Movie Recommender System Using Collaborative Filtering

Gupta, Meenu; Thakkar, Aditya; Aashish,; Gupta, Vishal; Rathore, Dhruv Pratap Singh

doi:10.1109/icesc48915.2020.9155879

Cited by 72 publications

(24 citation statements)

References 11 publications

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“…Different algorithms i.e K-NN and collaborative filtering are used in paper [2] in order to improving the accuracy of outcomes over content-based filtering. To avoid the limitations of content-based filtering process the strategy used is based on cosine similarity utilizing k-nearest neighbour and collaborative filtering.…”

Section: II Work In This Areamentioning

confidence: 99%

Movie Recommendation System Using Machine Learning Algorithms

Jadhav¹,

KB²

2022

JMC

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Movies are one kind of enjoyment, however the challenge is locating the specified resources among the lots of movies discharged annually. To search out a show what users are seeking out through this technology is incredibly troublesome. However, recommendation structures return abundant handier in these things. Searching is the first step depending on which the movies are recommended to the user. To search a movie the user may use any of the parameter like name of the actor, director, genres etc. Searching based on one of the parameter and recommending movies based on the parameter is how the current recommendation system works. The movies are recommended with the help of filtering technique i,e collaborative filtering which recommends the movies based on user preference and ratings of the movies. This can be further enhanced by taking multiple parameters (2 in this case) for searching the movies based on which the recommendation can be done. With the help of multiple parameters (2 in this case) the user can be more specific in what he wants to search so that it helps in more accurate recommendations of the movies.

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Section: II Work In This Areamentioning

confidence: 99%

Movie Recommendation System Using Machine Learning Algorithms

Jadhav¹,

KB²

2022

JMC

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“…It presents the novelty of this research. We attempt to explore and compare the performances across a range of models from the basic model [15] and several revised models [8,11,16] as shown in Table 1. The models are revised by selecting evaluation metrics such as the area under the curve and precision at k to compare how each model performs under different components.…”

Section: Experiments and Evaluationsmentioning

confidence: 99%

“…Music recommenders based on user preferences apply deep learning and a hybrid model with content-based and collaborative filtering to discover user patterns [9]. Entertainment areas like movie recommendations use algorithms such as K-nearest neighbour, Cosine similarity, and item-based collaborative filtering to improve performance [10,11].…”

Section: Introductionmentioning

confidence: 99%

Recommender System for Coupon Discount of E-commerce Applications

Wetprasit

Cao

Seow

2022

2022 5th International Conference on Data Science and Information Technology (DSIT)

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Recommender systems have recently been integrated into many fields to introduce numerous appealing products and help with consumer decisions in item selections.Applying recommender systems to coupon discount applications could improve consumers accessibility to coupons of their interest, leading to increased product sales related to coupons. The research aims to create a recommender system model for coupon suggestions based on customer data interactions. The proposed recommender model is a hybrid model that combines matrix factorization and association rule mining. While matrix factorization utilises customer-item interactions with customer transactions history and information as the implicit feedback, the association rule uses the Apriori algorithm to find frequent itemsets from customer discount transactions to create the weight of each item. As a result, the proposed hybrid recommender model provides overall performance higher than the baseline models and other benchmark models. In addition, the proposed model has a precision rank increase of nearly 20% in the same dataset compared to that of the baseline models.

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“…In practice, there are no distinct varieties of artificial intelligence [16]. Instead, a variety of applications are combined to create a more complete form [17].…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Neural Networks and Human-Computer Interaction Technology in Art and Design Area

Xue-ying

Liu

2022

Wireless Communications and Mobile Computing

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Technological development has given a new dimension to the art and design area. This research implements the deep learning model for human-computer integration technology in art and design with wireless sensor networks. In this human-computer interaction, each person and the devices are treated as network nodes. The user will act as a node requesting access privileges for utilizing the calligraphy design to learn the strokes in the self-pace mode for studies. In this research, the human-computer interaction is implemented for recognizing the strokes and curves in the calligraphy images loaded by the user. This identification will aid the user in learning different styles of calligraphy. The proposed fruit-fly optimization algorithm (FFOA) method is analyzed for high classification accuracy, less delay time, and reduced noise parameters against specific existing algorithms.

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Movie Recommender System Using Collaborative Filtering

Cited by 72 publications

References 11 publications

Movie Recommendation System Using Machine Learning Algorithms

Movie Recommendation System Using Machine Learning Algorithms

Recommender System for Coupon Discount of E-commerce Applications

Application of Deep Neural Networks and Human-Computer Interaction Technology in Art and Design Area

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