Artykuły w czasopismach na temat „HYBRID MOVIE”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Sprawdź 50 najlepszych artykułów w czasopismach naukowych na temat „HYBRID MOVIE”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Przeglądaj artykuły w czasopismach z różnych dziedzin i twórz odpowiednie bibliografie.
Nosshi, Anthony, Aziza Asem i Mohamed Badr Senousy. "Hybrid Recommender System via Personalized Users’ Context". Cybernetics and Information Technologies 19, nr 1 (1.03.2019): 101–15. http://dx.doi.org/10.2478/cait-2019-0006.
Pełny tekst źródłaWang, Yibo, Mingming Wang i 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.
Pełny tekst źródłaNosshi, Anthony, Aziza Saad Asem i Mohammed Badr Senousy. "Hybrid Recommender System Using Emotional Fingerprints Model". International Journal of Information Retrieval Research 9, nr 3 (lipiec 2019): 48–70. http://dx.doi.org/10.4018/ijirr.2019070104.
Pełny tekst źródłaTripathi, Jyoti, Sunita Tiwari, Anu Saini i 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, nr 3 (1.03.2023): 1750. http://dx.doi.org/10.11591/ijeecs.v29.i3.pp1750-1757.
Pełny tekst źródłaBohra, Sneha, Amit Gaikwad i Ghanapriya Singh. "Hybrid Machine Learning Based Recommendation Algorithm for Multiple Movie Dataset". Indian Journal Of Science And Technology 16, nr 37 (9.10.2023): 3121–28. http://dx.doi.org/10.17485/ijst/v16i37.2065.
Pełny tekst źródłaMohile, Sara, Hemant Ramteke, Pragati Shelgaonkar, Hritika Phule i M. M. Phadtare. "A Movie Recommender System Using Hybrid Approach: A Review". International Journal for Research in Applied Science and Engineering Technology 10, nr 3 (31.03.2022): 1834–37. http://dx.doi.org/10.22214/ijraset.2022.41014.
Pełny tekst źródłaLekakos, George, i Petros Caravelas. "A hybrid approach for movie recommendation". Multimedia Tools and Applications 36, nr 1-2 (21.12.2006): 55–70. http://dx.doi.org/10.1007/s11042-006-0082-7.
Pełny tekst źródłaJadhav, Prof Rupali. "Implementing a Movie Recommendation System in Machine Learning Using Hybrid Approach". International Journal for Research in Applied Science and Engineering Technology 11, nr 5 (31.05.2023): 6601–3. http://dx.doi.org/10.22214/ijraset.2023.53204.
Pełny tekst źródłaEz-zahout, Abderrahmane, Hicham Gueddah, Abir Nasry, Rabie Madani i 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, nr 1 (1.04.2022): 434. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp434-441.
Pełny tekst źródłaHuang, Yi-Ting, i Ping-Feng Pai. "Using the Least Squares Support Vector Regression to Forecast Movie Sales with Data from Twitter and Movie Databases". Symmetry 12, nr 4 (15.04.2020): 625. http://dx.doi.org/10.3390/sym12040625.
Pełny tekst źródłaAdikara, Putra Pandu, Yuita Arum Sari, Sigit Adinugroho i 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, nr 1 (30.01.2021): 31. http://dx.doi.org/10.26594/register.v7i1.2081.
Pełny tekst źródłaSoni, Karan, Rinky Goyal, Bhagyashree Vadera i Siddhi More. "A Three Way Hybrid Movie Recommendation Syste". International Journal of Computer Applications 160, nr 9 (15.02.2017): 29–32. http://dx.doi.org/10.5120/ijca2017913026.
Pełny tekst źródłaSharma, Saurabh, i Harish Kumar Shakya. "Hybrid Movie Recommendation System Using Machine Learning". International Journal of Emerging Technology and Advanced Engineering 13, nr 1 (5.01.2023): 100–123. http://dx.doi.org/10.46338/ijetae0123_12.
Pełny tekst źródłaPanyatip, Tammanoon, Manasawee Kaenampornpan i Phatthanaphong Chomphuwiset. "Conceptual framework of recommendation system with hybrid method". Indonesian Journal of Electrical Engineering and Computer Science 31, nr 3 (1.09.2023): 1696. http://dx.doi.org/10.11591/ijeecs.v31.i3.pp1696-1704.
Pełny tekst źródłaGomathy, 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, nr 11 (30.11.2021): 635–38. http://dx.doi.org/10.22214/ijraset.2021.38844.
Pełny tekst źródłaRoy, Arighna, i Simone A. Ludwig. "Genre based hybrid filtering for movie recommendation engine". Journal of Intelligent Information Systems 56, nr 3 (18.02.2021): 485–507. http://dx.doi.org/10.1007/s10844-021-00637-w.
Pełny tekst źródłaGogri, Meet, Dharmil Chheda i Vinit Solani. "Movie Recommendation Using Deep Learning with Hybrid Approach". Aksh - The Advance Journal 1, nr 2 (30.09.2020): 1–4. http://dx.doi.org/10.51916/aksh.2020.v01i02.001.
Pełny tekst źródłaBahl, Dushyant, Vaibhav Kain, Akshay Sharma i Mugdha Sharma. "A novel hybrid approach towards movie recommender systems". Journal of Statistics and Management Systems 23, nr 6 (29.07.2020): 1049–58. http://dx.doi.org/10.1080/09720510.2020.1799503.
Pełny tekst źródłaBalakrishnan, Vimala, i Hossein Arabi. "HyPeRM: A HYBRID PERSONALITY-AWARE RECOMMENDER FOR MOVIE". Malaysian Journal of Computer Science 31, nr 1 (25.01.2018): 48–62. http://dx.doi.org/10.22452/mjcs.vol31no1.4.
Pełny tekst źródłaPriscilla, S., i C. Naveena. "Social Balance Theory Based Hybrid Movie Recommendation System". Journal of Computational and Theoretical Nanoscience 17, nr 9 (1.07.2020): 4022–25. http://dx.doi.org/10.1166/jctn.2020.9012.
Pełny tekst źródłaDharaniya, R., i G. V. Uma. "Hybrid Genre Recognition Based on Movie Script Features". Journal of Computational and Theoretical Nanoscience 14, nr 10 (1.10.2017): 5133–37. http://dx.doi.org/10.1166/jctn.2017.6933.
Pełny tekst źródłaKumar, N. Suresh, i Pothina Praveena. "Evolution of hybrid distance based kNN classification". IAES International Journal of Artificial Intelligence (IJ-AI) 10, nr 2 (1.06.2021): 510. http://dx.doi.org/10.11591/ijai.v10.i2.pp510-518.
Pełny tekst źródłaSun, Yanni. "Genre mixing on WeChat: evidence from a movie review subscription account". Chinese Semiotic Studies 17, nr 3 (1.08.2021): 401–19. http://dx.doi.org/10.1515/css-2021-2005.
Pełny tekst źródłaSahu, Sandipan, Raghvendra Kumar, Pathan MohdShafi, Jana Shafi, SeongKi Kim i Muhammad Fazal Ijaz. "A Hybrid Recommendation System of Upcoming Movies Using Sentiment Analysis of YouTube Trailer Reviews". Mathematics 10, nr 9 (6.05.2022): 1568. http://dx.doi.org/10.3390/math10091568.
Pełny tekst źródłaParanjape, Vishal, Neelu Nihalani i Nishchol Mishra. "Design of a Hybrid Movie Recommender System Using Machine Learning". International Journal of Emerging Technology and Advanced Engineering 13, nr 3 (6.03.2023): 159–65. http://dx.doi.org/10.46338/ijetae0323_17.
Pełny tekst źródłaMalik, Sonika. "Movie Recommender System using Machine Learning". EAI Endorsed Transactions on Creative Technologies 9, nr 3 (11.10.2022): e3. http://dx.doi.org/10.4108/eetct.v9i3.2712.
Pełny tekst źródłaBehera, Rabi Narayan, i Sujata Dash. "A Particle Swarm Optimization based Hybrid Recommendation System". International Journal of Knowledge Discovery in Bioinformatics 6, nr 2 (lipiec 2016): 1–10. http://dx.doi.org/10.4018/ijkdb.2016070101.
Pełny tekst źródłaLiu, Duen-Ren, Yun-Cheng Chou i Ciao-Ting Jian. "Integrating collaborative topic modeling and diversity for movie recommendations during news browsing". Kybernetes 49, nr 11 (27.11.2019): 2633–49. http://dx.doi.org/10.1108/k-08-2019-0578.
Pełny tekst źródłaAmolochitis, Emmanouil, Ioannis T. Christou i Zheng-Hua Tan. "Implementing a Commercial-Strength Parallel Hybrid Movie Recommendation Engine". IEEE Intelligent Systems 29, nr 2 (marzec 2014): 92–96. http://dx.doi.org/10.1109/mis.2014.23.
Pełny tekst źródłaCHRISTAKOU, CHRISTINA, SPYROS VRETTOS i ANDREAS STAFYLOPATIS. "A HYBRID MOVIE RECOMMENDER SYSTEM BASED ON NEURAL NETWORKS". International Journal on Artificial Intelligence Tools 16, nr 05 (październik 2007): 771–92. http://dx.doi.org/10.1142/s0218213007003540.
Pełny tekst źródłaRokade, Prakash Pandharinath, PVRD Prasad Rao i Aruna Kumari Devarakonda. "Forecasting movie rating using k-nearest neighbor based collaborative filtering". International Journal of Electrical and Computer Engineering (IJECE) 12, nr 6 (1.12.2022): 6506. http://dx.doi.org/10.11591/ijece.v12i6.pp6506-6512.
Pełny tekst źródłaSingh, Kamred Udham. "A Multi-Criteria Movie Recommendation System based on User Preferences and Movie Features". Mathematical Statistician and Engineering Applications 70, nr 1 (31.01.2021): 348–60. http://dx.doi.org/10.17762/msea.v70i1.2317.
Pełny tekst źródłaDubey, Gaurav, Richa Khera, Ashish Grover, Amandeep Kaur, Abhishek Goyal, Rajkumar Rajkumar, Harsh Khatter i 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, nr 4 (4.05.2023): 341–48. http://dx.doi.org/10.17762/ijritcc.v11i4.6458.
Pełny tekst źródłaKumar, M. Sandeep, i Prabhu J. "Hybrid Model for Movie Recommendation System Using Fireflies and Fuzzy C-Means". International Journal of Web Portals 11, nr 2 (lipiec 2019): 1–13. http://dx.doi.org/10.4018/ijwp.2019070101.
Pełny tekst źródłaAwan, Mazhar Javed, Rafia Asad Khan, Haitham Nobanee, Awais Yasin, Syed Muhammad Anwar, Usman Naseem i Vishwa Pratap Singh. "A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach". Electronics 10, nr 10 (20.05.2021): 1215. http://dx.doi.org/10.3390/electronics10101215.
Pełny tekst źródłaSharma, Mugdha, Laxmi Ahuja i Vinay Kumar. "A Hybrid Filtering Approach for an Improved Context-aware Recommender System". Recent Patents on Engineering 13, nr 1 (8.02.2019): 39–47. http://dx.doi.org/10.2174/1872212112666180813124358.
Pełny tekst źródłaPotter, Michael, Hamlin Liu, Yash Lala, Christian Loanzon i Yizhou Sun. "GRU4RecBE: A Hybrid Session-Based Movie Recommendation System (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 11 (28.06.2022): 13029–30. http://dx.doi.org/10.1609/aaai.v36i11.21651.
Pełny tekst źródłaAli, Yasher, Osman Khalid, Imran Ali Khan, Syed Sajid Hussain, Faisal Rehman, Sajid Siraj i Raheel Nawaz. "A hybrid group-based movie recommendation framework with overlapping memberships". PLOS ONE 17, nr 3 (31.03.2022): e0266103. http://dx.doi.org/10.1371/journal.pone.0266103.
Pełny tekst źródłaAlshammari, Gharbi, Stelios Kapetanakis, Abdullah Alshammari, Nikolaos Polatidis i Miltos Petridis. "Improved Movie Recommendations Based on a Hybrid Feature Combination Method". Vietnam Journal of Computer Science 06, nr 03 (sierpień 2019): 363–76. http://dx.doi.org/10.1142/s2196888819500192.
Pełny tekst źródłaVellaichamy, Vimala, i Vivekanandan Kalimuthu. "Hybrid Collaborative Movie Recommender System Using Clustering and Bat Optimization". International Journal of Intelligent Engineering and Systems 10, nr 5 (31.10.2017): 38–47. http://dx.doi.org/10.22266/ijies2017.1031.05.
Pełny tekst źródłaWei, Shouxian, Xiaolin Zheng, Deren Chen i Chaochao Chen. "A hybrid approach for movie recommendation via tags and ratings". Electronic Commerce Research and Applications 18 (lipiec 2016): 83–94. http://dx.doi.org/10.1016/j.elerap.2016.01.003.
Pełny tekst źródłaAbdelkhalek, Raoua, Imen Boukhris i Zied Elouedi. "Towards more trustworthy predictions: A hybrid evidential movie recommender system". JUCS - Journal of Universal Computer Science 28, nr 10 (28.10.2022): 1003–29. http://dx.doi.org/10.3897/jucs.79777.
Pełny tekst źródłaLiu, Ziyun, i Feiyu Ren. "Algorithm Improvement of Movie Recommendation System based on Hybrid Recommendation Algorithm". Frontiers in Computing and Intelligent Systems 3, nr 3 (17.05.2023): 113–17. http://dx.doi.org/10.54097/fcis.v3i3.8581.
Pełny tekst źródłaManikandan, 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, nr 11 (30.11.2021): 393–98. http://dx.doi.org/10.22214/ijraset.2021.38651.
Pełny tekst źródłaSharma, Bharti, Adeel Hashmi, Charu Gupta, Osamah Ibrahim Khalaf, Ghaida Muttashar Abdulsahib i Malakeh Muhyiddeen Itani. "Hybrid Sparrow Clustered (HSC) Algorithm for Top-N Recommendation System". Symmetry 14, nr 4 (11.04.2022): 793. http://dx.doi.org/10.3390/sym14040793.
Pełny tekst źródłaHwang, Tae-Gyu, i Sung Kwon Kim. "Movie Recommendation through Multiple Bias Analysis". Applied Sciences 11, nr 6 (22.03.2021): 2817. http://dx.doi.org/10.3390/app11062817.
Pełny tekst źródłaMir, Jibran, i Azhar Mahmood. "Movie Aspects Identification Model for Aspect Based Sentiment Analysis". Information Technology And Control 49, nr 4 (19.12.2020): 564–82. http://dx.doi.org/10.5755/j01.itc.49.4.25350.
Pełny tekst źródłaZamanzadeh Darban, Zahra, i Mohammad Hadi Valipour. "GHRS: Graph-based hybrid recommendation system with application to movie recommendation". Expert Systems with Applications 200 (sierpień 2022): 116850. http://dx.doi.org/10.1016/j.eswa.2022.116850.
Pełny tekst źródłaKumar, Keerthi, B. S. Harish i H. K. Darshan. "Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method". International Journal of Interactive Multimedia and Artificial Intelligence 5, nr 5 (2019): 109. http://dx.doi.org/10.9781/ijimai.2018.12.005.
Pełny tekst źródłaJain, Kirti, Pinaki Ghosh i 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, nr 5s (7.06.2023): 424–31. http://dx.doi.org/10.17762/ijritcc.v11i5s.7082.
Pełny tekst źródła