Movie Recommendation System Using Genome Tags and Content-Based Filtering

Ali, Syed Mohd; Nayak, Gopal Krishna; Lenka, Rakesh Kumar; Barik, Rabindra K.

doi:10.1007/978-981-10-8360-0_8

Cited by 27 publications

(7 citation statements)

References 13 publications

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“…The similarity between directly or indirectly correlated items is calculated using network analysis. They have proposed a hybrid model where genomic tags of the movie have been used with CBF to recommend movies with similar taste [27]. The proposed model reduces the computational complexity by using principal component analysis (PCA) and Pearson correlation procedures to reduce redundant tags and dispense a low variance [64].…”

Section: A Content-based Recommendationmentioning

confidence: 99%

Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System

et al. 2022

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The movie is one of the integral components of our everyday entertainment.The worldwide movie industry is one of the most growing and significant industries and seizing the attention of people of all ages. It has been observed in the recent study that only a few of the movies achieve success. Uncertainty in the sector has created immense pressure on the film production stakeholder. Moviemakers and researchers continuously feel it necessary to have some expert systems predicting the movie success probability preceding its production with reasonable accuracy.A maximum of the research work has been conducted to predict the movie popularity in the post-production stage. To help the movie maker estimate the upcoming film and make necessary changes, we need to conduct the prediction at the early stage of movie production and provide specific observations about the upcoming movie.This study has proposed a content-based (CB) movie recommendation system (RS) using preliminary movie features like genre, cast, director, keywords, and movie description. Using RS output and movie rating and voting information of similar movies, we created a new feature set and proposed a CNN deep learning (DL) model to build a multiclass movie popularity prediction system. We also proposed a system to predict the popularity of the upcoming movie among different audience groups. We have divided the audience group into four age groups junior, teenage, mid-age and senior. This study has used publicly available Internet Movie Database (IMDb) data and The Movie Database (TMDb) data. We had implemented a multiclass classification model and achieved 96.8% accuracy, which outperforms all the benchmark models. This study highlights the potential of predictive and prescriptive data analytics in information systems to support industry decisions.

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Section: A Content-based Recommendationmentioning

confidence: 99%

Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System

et al. 2022

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“…The logic of content based filtering is intuitive, matching items with users' preferences. It does not suffer from the cold start problem, and has been used widely in various recommendation systems, for example, web pages [9], scientific papers [12], and movies [13].…”

Section: Literature Reviewmentioning

confidence: 99%

An intelligent model for news and publishing industry

Liu

2022

International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022)

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News and publishing industry had encountered dramatically changes in the last two decades. The traditional companies in the industry are facing challenges in the so-called new media era from two aspects. On the one hand, technology companies consistently occupy their territories, on the other hand, the traditional publication lifecycle suffers a long supply chain management and can hardly cope with the fast-changing internet realities. In order to deal with these problems, this paper proposes an intelligent publishing model. The model integrates modern information technologies into the publishing workflow in which artificial intelligence plays an important role. By machine learning technologies, the vast amount of intelligent properties accumulated by publishing houses can be converted to machine understandable knowledge and further to enhance users' experiences. The proposed model refines the publishing process and helps the industry entities facing the process of incoming media convergence.

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“…C-BF merupakan metode untuk memilah konten yang sudah ada dan disesuaikan dengan konteks studi kasus sebelum dilakukan pembobotan terhadap algoritma TF-IDF. Secara garis besar C-BF menggunakan konten atribut yang sudah tersedia dan tidak mengambil opini lain untuk merekomendasikan konten serupa [4], [11]. Pada penelitian ini C-BF menggunakan TF-IDF sebagai algoritma untuk melakukan perhitungan pembobotan nilai terhadap atribut konten dan pengguna.…”

Section: Content-based Filteringunclassified

Sistem Rekomendasi Produk Pena Eksklusif Menggunakan Metode Content-Based Filtering dan TF-IDF

2020

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Sistem rekomendasi saat ini sedang menjadi tren. Kebiasaan masyarakat yang saat ini lebih mengandalkan transaksi secara online dengan berbagai alasan pribadi. Sistem rekomendasi menawarkan cara yang lebih mudah dan cepat sehingga pengguna tidak perlu meluangkan waktu terlalu banyak untuk menemukan barang yang diinginkan. Persaingan antar pelaku bisnis pun berubah sehingga harus mengubah pendekatan agar bisa menjangkau calon pelanggan. Oleh karena itu dibutuhkan sebuah sistem yang dapat menunjang hal tersebut. Maka dalam penelitian ini, penulis membangun sistem rekomendasi produk menggunakan metode Content-Based Filtering dan Term Frequency Inverse Document Frequency (TF-IDF) dari model Information Retrieval (IR). Untuk memperoleh hasil yang efisien dan sesuai dengan kebutuhan solusi dalam meningkatkan Customer Relationship Management (CRM). Sistem rekomendasi dibangun dan diterapkan sebagai solusi agar dapat meningkatkan brand awareness pelanggan dan meminimalisir terjadinya gagal transaksi di karenakan kurang nya informasi yang dapat disampaikan secara langsung atau offline. Data yang digunakan terdiri dari 258 kode produk produk yang yang masing-masing memiliki delapan kategori dan 33 kata kunci pembentuk sesuai dengan product knowledge perusahaan. Hasil perhitungan TF-IDF menunjukkan nilai bobot 13,854 saat menampilkan rekomendasi produk terbaik pertama, dan memiliki keakuratan sebesar 96,5% dalam memberikan rekomendasi pena.

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Movie Recommendation System Using Genome Tags and Content-Based Filtering

Cited by 27 publications

References 13 publications

Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System

Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System

An intelligent model for news and publishing industry

Sistem Rekomendasi Produk Pena Eksklusif Menggunakan Metode Content-Based Filtering dan TF-IDF

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