movie review analysis. The performance of linear and non-linear models are discussed to classify the positive and negative reviews of movie data sets. The effectiveness of linear and non-linear algorithms are tested and compared in-terms of average accuracy. The performance of various algorithms is tested by implementing them on internet movie data base (IMDB). The hybrid kNN model optimizes the performance classification interns of accuracy. The accuracy of polarity prediction rate is improved with random-distance-weighted-kNN-ABC when compared with kNN algorithm applied alone.

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23

Sun, Yanni. "Genre mixing on WeChat: evidence from a movie review subscription account." Chinese Semiotic Studies 17, no. 3 (August 1, 2021): 401–19. http://dx.doi.org/10.1515/css-2021-2005.

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Abstract Genre mixing and hybrid genre have been vital concepts in genre studies. With the increasing popularity of WeChat, a social media platform in China, a new type of hybrid genre comprised of media content and advertisements is emerging on WeChat subscription accounts. The present study collects 28 hybrid texts from a movie review subscription account in order to closely examine their communicative purposes and generic structure. It is found that instead of being fused into a monocentric entity, these hybrid texts are divided into movie review and advertisement parts, both functionally and structurally dichotomous. This expands and complements the existing understanding of concepts like hybrid genre and genre mixing. It also brings into focus the anti-monocentric nature of these concepts and questions the logocentric framework advocated in genre mixing studies.

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Sahu, Sandipan, Raghvendra Kumar, Pathan MohdShafi, Jana Shafi, SeongKi Kim, and Muhammad Fazal Ijaz. "A Hybrid Recommendation System of Upcoming Movies Using Sentiment Analysis of YouTube Trailer Reviews." Mathematics 10, no. 9 (May 6, 2022): 1568. http://dx.doi.org/10.3390/math10091568.

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Movies are one of the integral components of our everyday entertainment. In today’s world, people prefer to watch movies on their personal devices. Many movies are available on all popular Over the Top (OTT) platforms. Multiple new movies are released onto these platforms every day. The recommendation system is beneficial for guiding the user to a choice from among the overloaded contents. Most of the research on these recommendation systems has been conducted based on existing movies. We need a recommendation system for forthcoming movies in order to help viewers make a personalized decision regarding which upcoming new movies to watch. In this article, we have proposed a framework combining sentiment analysis and a hybrid recommendation system for recommending movies that are not yet released, but the trailer has been released. In the first module, we extracted comments about the movie trailer from the official YouTube channel for Netflix, computed the overall sentiment, and predicted the rating of the upcoming movies. Next, in the second module, our proposed hybrid recommendation system produced a list of preferred upcoming movies for individual users. In the third module, we finally were able to offer recommendations regarding potentially popular forthcoming movies to the user, according to their personal preferences. This method fuses the predicted rating and preferred list of upcoming movies from modules one and two. This study used publicly available data from The Movie Database (TMDb). We also created a dataset of new movies by randomly selecting a list of one hundred movies released between 2020 and 2021 on Netflix. Our experimental results established that the predicted rating of unreleased movies had the lowest error. Additionally, we showed that the proposed hybrid recommendation system recommends movies according to the user’s preferences and potentially promising forthcoming movies.

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Paranjape, Vishal, Neelu Nihalani, and Nishchol Mishra. "Design of a Hybrid Movie Recommender System Using Machine Learning." International Journal of Emerging Technology and Advanced Engineering 13, no. 3 (March 6, 2023): 159–65. http://dx.doi.org/10.46338/ijetae0323_17.

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The primary aim of recommender system is to predict items which are of most interest to the users and today recommender systems play a vital role in boosting the sales in any e-commerce based platform. The present paper proposes an approach for recommending movies to the users on the basis on their choices. A novel technique for evaluation of collaborative filtering using SVD and hit ratio as a metric is taken in our proposed approach. We attempted to build a model-based Collaborative filtering technique. The proposed paper makes use of matrix factorization techniques like SVD & SVD++ for filtering movie recommendation system based on latent features. It makes better recommendations based on choice of user because it captures the underlying features driving the raw data. In this paper we are proposing a hybrid recommender system fusion of Content Based and SVD to get a new hybrid recommender system. Our proposed model gives the value of RMSE 0.87 for SVD model and RMSE 0.938 for SVD++ model. Keywords-- Collaborative filtering, movie recommendation, SVD, content based filtering

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Malik, Sonika. "Movie Recommender System using Machine Learning." EAI Endorsed Transactions on Creative Technologies 9, no. 3 (October 11, 2022): e3. http://dx.doi.org/10.4108/eetct.v9i3.2712.

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In this research, we propose a movie recommender system that can recommend movies to both new and existing customers. It searches movie databases for all of the relevant data, such as popularity and beauty that is required for a recommendation. We apply both content-based and collaborative filtering and evaluate their advantages and disadvantages. To build a system that delivers more exact movie recommendations, we employ hybrid filtering, which is a combination of the outcomes of these two processes. The recommendation engines are also used for business purposes and to make strategies for organizations. Due to the growing demands of customers and user’s recommendation systems plays a huge role. These recommender systems also help us to utilize our time in the busy world by giving us more relevant searches. These systems are generally used with the movie’s websites or with many commercial applications and are of great use. This type of recommendation system can be also used for precise results. It will make movies suggestions more relevant as per the need of the users.

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Behera, Rabi Narayan, and Sujata Dash. "A Particle Swarm Optimization based Hybrid Recommendation System." International Journal of Knowledge Discovery in Bioinformatics 6, no. 2 (July 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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Liu, Duen-Ren, Yun-Cheng Chou, and Ciao-Ting Jian. "Integrating collaborative topic modeling and diversity for movie recommendations during news browsing." Kybernetes 49, no. 11 (November 27, 2019): 2633–49. http://dx.doi.org/10.1108/k-08-2019-0578.

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Purpose Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users’ satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles. Design/methodology/approach Association rule mining is applied to users’ news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website. Findings The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users’ online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance. Originality/value Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user’s online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.

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Amolochitis, Emmanouil, Ioannis T. Christou, and Zheng-Hua Tan. "Implementing a Commercial-Strength Parallel Hybrid Movie Recommendation Engine." IEEE Intelligent Systems 29, no. 2 (March 2014): 92–96. http://dx.doi.org/10.1109/mis.2014.23.

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30

CHRISTAKOU, CHRISTINA, SPYROS VRETTOS, and ANDREAS STAFYLOPATIS. "A HYBRID MOVIE RECOMMENDER SYSTEM BASED ON NEURAL NETWORKS." International Journal on Artificial Intelligence Tools 16, no. 05 (October 2007): 771–92. http://dx.doi.org/10.1142/s0218213007003540.

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Recommender systems offer a solution to the problem of successful information search in the knowledge reservoirs of the Internet by providing individualized recommendations. Content-based and Collaborative Filtering are usually applied to predict recommendations. A combination of the results of the above techniques is used in this work to construct a system that provides precise recommendations concerning movies. The content filtering part of the system is based on trained neural networks representing individual user preferences. Filtering results are combined using Boolean and fuzzy aggregation operators. The proposed hybrid system was tested on the MovieLens data yielding high accuracy predictions.

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Rokade, Prakash Pandharinath, PVRD Prasad Rao, and Aruna Kumari Devarakonda. "Forecasting movie rating using k-nearest neighbor based collaborative filtering." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (December 1, 2022): 6506. http://dx.doi.org/10.11591/ijece.v12i6.pp6506-6512.

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Expressing reviews in the form of sentiments or ratings for item used or movie seen is the part of human habit. These reviews are easily available on different social websites. Based on interest pattern of a user, it is important to recommend him the items. Recommendation system is playing a vital role in everyone’s life as demand of recommendation for user’s interest increasing day by day. Movie recommendation system based on available ratings for a movie has become interesting part for new users. Till today, a lot many recommendation systems are designed using several machine learning algorithms. Still, sparsity problems, cold start problem, scalability, grey sheep problem are the hurdles for the recommendation systems that must be resolved using hybrid algorithms. We proposed in this paper, a movie rating system using a k-nearest neighbor (KNN-based) collaborative filtering (CF) approach. We compared user’s ratings for different movies to get top K users. Then we have used this top K set to find missing ratings by user for a movie using CF. Our proposed system when evaluated for various criteria shows promising results for movie recommendations compared with existing systems.

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Singh, Kamred Udham. "A Multi-Criteria Movie Recommendation System based on User Preferences and Movie Features." Mathematical Statistician and Engineering Applications 70, no. 1 (January 31, 2021): 348–60. http://dx.doi.org/10.17762/msea.v70i1.2317.

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In this research, we develop a multi-criteria movie recommendation system that provides personalised recommendations by taking into consideration both user preferences and movie aspects. To get over each method's specific drawbacks, the suggested system takes a hybrid approach that combines collaborative filtering with content-based techniques. The system uses collaborative filtering to capture user preferences based on historical ratings, while content-based methods analyze movie features such as genre, director, actors, and keywords to enhance the recommendation process. Additionally, we integrate various external data sources like movie reviews, social media sentiment, and box office performance to enrich the movie feature set. The system employs a weighted aggregation method to combine these criteria and generate a comprehensive recommendation score. The effectiveness of the proposed system is evaluated utilizing standard metrics including recall, precision, and F1-score on a publicly available dataset. The results demonstrate that our multi-criteria recommendation system effectively captures user preferences and provides more accurate and diverse recommendations compared to traditional single-criterion approaches.

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Dubey, Gaurav, Richa Khera, Ashish Grover, Amandeep Kaur, Abhishek Goyal, Rajkumar Rajkumar, Harsh Khatter, and Somya Srivastava. "A Hybrid Convolutional Network and Long Short-Term Memory (HBCNLS) model for Sentiment Analysis on Movie Reviews." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4 (May 4, 2023): 341–48. http://dx.doi.org/10.17762/ijritcc.v11i4.6458.

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This paper proposes a hybrid model (HBCNLS) for sentiment analysis that combines the strengths of multiple machine learning approaches. The model consists of a convolutional neural network (CNN) for feature extraction, a long short-term memory (LSTM) network for capturing sequential dependencies, and a fully connected layer for classification on movie review dataset. We evaluate the performance of the HBCNLS on the IMDb movie review dataset and compare it to other state-of-the-art models, including BERT. Our results show that the hybrid model outperforms the other models in terms of accuracy, precision, and recall, demonstrating the effectiveness of the hybrid approach. The research work also compares the performance of BERT, a pre-trained transformer model, with long short-term memory (LSTM) networks and convolutional neural networks (CNNs) for the task of sentiment analysis on a movie review dataset..

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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 (July 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 classify the similarity of the user ratings. The proposed collaborative recommender system performed well regarding accuracy and precision. Various metrics are used in a movie lens dataset like mean absolute error (MAE), precision, and recall. The experimental result delivered by the system provides more efficient performance compared to the existing system in term of mean absolute error (MAE).

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Awan, Mazhar Javed, Rafia Asad Khan, Haitham Nobanee, Awais Yasin, Syed Muhammad Anwar, Usman Naseem, and Vishwa Pratap Singh. "A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach." Electronics 10, no. 10 (May 20, 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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Sharma, Mugdha, Laxmi Ahuja, and Vinay Kumar. "A Hybrid Filtering Approach for an Improved Context-aware Recommender System." Recent Patents on Engineering 13, no. 1 (February 8, 2019): 39–47. http://dx.doi.org/10.2174/1872212112666180813124358.

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Background: The domain of context-aware recommender approaches has made a substantial advancement over the last decade, but many applications still do not include contextual information while providing recommendations. Contextual information is crucial for various application areas and should not be ignored. Objective: There are generally three algorithms which can be used to include context and those are - pre-filter approach, post-filter approach and contextual modeling. Each of the algorithms has their own drawbacks if any single approach is chosen. The goal of this work is to identify and propose a new hybrid approach which can include contextual information to improve the current movie recommender systems. Method: Post evaluation of various patents related to recommender systems, the proposed approach modifies the post filter approach to rectify its shortcomings and combines it with the pre-filter approach based on the importance of contextual attribute provided by the user. Results: The performance of the proposed system is measured in terms of precision of the system and ranking of the recommended movies to the user. The results of experimental setup also demonstrate that the proposed system improves the precision and ranking of the recommendations provided to the user. Conclusion: With the help of this hybrid approach, the proposed system eliminates the problem of sparsity which is present in the pre-filter algorithm, and has performance improvement over the traditional post-filter approach. The proposed system will be vital for movie ticketing brands for the promotional purposes and various online content providers to recommend the accurate movies to their users.

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Potter, Michael, Hamlin Liu, Yash Lala, Christian Loanzon, and Yizhou Sun. "GRU4RecBE: A Hybrid Session-Based Movie Recommendation System (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 13029–30. http://dx.doi.org/10.1609/aaai.v36i11.21651.

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We present a novel movie recommendation system, GRU4RecBE, which extends the GRU4Rec architecture with rich item features extracted by the pre-trained BERT model. GRU4RecBE outperforms state-of-the-art session-based models over the benchmark MovieLens 1m and MovieLens 20m datasets.

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Ali, Yasher, Osman Khalid, Imran Ali Khan, Syed Sajid Hussain, Faisal Rehman, Sajid Siraj, and Raheel Nawaz. "A hybrid group-based movie recommendation framework with overlapping memberships." PLOS ONE 17, no. 3 (March 31, 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 possibility of users having simultaneous membership of multiple groups. Unlike the existing group recommender systems that use traditional methods like K-Means, Pearson correlation, and cosine similarity to form groups, we use Fuzzy C-means clustering which assigns a degree of membership to each user for each group, and then Pearson similarity is used to form groups. We demonstrate the usefulness of our proposed framework using a movies data set. The experiments were conducted on MovieLens 1M dataset where we used Neural Collaborative Filtering to recommend Top-k movies to each group. The results demonstrate that our proposed framework outperforms the traditional approaches when compared in terms of group satisfaction parameters, as well as the conventional metrics of precision, recall, and F-measure.

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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 (August 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 propose a novel hybrid method that combines user–user CF with the attributes of DF to indicate the nearest users, and compare four classifiers against each other. This method has been developed through an investigation of ways to reduce the errors in rating predictions based on users’ past interactions, which leads to improved prediction accuracy in all four classification algorithms. We applied a feature combination method that improves the prediction accuracy and to test our approach, we ran an offline evaluation using the 1M MovieLens dataset, well-known evaluation metrics and comparisons between methods with the results validating our proposed method.

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Vellaichamy, Vimala, and Vivekanandan Kalimuthu. "Hybrid Collaborative Movie Recommender System Using Clustering and Bat Optimization." International Journal of Intelligent Engineering and Systems 10, no. 5 (October 31, 2017): 38–47. http://dx.doi.org/10.22266/ijies2017.1031.05.

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Wei, Shouxian, Xiaolin Zheng, Deren Chen, and Chaochao Chen. "A hybrid approach for movie recommendation via tags and ratings." Electronic Commerce Research and Applications 18 (July 2016): 83–94. http://dx.doi.org/10.1016/j.elerap.2016.01.003.

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Abdelkhalek, Raoua, Imen Boukhris, and Zied Elouedi. "Towards more trustworthy predictions: A hybrid evidential movie recommender system." JUCS - Journal of Universal Computer Science 28, no. 10 (October 28, 2022): 1003–29. http://dx.doi.org/10.3897/jucs.79777.

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Recommender Systems (RSs) are considered as popular tools that have revolutionized the e-commerce and digital marketing. Their main goal is predicting the users’ future preferences and providing accessible and personalized recommendations. However, uncertainty can spread at any level throughout the recommendation process, which may affect the results. In fact, the ratings given by the users are often unreliable. The final provided predictions itself may also be pervaded with uncertainty and doubt. Obviously, the reliability of the predictions cannot be fully certain and trustworthy. For the system to be effective, recommendations must inspire trust in the system and provide reliable and credible recommendations. The user may speculate about the uncertainty pervaded behind the given recommendation. He could tend to a reliable recommendation offering him a global overview about his preferences rather than an inappropriate one that contradicts his activities and objectives. While such imperfection cannot be ignored, traditional RSs are rarely able to deal with the uncertainty spreading around the prediction process, which may affect the credibility, the transparency and the trustworthiness of the current RS. Thus, in this paper, we opt for the uncertain framework of the belief function theory (BFT), which allows us to represent, quantify and manage imperfect evidence. By using the BFT, the users’ preferences and the interactions between the neighbors can be represented under uncertainty. Evidence from different information sources can then be combined leading to more reliable results. The proposed approach is a hybrid evidential movie RS that uses different data sources and delivers a personalized user-interface allowing a global overview of the possible future preferences. This representation would increase the users’ confidence towards the system as well as their satisfaction. Experiments are performed on MovieLens and their additional features provided by the Internet Movie Database (IMDb) and Rotten Tomatoes. The new approach achieves promising results compared to traditional approaches in terms of MAE, NMAE and RMSE. It also reaches interesting Precision, Recall and F-measure values of respectively, 0.782, 0.792 and 0.787.

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Liu, Ziyun, and Feiyu Ren. "Algorithm Improvement of Movie Recommendation System based on Hybrid Recommendation Algorithm." Frontiers in Computing and Intelligent Systems 3, no. 3 (May 17, 2023): 113–17. http://dx.doi.org/10.54097/fcis.v3i3.8581.

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In recent years, the Internet has developed rapidly, and in the face of thousands of data and information, it has become very critical for users to find the information that is of high value to them in the mass of information, and the recommendation system is one of the most effective ways to solve this information overload phenomenon. In this paper, the current movie recommendation algorithm is improved by using an item-based collaborative filtering algorithm for the similarity measure of items in the item-based recommendation process; In the recommendation process, two more applicable recommendation methods are considered: collaborative filtering content-based recommendation and matrix decomposition-based recommendation. It saves users time in searching, viewing and filtering, while discovering information about their potential movie preferences.

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Manikandan, J. "Movie Recommendation System Mistreatment Current Trends and Sentiment Analysis from Micro Blogging Knowledge." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 393–98. http://dx.doi.org/10.22214/ijraset.2021.38651.

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Abstract: Recommendation systems (RSs) have garnered immense interest for applications in e-commerce and digital media. Traditional approaches in RSs include such as collaborative filtering (CF) and content-based filtering (CBF) through these approaches that have certain limitations, such as the necessity of prior user history and habits for performing the task of recommendation. To minimize the effect of such limitation, this article proposes a hybrid RS for the movies that leverage the best of concepts used from CF and CBF along with sentiment analysis of tweets from microblogging sites. The purpose to use movie tweets is to understand the current trends, public sentiment, and user response of the movie. Experiments conducted on the public database have yielded promising results. Keywords: Collaborative filtering, Content based filtering, Recommendation System, Sentiment Analysis, Twitter

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Sharma, Bharti, Adeel Hashmi, Charu Gupta, Osamah Ibrahim Khalaf, Ghaida Muttashar Abdulsahib, and Malakeh Muhyiddeen Itani. "Hybrid Sparrow Clustered (HSC) Algorithm for Top-N Recommendation System." Symmetry 14, no. 4 (April 11, 2022): 793. http://dx.doi.org/10.3390/sym14040793.

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Recommendation systems suggest relevant items to a user based on the similarity between users or between items. In a collaborative filtering approach for generating recommendations, there is a symmetry between the users. That is, if user A has similar interests with user B, then an item liked by B can be recommended to A and vice versa. To provide optimal and fast recommendations, a recommender system may generate and keep clusters of existing users/items. In this research work, a hybrid sparrow clustered (HSC) recommender system is developed, and is applied to the MovieLens dataset to demonstrate its effectiveness and efficiency. The proposed method (HSC) is also compared to other methods, and the results are compared. Precision, mean absolute error, recall, and accuracy metrics were used to figure out how well the movie recommender system worked for the HSC collaborative movie recommender system. The results of the experiment on the MovieLens dataset show that the proposed method is quite promising when it comes to scalability, performance, and personalized movie recommendations.

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Hwang, Tae-Gyu, and Sung Kwon Kim. "Movie Recommendation through Multiple Bias Analysis." Applied Sciences 11, no. 6 (March 22, 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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Mir, Jibran, and Azhar Mahmood. "Movie Aspects Identification Model for Aspect Based Sentiment Analysis." Information Technology And Control 49, no. 4 (December 19, 2020): 564–82. http://dx.doi.org/10.5755/j01.itc.49.4.25350.

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Aspect Based Sentiment Analysis techniques have been applied in several application domains. From the last two decades, these techniques have been developed mostly for product and service application domains. However, very few aspect-based sentiment techniques have been proposed for the movie application domain. Moreover, these techniques only mine specific aspects (Script, Director, and Actor) of a movie application domain, nevertheless, the movie application domain is more complex than the product and service application domain. Since, it contains NER (Named Entity Recognition) problem and it cannot be ignored, since there is an opinion often associated with it. Consequently, in this paper MAIM (Movie Aspect Identification Model) is proposed that can extract not only movie specific aspects, also identifies NEs (Named Entities) such as Person Name and Movie Title. The three main contributions are 1) the identification of infrequent aspects, 2) the identification of NE (named entity) in movie application domain, 3) identifying N-gram opinion words as an entity. MAIM incorporates the BiLSTM-CRF hybrid technique and is implemented on the movie application domain having precision 89.9%, recall 88.9% and f1-measure 89.4%. The experimental results show that MAIM performs better than baseline models CRF and LSTM-CRF.

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Zamanzadeh Darban, Zahra, and Mohammad Hadi Valipour. "GHRS: Graph-based hybrid recommendation system with application to movie recommendation." Expert Systems with Applications 200 (August 2022): 116850. http://dx.doi.org/10.1016/j.eswa.2022.116850.

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Kumar, Keerthi, B. S. Harish, and H. K. Darshan. "Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method." International Journal of Interactive Multimedia and Artificial Intelligence 5, no. 5 (2019): 109. http://dx.doi.org/10.9781/ijimai.2018.12.005.

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Jain, Kirti, Pinaki Ghosh, and Shital Gupta. "A Hybrid Model for Sentiment Analysis Based on Movie Review Datasets." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (June 7, 2023): 424–31. http://dx.doi.org/10.17762/ijritcc.v11i5s.7082.

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The classification of sentiments, often known as sentiment analysis, is now widely recognized as an open field of research. Over the past few years, a huge amount of study work has been carried out in these disciplines by utilizing a wide variety of research approaches. Due to the possibility that the performance of sentiment analysis may be impacted by the high-dimensional feature set, text mining demands careful consideration during in the construction and selection of features.The process of recognising and extracting subjective information from written data is referred to as sentiment analysis. Sentiment analysis enables companies to understand the social sentiment around their brand, product, or service by monitoring the conversations that take place in internet chat rooms. In order to categorise people's attitudes or sentiments, this study provides a hybrid model (Support Vector Machine, Convolutional Neural Network, and Long Short-Term Memory). The findings of using the network model to sentiment analysis on the movie review or amazon review datasets reveal that it is possible to gain a good classification impact by using the model. The preprocessing is used for text mining, the removal of punctuation, and the generation of vocabulary, also uses GLOVE for vectorization and TF-IDF algorithms for better feature extraction. The results that were proposed were compared with various base models such as KNN, and MNB, amongst others, which demonstrates that the hybrid model performs better than other models.

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