A review of movie recommendation system: Limitations, Survey and Challenges

Sunilkumar, Chaurasia Neha

doi:10.5565/rev/elcvia.1232

Cited by 52 publications

(5 citation statements)

References 68 publications

(76 reference statements)

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“…A collaborative filtering(CFL) system with the primary function of filtering emails was created by Kastner et al [16]. Group Lens was created by Goyani et al [17] and is mostly used for collaborative filtering in newsgroups. The quick advancement of collaborative filtering technology in personalised suggestions has been substantially accelerated by its success.…”

Section: B Collaborative-based Filteringmentioning

confidence: 99%

RPRsHN: Research Paper Recommendations based on the Heterogeneous Networks using Personalized Weight of meta-paths and Impact Factors

Mahesh

Kumar

et al. 2023

Preprint

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Millions of research papers are available to users from various digital libraries. Finding relevant research that fits a researcher's tastes is therefore a difficult process. As a result, various paper recommendation models have been put out to deal with this problem. The previous studies have concentrated on only the historical preferences of the users. The diversified user preferences as well as interests of the users were not given enough priorities. We recommend a technique for making tailored paper recommendations that includes various preferences and users’ interests. In order to understand user preferences, we look into meta-paths in the network. We use random walks over meta-paths to calculate the recommendation scores of potential papers for the intended users. Then we use Bayesian Personalised Ranking and a personalised weight learning technique to identify a user's personalised weights on various meta-paths. Combining the scores of recommendation from several meta-paths with customized weights yields a global recommendation score. The proposed model performs better than existing state-of-art models.

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Section: B Collaborative-based Filteringmentioning

confidence: 99%

RPRsHN: Research Paper Recommendations based on the Heterogeneous Networks using Personalized Weight of meta-paths and Impact Factors

Mahesh

Kumar

et al. 2023

Preprint

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“…By giving social media users the most appealing and pertinent content, these systems aim to reduce information overhead [6]. Three types of filtering techniques are typically used by RS namely collaborative, content-based hybrid filtering [7]. Things similar to the one in question are recommended using content-based filtering, which examines item features to identify items that are comparable [8].…”

Section: Introductionmentioning

confidence: 99%

Movie-RNET: Best of Best Movie Recommendation via Deep Learning Networks from Multi-Format Data

Aramuthakannan,

Ahilan,

Jayapriya

et al. 2023

Preprint

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Recommendation systems are desperately needed because of the large volume of information generated by the ever-growing use of social networks and the Internet. A recommendation system is essential since exploring the large collection can be time-consuming and difficult. In this research, a novel deep learning-based MovieRNet for best of best movie recommendation system using multi-format data has been proposed. Initially, the multiformat data such as reviews, emoji and trailer are gathered and pre-processed the reviews, emojis using normalization. The trailer videos are converted into frames and pre-processed using contrast stretching adaptive Gaussian Star filter for eliminating the noise artifacts. Pre-processed text is used as an input for BiGRU, which uses reviews to categorise films as offensive, non-offensive, or offensive but non-offensive. Pre-processed images are used as an input for Yolo v7, which uses features extracted from the movie trailer to categorise films as violent and non-violent. Pre-processed text is used as an input for BiGRU, which uses reviews to categorise films as offensive, non-offensive, or offensive but non-offensive. Pre-processed images are used as an input for Yolo v7, which uses features extracted from the movie trailer to categorise films as violent and non-violent. Jelly fish optimization algorithm is used for decision making by analyzing the outputs of the two neural networks to get the optimal prediction rate for time-to-time by updating the user profile with past references of the users. Recall, accuracy, specificity, precision, and F-measure were some of the criteria used to evaluate the proposed technique. The accuracy of the proposed method is improved by 9.6%, 7.8%, 3.5%, and 2.15% better than the existing LSTM-CNN, SRDNet, VRConvMF and SDLM methods respectively.

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“…The most important phase in collaborative filtering is identifying the user’s preferences that are comparable to those of other users. However, it has drawbacks including sparsity, cold start, and scalability [ 4 , 34 ]. As per Kanmani, and others in their paper “ Recency augmented hybrid collaborative movie recommendation system ”, “Amazon and Netflix are the pioneers of the movie recommender systems” [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

A parametric analysis of AVA to optimise Netflix performance

Rastogi

Parihar

Kumar

2023

Int. j. inf. tecnol.

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In this research study, researcher tries to understand how Over-the-top platforms like Netflix categorically utilize aesthetic visual analysis (AVA); an image selection tool to reduce time and increase efficacy, through a parametric analysis of AVA to optimise Netflix performance. This research paper tries to answer all the questions related to the how the database of aesthetic visual analysis (AVA), an image selection tool works better or more like humans. To further substantiate the popularity of Netflix, a real time data of 307 respondents who use OTT platforms in Delhi was collected to determine whether Netflix in fact is or not the market leader. 63.8% of them selected Netflix as their top option.

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A review of movie recommendation system: Limitations, Survey and Challenges

Cited by 52 publications

References 68 publications

RPRsHN: Research Paper Recommendations based on the Heterogeneous Networks using Personalized Weight of meta-paths and Impact Factors

RPRsHN: Research Paper Recommendations based on the Heterogeneous Networks using Personalized Weight of meta-paths and Impact Factors

Movie-RNET: Best of Best Movie Recommendation via Deep Learning Networks from Multi-Format Data

A parametric analysis of AVA to optimise Netflix performance

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